how to write dedication in research report

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Formatting Your Thesis with Microsoft Word

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Dedication, acknowledgements, & preface

Dedication, Acknowledgements, and Preface sections are all optional pages. But, what is the difference between these three?

Dedication — A personal tribute to someone or a group of people. Example: "To my parents."

Acknowledgements — A professional tribute to a person or people who helped you with the thesis. Example: "I wish to thank my thesis advisor for the hours of help in the lab making sure the experiments were set up properly and guiding me through the thesis process."

Preface ( sometimes confused with Foreward or Prologue ) — A very rarely included section that details why you are qualified to write about your topic of your thesis or why you became interested in the topic (for example, an anecdote about a childhood incident that led you to become interested in the topic). Do not confuse this with the literature review; this is more of a personal story.

The video below gives more examples of what can be included in these sections. Note: If your dedication is short (like "To my parents"), you may choose not to include the "Dedication" heading at the top of the page. A demonstration of how to format the dedication page this way is shown below.

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How to Write a Dedication for a Dissertation | Structure Examples

The dedication is a personal and optional part of your dissertation or thesis. In this section, you have the opportunity to express your gratitude to the people who have supported you during your graduate studies. You may choose to dedicate your work to your family, friends, mentors, or other individuals who have helped you throughout your academic career. Keep in mind that the dedication is a personal expression of thanks, so you can be as creative as you like in this section. However, if you do choose to include a dedication, be sure to keep it short and sweet. A few well-chosen words can go a long way in expressing your appreciation.

What is the dedication section in a dissertation?

The dedication section in a dissertation is where you honor and thank the people who have helped you during your project. This might include your advisor, committee members, fellow students, family, and friends. You can also dedicate your work to someone who has inspired you or passed away. The dedication should be short (a few sentences at most) and to the point. It’s a nice way to express your gratitude and show that you understand how important their support was to your success.

How to write a dedication section

The dedication section of a dissertation is not just a simple list of names. It is an opportunity to give thanks and express appreciation to those who have supported you during your journey. Here are the steps in writing an effective thesis dedication:

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1. Choose a person

The first step to writing a dedication section is to choose a person. This can be anyone from your family, friends, or someone you look up to. When choosing a person, it is important to think about why you are dedicating your work to them. Are they your biggest supporter? Do they inspire you? Once you have chosen a person, the next step is to write a short paragraph about why you are dedicating your work to them. Be sure to include how they have impacted your life and why you are grateful for their support.

2. Naming the person

The second step in writing a dedication section is to name the person or persons you are dedicating the work to. This may seem like a simple task, but it is important to take some time to consider who you want to honor with your words. It is also important to be sure that you have the permission of the person or people you are naming before you include them in your dedication. Once you have chosen the perfect person or people to dedicate your work to, the next step is to write a few sentences about why you are making this gesture. In doing so, you will be able to create a beautiful and personal dedication that will be cherished for years to come.

3. Purpose of the dedication

The third step in writing a dedication section is determining why you are dedicating the work to that person. This is important as it will help to set the tone and language. It will also be a key factor in how readers react to your work. Are you honoring someone’s memory? Are you thanking someone for their support? Once you know why you are dedicating the work, you can begin to craft your words carefully. Doing so will ensure that your dedication is effective and meaningful.

4. Addressing the dedication

The fourth step in writing a dedication section is to address the dedication. This can be done in a number of ways, but the most important thing is to be clear and concise. The best way to do this is to use language that is respectful and affectionate. For example, you might say “To my parents, who have always supported me,” or “To my husband, who has been my rock during these difficult times.” Whatever you choose to say, make sure that it comes from the heart. With a little thought and effort, you can easily create a dedication that will be cherished for years to come.

Tips in writing a good dedication section in a dissertation or thesis

When you write a dedication, you want to make sure that it is sincere, personal, and specific. Here are tips to help you write a good dedication:

• First, think about why you are writing the dedication . Who do you want to honor, and why?
• Keep it short and sweet . A dedication does not need to be long or flowery.
• Be specific . Do not just say “to my parents.” Instead, try something like “to my parents, who have always encouraged me to pursue my dreams.”
• Make it personal . You can share a story or anecdote about the person you are honoring.
• Avoid clichéd phrases . Instead of saying “thank you for everything,” try to be more original.

In conclusion, a dedication is a great way to show your appreciation for someone who has made a difference in your life. By following the tips above, you can easily write a dedication that is both personal and meaningful.

Author: tutlance

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How to Write a Dedication

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And the dedication goes to…

When you have finally completed the gruelling yet wonderful process of writing a book, short story , dissertation, etc., you are then faced with the difficult decision of dedicating this source of all your hard work to someone special. Here are some helpful tips to ease your anxiety and assist you in writing a dedication page.

Picking a person 

The most difficult part of writing this piece of front matter is choosing who you would like to dedicate your work to. Some writers may find it to be the most difficult part of the whole process. When choosing who to write your dedication for, think about the process you just went through and who helped you get through it. This could include a variety of people, including a parent, sibling, or other family member, a spouse or partner, a friend, a supervisor, a colleague, or even a pet. This is a very personal choice and there is no wrong decision.

Naming names

After you have decided who you will write your dedication for, you must decide how you are going to identify them. This will be based on your own personal preference and what is appropriate, according to your relationship with that person. The identification could vary from formal to informal.

On the formal end of the spectrum, your dedication could be addressed to Dr. So and So, Mr. X, or even Mother and Father. In between formal and informal, there are options like Mom, Dad, My sister, My friend, a person’s first and last name (no title), etc. On the informal side, you could use the first name or nickname of someone you know.

Reason for the dedication

The next component in writing your dedication is explaining why you chose this person. Many authors provide a reason for their dedication selections. As with the whole dedication process, this is an extremely personal and subjective decision. The dedication could simply be: "For my mom"; others may choose to explain their decision: "For my mom; without her I would not be here." You may want to write a funny anecdote about the person, an experience you shared, or even a private joke shared only by the two of you. As seen in our example dedication page, there are many types of dedications, each with it's own style. Your reason is completely dependent on your personality and your relationship with the person to whom you are dedicating your work.

Addressing the dedication

There are many ways you can address your dedication. You could write, "I dedicate this book to …", "This is dedicated to …", "To: …", "For: …", or simply just start writing your dedication without any formal address. It should be on its own page so everyone will get the hint that it is a dedication page, even if there isn't any formal address. Take into consideration the person you have chosen to dedicate your work to, your personality, and the formality of your relationship and the address will follow suit.

Alternative dedications

It has been extremely popular over the years to write a dedication page using alternative formats. Authors have used poems or funny anecdotes to express their gratitude. In the past, many dedications were often written in the style of a formal letter.

The most important things to remember when writing a dedication are to keep it simple, concise, and ensure that it truly reflects your personality and your relationship with the person the dedication is for. Remember to get your finished dedication edited by one of our book editors . You don't want to overlook calling your spouse the pettiest person in the world when you really meant the prettiest person in the world!

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Examples of Dedications

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Writing Your Dedication and Acknowledgements

When it comes to writing a thesis, the chapters get most of our attention – and so they should! They are the bulk of the thesis, and they contain the actual research. But there are other sections to think about as well, and two of them can be particularly fun to write.

The dedication and acknowledgements pages are where you can show your love and gratitude to the people who have helped you to achieve your goals. They tend to be less prescribed than other sections, so you have a little freedom to get creative in the way you express yourself!

While they might seem similar, there is a difference between the dedications and the acknowledgements. The dedication, as the name suggests, allows you to dedicate your thesis to someone (or multiple people). This is quite a personal matter, and dedications are often made to family members, spouses, friends, or community groups.

The acknowledgements page, by contrast, allows you to express thanks for specific contributions, and often features people that the author knows professionally as well as personally. If a person or group helped you by giving advice, funding, mentorship, editorial assistance, or practical support, it’s a good idea to give them a shout-out in the acknowledgements. This is more than just a courtesy – it’s an important part of maintaining your relationships by formally recognising the people and organisations who have made a contribution to your work.

As an example – I dedicated my PhD thesis to several members of my family, and wrote short personal notes about their contributions. In my acknowledgements section, I thanked my supervisor, a mentor, a friend who provided technical advice during my research, and my scholarship provider.

My dedication and acknowledgements sections were fairly conventional, but that doesn’t mean yours have to be! I’ve heard of writers dedicating their thesis to their pets, their computers, and even their local barista. There have even been rumours of one acknowledgements page which included a coupon for a free beer for anyone whose name was listed!

Other scholars have been known to thank:

• Public transport providers “for making the task of writing in buses and trains such an enjoyable exercise” ( source )
• The “spell-check function of Microsoft Word, without whom ‘institution’ would be misspelled differently 197 times” ( source )
• “The U.S. Immigration Service under the Bush administration, whose visa background security check forced [the author] to spend two months (following an international conference) in a third country, free of routine obligations” ( source )

One enterprising paleontologist even proposed to his girlfriend via the acknowledgements section of his journal article. (She said yes, apparently.)

So if you haven’t already, have a go at drafting your dedication and acknowledgements pages. After all, it’s not often you can gush about your favourite people while still technically being productive!

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Thesis / dissertation formatting manual (2022).

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Dedication Page

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The Dedication Page is optional. If you choose to include a Dedication Page, please ensure that:

• You are using the same font as in the rest of your manuscript.
• No images are included.
• Page number ii appears centered at the bottom of the page.

​Please note that the Dedication Page is different from the Acknowledgements Page.

Dedication Page Example

Here is an example of a dedication page from the template:

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• Research paper

How to Write a Research Paper | A Beginner's Guide

A research paper is a piece of academic writing that provides analysis, interpretation, and argument based on in-depth independent research.

Research papers are similar to academic essays , but they are usually longer and more detailed assignments, designed to assess not only your writing skills but also your skills in scholarly research. Writing a research paper requires you to demonstrate a strong knowledge of your topic, engage with a variety of sources, and make an original contribution to the debate.

This step-by-step guide takes you through the entire writing process, from understanding your assignment to proofreading your final draft.

Table of contents

Understand the assignment, choose a research paper topic, conduct preliminary research, develop a thesis statement, create a research paper outline, write a first draft of the research paper, write the introduction, write a compelling body of text, write the conclusion, the second draft, the revision process, research paper checklist, free lecture slides.

Completing a research paper successfully means accomplishing the specific tasks set out for you. Before you start, make sure you thoroughly understanding the assignment task sheet:

• Read it carefully, looking for anything confusing you might need to clarify with your professor.
• Identify the assignment goal, deadline, length specifications, formatting, and submission method.
• Make a bulleted list of the key points, then go back and cross completed items off as you’re writing.

Carefully consider your timeframe and word limit: be realistic, and plan enough time to research, write, and edit.

Prevent plagiarism. Run a free check.

There are many ways to generate an idea for a research paper, from brainstorming with pen and paper to talking it through with a fellow student or professor.

You can try free writing, which involves taking a broad topic and writing continuously for two or three minutes to identify absolutely anything relevant that could be interesting.

You can also gain inspiration from other research. The discussion or recommendations sections of research papers often include ideas for other specific topics that require further examination.

Once you have a broad subject area, narrow it down to choose a topic that interests you, m eets the criteria of your assignment, and i s possible to research. Aim for ideas that are both original and specific:

• A paper following the chronology of World War II would not be original or specific enough.
• A paper on the experience of Danish citizens living close to the German border during World War II would be specific and could be original enough.

Note any discussions that seem important to the topic, and try to find an issue that you can focus your paper around. Use a variety of sources , including journals, books, and reliable websites, to ensure you do not miss anything glaring.

Do not only verify the ideas you have in mind, but look for sources that contradict your point of view.

• Is there anything people seem to overlook in the sources you research?
• Are there any heated debates you can address?
• Do you have a unique take on your topic?
• Have there been some recent developments that build on the extant research?

In this stage, you might find it helpful to formulate some research questions to help guide you. To write research questions, try to finish the following sentence: “I want to know how/what/why…”

A thesis statement is a statement of your central argument — it establishes the purpose and position of your paper. If you started with a research question, the thesis statement should answer it. It should also show what evidence and reasoning you’ll use to support that answer.

The thesis statement should be concise, contentious, and coherent. That means it should briefly summarize your argument in a sentence or two, make a claim that requires further evidence or analysis, and make a coherent point that relates to every part of the paper.

You will probably revise and refine the thesis statement as you do more research, but it can serve as a guide throughout the writing process. Every paragraph should aim to support and develop this central claim.

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A research paper outline is essentially a list of the key topics, arguments, and evidence you want to include, divided into sections with headings so that you know roughly what the paper will look like before you start writing.

A structure outline can help make the writing process much more efficient, so it’s worth dedicating some time to create one.

Your first draft won’t be perfect — you can polish later on. Your priorities at this stage are as follows:

• Maintaining forward momentum — write now, perfect later.
• Paying attention to clear organization and logical ordering of paragraphs and sentences, which will help when you come to the second draft.
• Expressing your ideas as clearly as possible, so you know what you were trying to say when you come back to the text.

You do not need to start by writing the introduction. Begin where it feels most natural for you — some prefer to finish the most difficult sections first, while others choose to start with the easiest part. If you created an outline, use it as a map while you work.

Do not delete large sections of text. If you begin to dislike something you have written or find it doesn’t quite fit, move it to a different document, but don’t lose it completely — you never know if it might come in useful later.

Paragraph structure

Paragraphs are the basic building blocks of research papers. Each one should focus on a single claim or idea that helps to establish the overall argument or purpose of the paper.

Example paragraph

George Orwell’s 1946 essay “Politics and the English Language” has had an enduring impact on thought about the relationship between politics and language. This impact is particularly obvious in light of the various critical review articles that have recently referenced the essay. For example, consider Mark Falcoff’s 2009 article in The National Review Online, “The Perversion of Language; or, Orwell Revisited,” in which he analyzes several common words (“activist,” “civil-rights leader,” “diversity,” and more). Falcoff’s close analysis of the ambiguity built into political language intentionally mirrors Orwell’s own point-by-point analysis of the political language of his day. Even 63 years after its publication, Orwell’s essay is emulated by contemporary thinkers.

Citing sources

It’s also important to keep track of citations at this stage to avoid accidental plagiarism . Each time you use a source, make sure to take note of where the information came from.

You can use our free citation generators to automatically create citations and save your reference list as you go.

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The research paper introduction should address three questions: What, why, and how? After finishing the introduction, the reader should know what the paper is about, why it is worth reading, and how you’ll build your arguments.

What? Be specific about the topic of the paper, introduce the background, and define key terms or concepts.

Why? This is the most important, but also the most difficult, part of the introduction. Try to provide brief answers to the following questions: What new material or insight are you offering? What important issues does your essay help define or answer?

How? To let the reader know what to expect from the rest of the paper, the introduction should include a “map” of what will be discussed, briefly presenting the key elements of the paper in chronological order.

The major struggle faced by most writers is how to organize the information presented in the paper, which is one reason an outline is so useful. However, remember that the outline is only a guide and, when writing, you can be flexible with the order in which the information and arguments are presented.

One way to stay on track is to use your thesis statement and topic sentences . Check:

• topic sentences against the thesis statement;
• topic sentences against each other, for similarities and logical ordering;
• and each sentence against the topic sentence of that paragraph.

Be aware of paragraphs that seem to cover the same things. If two paragraphs discuss something similar, they must approach that topic in different ways. Aim to create smooth transitions between sentences, paragraphs, and sections.

The research paper conclusion is designed to help your reader out of the paper’s argument, giving them a sense of finality.

Trace the course of the paper, emphasizing how it all comes together to prove your thesis statement. Give the paper a sense of finality by making sure the reader understands how you’ve settled the issues raised in the introduction.

You might also discuss the more general consequences of the argument, outline what the paper offers to future students of the topic, and suggest any questions the paper’s argument raises but cannot or does not try to answer.

You should not :

• Offer new arguments or essential information
• Take up any more space than necessary
• Begin with stock phrases that signal you are ending the paper (e.g. “In conclusion”)

There are four main considerations when it comes to the second draft.

• Check how your vision of the paper lines up with the first draft and, more importantly, that your paper still answers the assignment.
• Identify any assumptions that might require (more substantial) justification, keeping your reader’s perspective foremost in mind. Remove these points if you cannot substantiate them further.
• Be open to rearranging your ideas. Check whether any sections feel out of place and whether your ideas could be better organized.
• If you find that old ideas do not fit as well as you anticipated, you should cut them out or condense them. You might also find that new and well-suited ideas occurred to you during the writing of the first draft — now is the time to make them part of the paper.

The goal during the revision and proofreading process is to ensure you have completed all the necessary tasks and that the paper is as well-articulated as possible.

Global concerns

• Confirm that your paper completes every task specified in your assignment sheet.
• Check for logical organization and flow of paragraphs.
• Check paragraphs against the introduction and thesis statement.

Fine-grained details

Check the content of each paragraph, making sure that:

• each sentence helps support the topic sentence.
• no unnecessary or irrelevant information is present.
• all technical terms your audience might not know are identified.

Next, think about sentence structure , grammatical errors, and formatting . Check that you have correctly used transition words and phrases to show the connections between your ideas. Look for typos, cut unnecessary words, and check for consistency in aspects such as heading formatting and spellings .

Finally, you need to make sure your paper is correctly formatted according to the rules of the citation style you are using. For example, you might need to include an MLA heading  or create an APA title page .

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Checklist: Research paper

I have followed all instructions in the assignment sheet.

My introduction presents my topic in an engaging way and provides necessary background information.

My introduction presents a clear, focused research problem and/or thesis statement .

My paper is logically organized using paragraphs and (if relevant) section headings .

Each paragraph is clearly focused on one central idea, expressed in a clear topic sentence .

Each paragraph is relevant to my research problem or thesis statement.

I have used appropriate transitions  to clarify the connections between sections, paragraphs, and sentences.

My conclusion provides a concise answer to the research question or emphasizes how the thesis has been supported.

My conclusion shows how my research has contributed to knowledge or understanding of my topic.

My conclusion does not present any new points or information essential to my argument.

I have provided an in-text citation every time I refer to ideas or information from a source.

I have included a reference list at the end of my paper, consistently formatted according to a specific citation style .

I have thoroughly revised my paper and addressed any feedback from my professor or supervisor.

I have followed all formatting guidelines (page numbers, headers, spacing, etc.).

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How to write the Acknowledgements section of a research paper

References & Acknowledgements

Yateendra Joshi

Among all the sections of a typical research paper, the acknowledgements section is the easiest to write—which is probably why most books on writing research papers tend to ignore it. Yet, acknowledgements can be politically tricky. By forgetting to acknowledge those whom you should have acknowledged, you risk offending them; but even those whom you have acknowledged in your paper can take offence at the manner in which this is done. At times, when the help received is substantial, it can be hard to decide whether you should acknowledge the support or offer authorship instead. Wish this were as simple as remembering that it is Acknowledgments without the ‘e’ in US English but Acknowledgements with the ‘e’ in UK English.

This article discusses the purpose of the acknowledgements section in a research paper and offers tips on who should be mentioned in it and how, who should be excluded, and how the section should be formatted.

Why acknowledge?

In academic writing, the time-honoured method of acknowledging people is to cite their work, but that does not apply here. And yet, it is only proper that you put on record – by means of an appropriate mention in the acknowledgements section – any help that you received in conducting your research, in writing about it and publishing it. By doing so, albeit indirectly, you make your work more credible: for instance, when you acknowledge the help you received from a statistician in designing your experiment and in analysing its results, you reassure journal editors and, more important, those who review your manuscript, about the experimental design and the analysis of results. Similarly, acknowledging the help you received from a copy editor shows that you have taken care of the language, style, and formatting.

Who should be acknowledged ?

Broadly, you should acknowledge those who helped you by going beyond their normal call of duty, especially those whose help proved crucial to your work or who provided expertise that you lacked. Such people may include some of your peers, your mentors (research supervisors or guides), and even your students. If you received funding, the fact should be acknowledged. Some funding agencies may have specific instructions about how their funding should be mentioned; if that is so, make sure that the form of acknowledgement is consistent with such instructions. You should also consider acknowledging any material or other resources made available to you free of charge. However, if such help is mentioned as part of a conflict-of-interest statement, it should not be repeated in the acknowledgements section.

Consider including reviewers, even if they are anonymous, if their suggestions have resulted in a substantially improved manuscript.

It is also advisable to have your phrasing approved by those mentioned in the acknowledgements, because such a mention may imply that they approve of the contents of the research paper.

Who should not be acknowledged ?

Unlike dedications and acknowledgement commonly found in books, acknowledgements in research papers do not feature parents, family members, or friends (unless of course they qualify on other grounds). Similarly, those who provide a service as part of their job (laboratory technicians, field assistants, and so on) are usually excluded. Heads of departments, directors of laboratories, and people in similar positions also should not be acknowledged routinely: include them only if they went out of their way to help you.

Phrasing the acknowledgements

In general, be factual and avoid going overboard. Something along the lines of “The authors thank John Smith for advice on experimental design and statistical analysis” should be fine. Courtesy titles (Mr, Ms, Dr, etc.) before the names are rarely used (but check your target journal), and job titles or designations are seldom given. Avoid such expressions as ‘kind help’, ‘eternally grateful’, and ‘greatly indebted to’. If the acknowledgement is specifically by one of the authors of the paper, it is customary to use only the initials, as in “JS thanks...”.

In terms of sequence, any intellectual contributions come first, followed by technical support, help in revising and writing; financial support is mentioned at the end.

Formatting the acknowledgements

As a rule of thumb, the acknowledgement section should be a single short paragraph of say half a dozen lines. Examine the target journal for the format: whether the heading appears on a separate line or run on (that is, the text follows the heading on the same line). Check also whether the heading is in bold or in italics. The headings in the main body of the paper may be numbered, but the acknowledgement section is not numbered. Do not use any special formatting within the paragraph.

Effectively, acknowledgements signal the end of the main body of a research paper. Of course, they are followed by references—but that is another story.

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How to Write a Dedication for a Thesis or Dissertation?

Writing a dedication page for your thesis or dissertation is a great way to honor the people who have supported you throughout the journey of your research and writing. In this guide, we’ll show you everything you need to know about writing a dedication page for your thesis or dissertation. From formatting it to what you can include, we’ll run through all of the details to help you write your dedication page with confidence and gratitude.

What is a dedication page?

In academic writing (as well as book writing), the dedication page is where you can honor the people who have inspired or emotionally support you throughout your research and writing in a personal manner.

The dedication page is an optional section in a thesis or dissertation when it comes to academic writing.

Why should I include a dedication page in my writing?

The dedication page is not mandatory in most academic writing.

However, by paying tributes to the individuals or even the higher power who meant the most to you, you attach meaning to your work beyond the academic level.

A song is merely a song with lyrics, and that’s that. But if the same song is dedicated to someone, it will certainly entail special meanings to those who are dedicated and the dedicator (yourself). In other words, dedication serves to connect your work with the people who mean the most to you.

The same goes for your work. Do you agree?

Where does the dedication page appear in a paper?

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The dedication page should appear before the main body of a thesis or dissertation. But every institution has its own requirements. You should always check the formatting guidelines provided by your school, faculty or department.

For this matter, we took a quick tour of the formatting guidelines for the top three universities in the US. And we’ve already found 3 variations.

How long is a dedication page?

A dedication page can be as short as one sentence, if not in a few short paragraphs.

Who should I include on the dedication page?

In academic writings, the dedication page is where you can show your gratitude to the individuals (and even the higher power) who have inspired you or emotionally support you on a personal level throughout your work. 

They may or may not involve in your research work. You may include:

• God or the higher power

What is the formatting of a dedication page?

Always check the formatting guidelines provided by your school, faculty, or department.

As a general rule, the title, as well as the text, should be aligned in the center of the page like this:

What is the tone and style of a dedication page?

The tone of the dedication page can be formal or informal. It can be personal, sometimes even emotional and spiritual.

Formal phases to begin a dedication:

*Work: thesis/dissertation/report/research

This [work] is dedicated to…

Example: This project is dedicated to Mr. Smith, our teacher who helped and guided us to successfully complete this work.

I dedicate this [work] to…

Example: I dedicate this thesis to my father and my mother, who with love and effort have accompanied me in this process, without hesitating at any moment of seeing my dreams come true, which are also their dreams

I am dedicating this [work] to…

Example: I am dedicating this thesis to my late grandfather who taught me all about perseverance. His memories continue to keep me going in every single day of my life

Informal phases to begin a dedication:

Example: For my Almighty God, the reason for my existence.

Example: To Bruno, who has been my support in the difficulties.

Example: To my dearest wife Jenny, to my lovely little girl Jin, to my parents, to my brothers William, John and Paul, and to all those who made this thesis possible.

The dedication page vs the acknowledgement page, what’s the difference?

While both the dedication page and acknowledgement page let you show appreciation for the help and support in your research and writing, there are some major similarities and differences between the two.

Dedication in academic writing

On a dedication page, you honor a particular group of people or an individual for inspiring or motivating you for completing the project or paper. It can be personal, emotional, or even spiritual and does not necessarily have anything to do with the academic aspects.

You dedicate your research work to the people who mean the most to you, such as the higher power, your core family members, a particular individual, friends, or someone who has a special role in your life.

Acknowledgment in academic writing

In acknowledgments, you recognize resources (e.g. grants or funding), institutions as well as individuals that are involved or have support in the course of your research and writing. These parties directly play a role in your academic career. Here, you disclose as much academic-related information as possible.

The Similarities

These sections, usually optional, should be no longer than one page.

Depending on the requirements of school or academic department, they can appear before or after the table of contents in your paper.

The Differences

The key difference between acknowledgement and dedication is that the former is more formal and the latter is more personal.

Acknowledgement usually recognizes the contributions of those who were directly involved in the research, whereas dedications are a way for the writer to pay tribute to individuals who have had a significant personal or emotional impact on their life or work.

It is common for people to dedicate their writing to God or another higher power who they believe provided them with spiritual support during the writing process.”

Here’s a brief comparison table showing the main differences between the two:

If you want to check out examples of dedication for projects, reports, theses, dissertations, and books, also read: Examples of Well-Written Dedication Section

Acknowledgement Examples for School/College Projects

Most popular Acknowledgement For School/College Projects [7 Examples] Acknowledgement for English Project [5 Examples] Acknowledgement for Project Class 11 and 12 Acknowledgement for Project of Class 8, 9 and 10 By subjects Acknowledgement for Accounting Project [3 Examples] Acknowledgement for Business Studies Project [5 Examples] Acknowledgement for Chemistry Project [5 Examples] Acknowledgement for Computer Project [5 Examples] Acknowledgement for Economics Project [5 Examples] Acknowledgement for English Project [5 Examples] Acknowledgement for Geography Project [5 Examples] Acknowledgement for History Project [5 Examples] Acknowledgement for Maths Project for Students [5 Examples] Acknowledgement for Physics Project [5 Examples] Acknowledgement for Social Science Project [5 Examples] Others Acknowledgement for Group Project [5 Examples] Acknowledgement for Graduation Project [5 Examples] Acknowledgement for Disaster Management Project [3 Examples] Acknowledgement for Yoga Project [3 Samples]

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For work or business Acknowledgement Receipt of Payment [4 Examples] Acknowledging Receipt of Documents: A Quick Guide with Examples Acknowledgement for Presentation [9 Examples] Acknowledgement for Job Offer [3 Examples] Acknowledgement for Business Plan [4 Examples] Acknowledgement for Work Immersion [5 Examples] Acknowledgement of Receipt of Appraisal [3 Examples] Acknowledegment of Debt [5 Examples] Resignation Acknowledgement for Employers [5 Examples]

Academic Acknowledgement for Research Paper [5 Examples] Acknowledgement for Internship Report [5 Examples] Acknowledgement for Thesis and Dissertation [15 Examples] Acknowledgement for Portfolio [5 Examples] Acknowledgement for Case Study [4 Examples] Acknowledgement for Academic Research Paper [5 Examples] Acknowledgement for College/School Assignment [5 Examples] Acknowledgemet to God in Reports [5 Examples]

Others Acknowledgement to Funeral Attendees [5 Examples] Funeral Acknowledgement Templates (for Newspapers and Websites) Common Website Disclaimers to Protect Your Online Business Notary Acknowledgement [5 Examples]

How-to Guides on Academic Writing and Others

Most popular How to Write an Acknowledgement: The Complete Guide for Students How to Write an Acknowledgement for College Project? How to Write a Dedication Page for a Thesis or Dissertation? More on acknowledgements How to Write Acknowledgment for a Dissertation or a Thesis? Is Acknowledgement and Dedication the Same? Thesis or Dissertation How to Write a Master’s Thesis: The Ultimate Guide How to Write a Thesis Proposal? How to Write an Abstract for a Thesis? How to Write a Preface for a Thesis? Others How to Write an Introduction for a Research Paper? 7 Real Research Paper Examples to Get You Started How to Write Cover Letter for an Internship Program? How to Write an Internship Acceptance Letter? How to Write a Leave Application? For Schools and the Workplace How to Write a Resignation Letter?

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8+ Examples of Well Written Dedication Section for Your Report

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A dedication is usually one statement or a short paragraph at the beginning of a book, thesis, or dissertation that gives the work to someone who inspired or helped you with it. It’s a short sentence but it can mean a lot to you.

What is dedication in a report?

Dedication is the author’s way of showing appreciation and respect for people who have been influential in their life. Since the dedication is personal, the people who are named usually have nothing to do with the dissertation topic. However, some authors give a book to a cause or in memory of a loved one.

What’s The Difference Between Dedication and Acknowledgement for reports? 

There are some similarities between a book’s dedication page and the list of people thanked by the author on the Acknowledgements page, but there are also some crucial differences.

Dedications are usually short and only talk about one person. They also don’t have to thank anyone who helped make the book.

The people or groups who contributed to making the book and anyone else the writer wants to thank for their help or support are usually listed on the acknowledgment page.

Examples of Dedication Page

Below are some samples to help you professionally write your dedication section.

Dedication Page Sample 1

I want to thank my family and many friends for helping me with my dissertation. I’m incredibly thankful to my loving parents, John and Louise Johnson, for their words of support and push to be consistent. 

My sisters, Kate, Jamie, and Rhonda have never left my side. They are significant to me. I also want to thank my friends and church family who have been there for me during this process. I will always be grateful for everything they have done, especially Shelly Myers for helping me improve my technology skills, LaTanja Riley-Hedgepeth for the many hours she spent proofreading, and Ramona Taylor for teaching me how to use the leader dots. 

I want to thank my best friend, L. Jennings, and my wonderful daughter, Cime, for being there for me throughout the doctorate program and for being the reason I could do this work. You’ve both been the best people to cheer me on.

Dedication Page Sample 2

I’m dedicating this project to God Almighty, who made me and gave me strength, ideas, wisdom, knowledge, and understanding. He has strengthened me throughout this program, and I have only flown on His wings. 

I also dedicate this work to my loving husband, Mike Arasomwan, who has always been there for me and made sure I did everything I could to finish what I started. To my children Charisah (Maama), Joel, and Sophie (Babe), who have been impacted in every way by this journey. Many thanks. My love for you all is impossible to measure. God bless you

Dedication Page Sample 3

This thesis is dedicated to four important people in my life who have meant a lot to me and still do. Even though they are no longer alive, their memories still control how I live. First of all, I want to thank my grandfather, Amadou Seydou, who loved me so much and taught me the value of working hard. Adam, thank you so much; I will never forget you. Next, my mother’s mother, Hamady, who raised me, adored me and taught me to speak Pulaar, my native language. I can’t send her to Mecca as promised, though, because I need more money.

I also want to remember my uncle Massamba Amadou, who was killed by a drunk driver when he was only 24 years old. May you be happy in Paradise. Last but not least, I want to dedicate this to my late little brother Moustapha Seydou, who has left a hole in our lives that will never be filled. Even though you only lived a short time, I will keep your memory alive as long as I do. I love and miss you all more than I can say. May Allah (SWT) grant you Jannah Firdaws.

Dedication Page Sample 4

This thesis is dedicated to my husband, Bruce, who has supported and encouraged me through all the challenges of graduate school and life. I appreciate having you in my life. This work is also for my parents, John and Karen Aaron, who have always loved me no matter what and who have shown me how to work hard for the things I want to achieve.

Dedication Page Sample 5

I want to thank my family and many friends for helping me with my report . First, I’m incredibly thankful for my loving parents. William and Helen Johnson said encouraging things and pushed me to be persistent. In addition, my sisters, Sarah,  Lindsy, and Rhonda, have never left my side.

Dedication Page Sample 6

This study is dedicated from the bottom of our hearts to our wonderful parents, who have given us so much,  who gave us hope and strength when we wanted to give up, and who continue to be a source of moral, emotional, and financial support.

To our brothers, sisters, cousins, uncles, aunts, mentors, friends, and classmates who gave me their valuable advice. And finally, we dedicate this report to the All-Powerful God and say, “Thank you for guiding us.”

Dedication Page Sample 7

This dissertation is for the ninth-grade teacher who interested me in economics.

For my father, who helped me with everything big and small, and especially for my loving husband, who pushed me to follow my dreams and finish my thesis. To my children Jenny (Maama), Joel, and Sophia (Babe), whose lives this search has changed in every way.

Dedication Page Sample 8

This dissertation is written to honor Jane Smith’s memory. She was the reason I decided to get my doctorate, but she couldn’t be there for my graduation. She will get this.

Thank you to my academic supervisor and the committee for helping me through this process.

Dedication Page Sample 9

I want to dedicate my report to the loving memory of my friend Maya. She was a doctoral student at Columbia with me. She encouraged me to do this study, and she and her husband, Eugene McDowell, helped me with my research over several years by letting me stay in their Washington home when I needed to work in the District. 

Maya’s courage and faith in the last year of her life taught me a lot about friendship and its importance. She lived a good life and was true to her spiritual beliefs by helping both friends and strangers who were in need. She was brave when her death came too soon. Even though she was dying, she was able to finish her dissertation. When I wanted to give up, her example kept me going.

Whether you’re giving a book or thesis to a friend or family member, our examples can help you write a dedication that you and the person you’re giving it to will be proud of.

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Sample dedication in thesis writing

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Covering Dedication Samples

Exactly what is a dedication page inside a thesis or research paper.

Here, you includes dedication quotes. words for dedication. sample dedication along with other dedication examples that will help you in turning your dedication an excellent blast.

Some Useful Ideas to Remember Before Writing the Dedication Page of Thesis

To begin with, remember writing a dedication page for any thesis, dissertation or perhaps a isn’t a difficult deal. Though, it’s true that some concentration and concentrate can better show you on paper an excellent dedication page for the final project. Essentially, dedication page shows your devotion and feeling of appreciation for individuals who’ve aided you within the quest for your academic goal. It may be referred to as a supply of acknowledgment for individuals who’ve offered their help whole heartedly within the fulfillment of the studies. However, it’s also interesting to notice that dedication page is completely optional and as much as your readiness to include or take away. Sometimes, it takes place that you will get quite nostalgic when start discovering people with you who’ve supported you to attach using the finish type of any project. Anyways, you don’t need to bother with such situation as possible streamline all of the names and add them within the dedication segment.

Useful Help guide to Write a Dedication Page in Research

Here are a few details to keep in mind prior to going through dedication writing.

• To begin with, if you are planning to include dedication page inside your school assignment, thesis or dissertation it ought to always be added soon after endorsement.
• The dedication page won’t be numbered in addition to it won’t be counted as a whole page count.
• Formatting of dedication page can also be unrestricted. It is simple to opt for any style or format. Anyways, for much better output, it’s also suggested have a tendency to stick to the advice of the supervisor or project coordinator concerning the style and formatting of dedication page. Read much more about at length such as the. and .
• The dedication page ought to be briefest and cannot exceed in one page.
• Before beginning dedication page, you need to develop a summary of institutions in addition to people you want to pay for tribute to. Individuals that you’re selecting may be your folks, brothers and sisters or peers who’ve helped you by any means on your career. The support they provide you may be moral in addition to financial. Dedication is compensated just to provide a grateful note towards their sincere support. Institutions are put in the dedication which have supported inside your research or funded you having a scholarship for that effective fulfillment of the thesis or dissertation.

Read much more about the .

Steps to create a Dedication Page for the Thesis or Research Paper

Whenever you then it’s time to create a right decision in selecting an individual whom you want is the a part of your book. It’s to say that you might go through anxiety while selecting some from a lot of people whom you will dedicate your projects. However, simply make right choice after getting careful look.

Remember, your dedication might be formal to informal. You may also opt for beautiful dedication quotes or poems with this page of the thesis. On formal note, you are able to dedicate your projects for your professor, supervisor, parents or any institution that supported you within the fulfillment of the academic goal. However, informal options may be your brothers and sisters, peers or perhaps a favorite pet. However, regardless of the choice you are making, make sure to enclose the main reason of the persistence for that exact person or institution.

Below are great tips as the best way to address a dedication in addition to sample dedications for the assistance.

Tips about how to Address a Dedication

There are plenty of ways that it is simple to address your dedication. For instance

• “This is devoted to …”
• “I want to dedicate my work to…”
• “I dedicate this book to…”
• “In persistence for my…”
• “It is our genuine gratefulness and warmest regard that people dedicate the work to…”

It is extremely interesting to notice that funny anecdotes and poems can also be negligence your dedication. However, it is best option to maintain your dedication as concise and straightforward as possible. Read some .

For example from famous

“To my spouse Marganit and the kids Ella Rose and Daniel Adam without whom this book could have been completed 2 yrs earlier.”

“For Beatrice-I valued, you passed on.The world’s been night-marished.”

“To caffeine and sugar, my buddies through many a lengthy nights writing.”

“To Mother, Who required me towards the library.”

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Home » Research Report – Example, Writing Guide and Types

Research Report – Example, Writing Guide and Types

Table of Contents

Research Report

Definition:

Research Report is a written document that presents the results of a research project or study, including the research question, methodology, results, and conclusions, in a clear and objective manner.

The purpose of a research report is to communicate the findings of the research to the intended audience, which could be other researchers, stakeholders, or the general public.

Components of Research Report

Components of Research Report are as follows:

Introduction

The introduction sets the stage for the research report and provides a brief overview of the research question or problem being investigated. It should include a clear statement of the purpose of the study and its significance or relevance to the field of research. It may also provide background information or a literature review to help contextualize the research.

Literature Review

The literature review provides a critical analysis and synthesis of the existing research and scholarship relevant to the research question or problem. It should identify the gaps, inconsistencies, and contradictions in the literature and show how the current study addresses these issues. The literature review also establishes the theoretical framework or conceptual model that guides the research.

Methodology

The methodology section describes the research design, methods, and procedures used to collect and analyze data. It should include information on the sample or participants, data collection instruments, data collection procedures, and data analysis techniques. The methodology should be clear and detailed enough to allow other researchers to replicate the study.

The results section presents the findings of the study in a clear and objective manner. It should provide a detailed description of the data and statistics used to answer the research question or test the hypothesis. Tables, graphs, and figures may be included to help visualize the data and illustrate the key findings.

The discussion section interprets the results of the study and explains their significance or relevance to the research question or problem. It should also compare the current findings with those of previous studies and identify the implications for future research or practice. The discussion should be based on the results presented in the previous section and should avoid speculation or unfounded conclusions.

The conclusion summarizes the key findings of the study and restates the main argument or thesis presented in the introduction. It should also provide a brief overview of the contributions of the study to the field of research and the implications for practice or policy.

The references section lists all the sources cited in the research report, following a specific citation style, such as APA or MLA.

The appendices section includes any additional material, such as data tables, figures, or instruments used in the study, that could not be included in the main text due to space limitations.

Types of Research Report

Types of Research Report are as follows:

Thesis is a type of research report. A thesis is a long-form research document that presents the findings and conclusions of an original research study conducted by a student as part of a graduate or postgraduate program. It is typically written by a student pursuing a higher degree, such as a Master’s or Doctoral degree, although it can also be written by researchers or scholars in other fields.

Research Paper

Research paper is a type of research report. A research paper is a document that presents the results of a research study or investigation. Research papers can be written in a variety of fields, including science, social science, humanities, and business. They typically follow a standard format that includes an introduction, literature review, methodology, results, discussion, and conclusion sections.

Technical Report

A technical report is a detailed report that provides information about a specific technical or scientific problem or project. Technical reports are often used in engineering, science, and other technical fields to document research and development work.

Progress Report

A progress report provides an update on the progress of a research project or program over a specific period of time. Progress reports are typically used to communicate the status of a project to stakeholders, funders, or project managers.

Feasibility Report

A feasibility report assesses the feasibility of a proposed project or plan, providing an analysis of the potential risks, benefits, and costs associated with the project. Feasibility reports are often used in business, engineering, and other fields to determine the viability of a project before it is undertaken.

Field Report

A field report documents observations and findings from fieldwork, which is research conducted in the natural environment or setting. Field reports are often used in anthropology, ecology, and other social and natural sciences.

Experimental Report

An experimental report documents the results of a scientific experiment, including the hypothesis, methods, results, and conclusions. Experimental reports are often used in biology, chemistry, and other sciences to communicate the results of laboratory experiments.

Case Study Report

A case study report provides an in-depth analysis of a specific case or situation, often used in psychology, social work, and other fields to document and understand complex cases or phenomena.

Literature Review Report

A literature review report synthesizes and summarizes existing research on a specific topic, providing an overview of the current state of knowledge on the subject. Literature review reports are often used in social sciences, education, and other fields to identify gaps in the literature and guide future research.

Research Report Example

Following is a Research Report Example sample for Students:

Title: The Impact of Social Media on Academic Performance among High School Students

This study aims to investigate the relationship between social media use and academic performance among high school students. The study utilized a quantitative research design, which involved a survey questionnaire administered to a sample of 200 high school students. The findings indicate that there is a negative correlation between social media use and academic performance, suggesting that excessive social media use can lead to poor academic performance among high school students. The results of this study have important implications for educators, parents, and policymakers, as they highlight the need for strategies that can help students balance their social media use and academic responsibilities.

Introduction:

Social media has become an integral part of the lives of high school students. With the widespread use of social media platforms such as Facebook, Twitter, Instagram, and Snapchat, students can connect with friends, share photos and videos, and engage in discussions on a range of topics. While social media offers many benefits, concerns have been raised about its impact on academic performance. Many studies have found a negative correlation between social media use and academic performance among high school students (Kirschner & Karpinski, 2010; Paul, Baker, & Cochran, 2012).

Given the growing importance of social media in the lives of high school students, it is important to investigate its impact on academic performance. This study aims to address this gap by examining the relationship between social media use and academic performance among high school students.

Methodology:

The study utilized a quantitative research design, which involved a survey questionnaire administered to a sample of 200 high school students. The questionnaire was developed based on previous studies and was designed to measure the frequency and duration of social media use, as well as academic performance.

The participants were selected using a convenience sampling technique, and the survey questionnaire was distributed in the classroom during regular school hours. The data collected were analyzed using descriptive statistics and correlation analysis.

The findings indicate that the majority of high school students use social media platforms on a daily basis, with Facebook being the most popular platform. The results also show a negative correlation between social media use and academic performance, suggesting that excessive social media use can lead to poor academic performance among high school students.

Discussion:

The results of this study have important implications for educators, parents, and policymakers. The negative correlation between social media use and academic performance suggests that strategies should be put in place to help students balance their social media use and academic responsibilities. For example, educators could incorporate social media into their teaching strategies to engage students and enhance learning. Parents could limit their children’s social media use and encourage them to prioritize their academic responsibilities. Policymakers could develop guidelines and policies to regulate social media use among high school students.

Conclusion:

In conclusion, this study provides evidence of the negative impact of social media on academic performance among high school students. The findings highlight the need for strategies that can help students balance their social media use and academic responsibilities. Further research is needed to explore the specific mechanisms by which social media use affects academic performance and to develop effective strategies for addressing this issue.

Limitations:

One limitation of this study is the use of convenience sampling, which limits the generalizability of the findings to other populations. Future studies should use random sampling techniques to increase the representativeness of the sample. Another limitation is the use of self-reported measures, which may be subject to social desirability bias. Future studies could use objective measures of social media use and academic performance, such as tracking software and school records.

Implications:

The findings of this study have important implications for educators, parents, and policymakers. Educators could incorporate social media into their teaching strategies to engage students and enhance learning. For example, teachers could use social media platforms to share relevant educational resources and facilitate online discussions. Parents could limit their children’s social media use and encourage them to prioritize their academic responsibilities. They could also engage in open communication with their children to understand their social media use and its impact on their academic performance. Policymakers could develop guidelines and policies to regulate social media use among high school students. For example, schools could implement social media policies that restrict access during class time and encourage responsible use.

References:

• Kirschner, P. A., & Karpinski, A. C. (2010). Facebook® and academic performance. Computers in Human Behavior, 26(6), 1237-1245.
• Paul, J. A., Baker, H. M., & Cochran, J. D. (2012). Effect of online social networking on student academic performance. Journal of the Research Center for Educational Technology, 8(1), 1-19.
• Pantic, I. (2014). Online social networking and mental health. Cyberpsychology, Behavior, and Social Networking, 17(10), 652-657.
• Rosen, L. D., Carrier, L. M., & Cheever, N. A. (2013). Facebook and texting made me do it: Media-induced task-switching while studying. Computers in Human Behavior, 29(3), 948-958.

Note*: Above mention, Example is just a sample for the students’ guide. Do not directly copy and paste as your College or University assignment. Kindly do some research and Write your own.

Applications of Research Report

Research reports have many applications, including:

• Communicating research findings: The primary application of a research report is to communicate the results of a study to other researchers, stakeholders, or the general public. The report serves as a way to share new knowledge, insights, and discoveries with others in the field.
• Informing policy and practice : Research reports can inform policy and practice by providing evidence-based recommendations for decision-makers. For example, a research report on the effectiveness of a new drug could inform regulatory agencies in their decision-making process.
• Supporting further research: Research reports can provide a foundation for further research in a particular area. Other researchers may use the findings and methodology of a report to develop new research questions or to build on existing research.
• Evaluating programs and interventions : Research reports can be used to evaluate the effectiveness of programs and interventions in achieving their intended outcomes. For example, a research report on a new educational program could provide evidence of its impact on student performance.
• Demonstrating impact : Research reports can be used to demonstrate the impact of research funding or to evaluate the success of research projects. By presenting the findings and outcomes of a study, research reports can show the value of research to funders and stakeholders.
• Enhancing professional development : Research reports can be used to enhance professional development by providing a source of information and learning for researchers and practitioners in a particular field. For example, a research report on a new teaching methodology could provide insights and ideas for educators to incorporate into their own practice.

How to write Research Report

Here are some steps you can follow to write a research report:

• Identify the research question: The first step in writing a research report is to identify your research question. This will help you focus your research and organize your findings.
• Conduct research : Once you have identified your research question, you will need to conduct research to gather relevant data and information. This can involve conducting experiments, reviewing literature, or analyzing data.
• Organize your findings: Once you have gathered all of your data, you will need to organize your findings in a way that is clear and understandable. This can involve creating tables, graphs, or charts to illustrate your results.
• Write the report: Once you have organized your findings, you can begin writing the report. Start with an introduction that provides background information and explains the purpose of your research. Next, provide a detailed description of your research methods and findings. Finally, summarize your results and draw conclusions based on your findings.
• Proofread and edit: After you have written your report, be sure to proofread and edit it carefully. Check for grammar and spelling errors, and make sure that your report is well-organized and easy to read.
• Include a reference list: Be sure to include a list of references that you used in your research. This will give credit to your sources and allow readers to further explore the topic if they choose.
• Format your report: Finally, format your report according to the guidelines provided by your instructor or organization. This may include formatting requirements for headings, margins, fonts, and spacing.

Purpose of Research Report

The purpose of a research report is to communicate the results of a research study to a specific audience, such as peers in the same field, stakeholders, or the general public. The report provides a detailed description of the research methods, findings, and conclusions.

Some common purposes of a research report include:

• Sharing knowledge: A research report allows researchers to share their findings and knowledge with others in their field. This helps to advance the field and improve the understanding of a particular topic.
• Identifying trends: A research report can identify trends and patterns in data, which can help guide future research and inform decision-making.
• Addressing problems: A research report can provide insights into problems or issues and suggest solutions or recommendations for addressing them.
• Evaluating programs or interventions : A research report can evaluate the effectiveness of programs or interventions, which can inform decision-making about whether to continue, modify, or discontinue them.
• Meeting regulatory requirements: In some fields, research reports are required to meet regulatory requirements, such as in the case of drug trials or environmental impact studies.

When to Write Research Report

A research report should be written after completing the research study. This includes collecting data, analyzing the results, and drawing conclusions based on the findings. Once the research is complete, the report should be written in a timely manner while the information is still fresh in the researcher’s mind.

In academic settings, research reports are often required as part of coursework or as part of a thesis or dissertation. In this case, the report should be written according to the guidelines provided by the instructor or institution.

In other settings, such as in industry or government, research reports may be required to inform decision-making or to comply with regulatory requirements. In these cases, the report should be written as soon as possible after the research is completed in order to inform decision-making in a timely manner.

Overall, the timing of when to write a research report depends on the purpose of the research, the expectations of the audience, and any regulatory requirements that need to be met. However, it is important to complete the report in a timely manner while the information is still fresh in the researcher’s mind.

Characteristics of Research Report

There are several characteristics of a research report that distinguish it from other types of writing. These characteristics include:

• Objective: A research report should be written in an objective and unbiased manner. It should present the facts and findings of the research study without any personal opinions or biases.
• Systematic: A research report should be written in a systematic manner. It should follow a clear and logical structure, and the information should be presented in a way that is easy to understand and follow.
• Detailed: A research report should be detailed and comprehensive. It should provide a thorough description of the research methods, results, and conclusions.
• Accurate : A research report should be accurate and based on sound research methods. The findings and conclusions should be supported by data and evidence.
• Organized: A research report should be well-organized. It should include headings and subheadings to help the reader navigate the report and understand the main points.
• Clear and concise: A research report should be written in clear and concise language. The information should be presented in a way that is easy to understand, and unnecessary jargon should be avoided.
• Citations and references: A research report should include citations and references to support the findings and conclusions. This helps to give credit to other researchers and to provide readers with the opportunity to further explore the topic.

Advantages of Research Report

Research reports have several advantages, including:

• Communicating research findings: Research reports allow researchers to communicate their findings to a wider audience, including other researchers, stakeholders, and the general public. This helps to disseminate knowledge and advance the understanding of a particular topic.
• Providing evidence for decision-making : Research reports can provide evidence to inform decision-making, such as in the case of policy-making, program planning, or product development. The findings and conclusions can help guide decisions and improve outcomes.
• Supporting further research: Research reports can provide a foundation for further research on a particular topic. Other researchers can build on the findings and conclusions of the report, which can lead to further discoveries and advancements in the field.
• Demonstrating expertise: Research reports can demonstrate the expertise of the researchers and their ability to conduct rigorous and high-quality research. This can be important for securing funding, promotions, and other professional opportunities.
• Meeting regulatory requirements: In some fields, research reports are required to meet regulatory requirements, such as in the case of drug trials or environmental impact studies. Producing a high-quality research report can help ensure compliance with these requirements.

Limitations of Research Report

Despite their advantages, research reports also have some limitations, including:

• Time-consuming: Conducting research and writing a report can be a time-consuming process, particularly for large-scale studies. This can limit the frequency and speed of producing research reports.
• Expensive: Conducting research and producing a report can be expensive, particularly for studies that require specialized equipment, personnel, or data. This can limit the scope and feasibility of some research studies.
• Limited generalizability: Research studies often focus on a specific population or context, which can limit the generalizability of the findings to other populations or contexts.
• Potential bias : Researchers may have biases or conflicts of interest that can influence the findings and conclusions of the research study. Additionally, participants may also have biases or may not be representative of the larger population, which can limit the validity and reliability of the findings.
• Accessibility: Research reports may be written in technical or academic language, which can limit their accessibility to a wider audience. Additionally, some research may be behind paywalls or require specialized access, which can limit the ability of others to read and use the findings.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Home Market Research

Research Reports: Definition and How to Write Them

Reports are usually spread across a vast horizon of topics but are focused on communicating information about a particular topic and a niche target market. The primary motive of research reports is to convey integral details about a study for marketers to consider while designing new strategies.

Certain events, facts, and other information based on incidents need to be relayed to the people in charge, and creating research reports is the most effective communication tool. Ideal research reports are extremely accurate in the offered information with a clear objective and conclusion. These reports should have a clean and structured format to relay information effectively.

What are Research Reports?

Research reports are recorded data prepared by researchers or statisticians after analyzing the information gathered by conducting organized research, typically in the form of surveys or qualitative methods .

A research report is a reliable source to recount details about a conducted research. It is most often considered to be a true testimony of all the work done to garner specificities of research.

The various sections of a research report are:

• Background/Introduction
• Implemented Methods
• Results based on Analysis
• Deliberation

Learn more: Quantitative Research

Components of Research Reports

Research is imperative for launching a new product/service or a new feature. The markets today are extremely volatile and competitive due to new entrants every day who may or may not provide effective products. An organization needs to make the right decisions at the right time to be relevant in such a market with updated products that suffice customer demands.

The details of a research report may change with the purpose of research but the main components of a report will remain constant. The research approach of the market researcher also influences the style of writing reports. Here are seven main components of a productive research report:

• Research Report Summary: The entire objective along with the overview of research are to be included in a summary which is a couple of paragraphs in length. All the multiple components of the research are explained in brief under the report summary.  It should be interesting enough to capture all the key elements of the report.
• Research Introduction: There always is a primary goal that the researcher is trying to achieve through a report. In the introduction section, he/she can cover answers related to this goal and establish a thesis which will be included to strive and answer it in detail.  This section should answer an integral question: “What is the current situation of the goal?”.  After the research design was conducted, did the organization conclude the goal successfully or they are still a work in progress –  provide such details in the introduction part of the research report.
• Research Methodology: This is the most important section of the report where all the important information lies. The readers can gain data for the topic along with analyzing the quality of provided content and the research can also be approved by other market researchers . Thus, this section needs to be highly informative with each aspect of research discussed in detail.  Information needs to be expressed in chronological order according to its priority and importance. Researchers should include references in case they gained information from existing techniques.
• Research Results: A short description of the results along with calculations conducted to achieve the goal will form this section of results. Usually, the exposition after data analysis is carried out in the discussion part of the report.

Learn more: Quantitative Data

• Research Discussion: The results are discussed in extreme detail in this section along with a comparative analysis of reports that could probably exist in the same domain. Any abnormality uncovered during research will be deliberated in the discussion section.  While writing research reports, the researcher will have to connect the dots on how the results will be applicable in the real world.
• Research References and Conclusion: Conclude all the research findings along with mentioning each and every author, article or any content piece from where references were taken.

Learn more: Qualitative Observation

15 Tips for Writing Research Reports

Writing research reports in the manner can lead to all the efforts going down the drain. Here are 15 tips for writing impactful research reports:

• Prepare the context before starting to write and start from the basics:  This was always taught to us in school – be well-prepared before taking a plunge into new topics. The order of survey questions might not be the ideal or most effective order for writing research reports. The idea is to start with a broader topic and work towards a more specific one and focus on a conclusion or support, which a research should support with the facts.  The most difficult thing to do in reporting, without a doubt is to start. Start with the title, the introduction, then document the first discoveries and continue from that. Once the marketers have the information well documented, they can write a general conclusion.
• Keep the target audience in mind while selecting a format that is clear, logical and obvious to them:  Will the research reports be presented to decision makers or other researchers? What are the general perceptions around that topic? This requires more care and diligence. A researcher will need a significant amount of information to start writing the research report. Be consistent with the wording, the numbering of the annexes and so on. Follow the approved format of the company for the delivery of research reports and demonstrate the integrity of the project with the objectives of the company.
• Have a clear research objective: A researcher should read the entire proposal again, and make sure that the data they provide contributes to the objectives that were raised from the beginning. Remember that speculations are for conversations, not for research reports, if a researcher speculates, they directly question their own research.
• Establish a working model:  Each study must have an internal logic, which will have to be established in the report and in the evidence. The researchers’ worst nightmare is to be required to write research reports and realize that key questions were not included.

Learn more: Quantitative Observation

• Gather all the information about the research topic. Who are the competitors of our customers? Talk to other researchers who have studied the subject of research, know the language of the industry. Misuse of the terms can discourage the readers of research reports from reading further.
• Read aloud while writing. While reading the report, if the researcher hears something inappropriate, for example, if they stumble over the words when reading them, surely the reader will too. If the researcher can’t put an idea in a single sentence, then it is very long and they must change it so that the idea is clear to everyone.
• Check grammar and spelling. Without a doubt, good practices help to understand the report. Use verbs in the present tense. Consider using the present tense, which makes the results sound more immediate. Find new words and other ways of saying things. Have fun with the language whenever possible.
• Discuss only the discoveries that are significant. If some data are not really significant, do not mention them. Remember that not everything is truly important or essential within research reports.

Learn more: Qualitative Data

• Try and stick to the survey questions. For example, do not say that the people surveyed “were worried” about an research issue , when there are different degrees of concern.
• The graphs must be clear enough so that they understand themselves. Do not let graphs lead the reader to make mistakes: give them a title, include the indications, the size of the sample, and the correct wording of the question.
• Be clear with messages. A researcher should always write every section of the report with an accuracy of details and language.
• Be creative with titles – Particularly in segmentation studies choose names “that give life to research”. Such names can survive for a long time after the initial investigation.
• Create an effective conclusion: The conclusion in the research reports is the most difficult to write, but it is an incredible opportunity to excel. Make a precise summary. Sometimes it helps to start the conclusion with something specific, then it describes the most important part of the study, and finally, it provides the implications of the conclusions.
• Get a couple more pair of eyes to read the report. Writers have trouble detecting their own mistakes. But they are responsible for what is presented. Ensure it has been approved by colleagues or friends before sending the find draft out.

Learn more: Market Research and Analysis

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• Published: 22 September 2023

Revolutionizing healthcare: the role of artificial intelligence in clinical practice

• Shuroug A. Alowais 1 , 2 , 3 ,
• Sahar S. Alghamdi 2 , 3 , 4 ,
• Nada Alsuhebany 1 , 2 , 3 ,
• Tariq Alqahtani 2 , 3 , 4 ,
• Abdulrahman I. Alshaya 1 , 2 , 3 ,
• Sumaya N. Almohareb 1 , 2 , 3 ,
• Atheer Aldairem 1 , 2 , 3 ,
• Mohammed Alrashed 1 , 2 , 3 ,
• Khalid Bin Saleh 1 , 2 , 3 ,
• Hisham A. Badreldin 1 , 2 , 3 ,
• Majed S. Al Yami 1 , 2 , 3 ,
• Shmeylan Al Harbi 1 , 2 , 3 &
• Abdulkareem M. Albekairy 1 , 2 , 3  

BMC Medical Education volume  23 , Article number:  689 ( 2023 ) Cite this article

848 Accesses

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Metrics details

Introduction

Healthcare systems are complex and challenging for all stakeholders, but artificial intelligence (AI) has transformed various fields, including healthcare, with the potential to improve patient care and quality of life. Rapid AI advancements can revolutionize healthcare by integrating it into clinical practice. Reporting AI’s role in clinical practice is crucial for successful implementation by equipping healthcare providers with essential knowledge and tools.

Research Significance

This review article provides a comprehensive and up-to-date overview of the current state of AI in clinical practice, including its potential applications in disease diagnosis, treatment recommendations, and patient engagement. It also discusses the associated challenges, covering ethical and legal considerations and the need for human expertise. By doing so, it enhances understanding of AI’s significance in healthcare and supports healthcare organizations in effectively adopting AI technologies.

Materials and Methods

The current investigation analyzed the use of AI in the healthcare system with a comprehensive review of relevant indexed literature, such as PubMed/Medline, Scopus, and EMBASE, with no time constraints but limited to articles published in English. The focused question explores the impact of applying AI in healthcare settings and the potential outcomes of this application.

Integrating AI into healthcare holds excellent potential for improving disease diagnosis, treatment selection, and clinical laboratory testing. AI tools can leverage large datasets and identify patterns to surpass human performance in several healthcare aspects. AI offers increased accuracy, reduced costs, and time savings while minimizing human errors. It can revolutionize personalized medicine, optimize medication dosages, enhance population health management, establish guidelines, provide virtual health assistants, support mental health care, improve patient education, and influence patient-physician trust.

AI can be used to diagnose diseases, develop personalized treatment plans, and assist clinicians with decision-making. Rather than simply automating tasks, AI is about developing technologies that can enhance patient care across healthcare settings. However, challenges related to data privacy, bias, and the need for human expertise must be addressed for the responsible and effective implementation of AI in healthcare.

Peer Review reports

Artificial Intelligence (AI) is a rapidly evolving field of computer science that aims to create machines that can perform tasks that typically require human intelligence. AI includes various techniques such as machine learning (ML), deep learning (DL), and natural language processing (NLP). Large Language Models (LLMs) are a type of AI algorithm that uses deep learning techniques and massively large data sets to understand, summarize, generate, and predict new text-based content [ 1 , 2 , 3 ]. LLMs have been architected to generate text-based content and possess broad applicability for various NLP tasks, including text generation, translation, content summary, rewriting, classification, categorization, and sentiment analysis. NLP is a subfield of AI that focuses on the interaction between computers and humans through natural language, including understanding, interpreting, and generating human language. NLP involves various techniques such as text mining, sentiment analysis, speech recognition, and machine translation. Over the years, AI has undergone significant transformations, from the early days of rule-based systems to the current era of ML and deep learning algorithms [ 1 , 2 , 3 ].

AI has evolved since the first AI program was developed in 1951 by Christopher Strachey. At that time, AI was in its infancy and was primarily an academic research topic. In 1956, John McCarthy organized the Dartmouth Conference, where he coined the term “Artificial Intelligence.“ This event marked the beginning of the modern AI era. In the 1960 and 1970 s, AI research focused on rule-based and expert systems. However, this approach was limited by the need for more computing power and data [ 4 ].

In the 1980 and 1990 s, AI research shifted to ML and neural networks, which allowed machines to learn from data and improve their performance over time. This period saw the development of systems such as IBM’s Deep Blue, which defeated world chess champion Garry Kasparov in 1997. In the 2000s, AI research continued to evolve, focusing on NLP and computer vision, which led to the development of virtual assistants, such as Apple’s Siri and Amazon’s Alexa, which could understand natural language and respond to user requests (Fig.  1 ) [ 3 , 4 ].

Tracing the Evolution of AI with a Better Understanding of the Relationship Between AI, ML, DL, and NLP

Today, AI is transforming healthcare, finance, and transportation, among other fields, and its impact is only set to grow. In academia, AI has been used to develop intelligent tutoring systems, which are computer programs that can adapt to the needs of individual students. These systems have improved student learning outcomes in various subjects, including math and science. In research, AI has been used to analyze large datasets and identify patterns that would be difficult for humans to detect; this has led to breakthroughs in fields such as genomics and drug discovery. AI has been used in healthcare settings to develop diagnostic tools and personalized treatment plans. As AI continues to evolve, it is crucial to ensure that it is developed responsibly and for the benefit of all [ 5 , 6 , 7 , 8 ].

The rapid progression of AI technology presents an opportunity for its application in clinical practice, potentially revolutionizing healthcare services. It is imperative to document and disseminate information regarding AI’s role in clinical practice, to equip healthcare providers with the knowledge and tools necessary for effective implementation in patient care. This review article aims to explore the current state of AI in healthcare, its potential benefits, limitations, and challenges, and to provide insights into its future development. By doing so, this review aims to contribute to a better understanding of AI’s role in healthcare and facilitate its integration into clinical practice.

Materials and methods

Search strategy and inclusion.

Indexed databases, including PubMed/Medline (National Library of Medicine), Scopus, and EMBASE, were independently searched with notime restrictions, but the searches were limited to the English language.

Databases search protocol and keywords

In the review article, the authors extensively examined the use of AI in healthcare settings. The authors analyzed various combinations of keywords such as NLP in healthcare, ML in healthcare, DL in healthcare, LLM in healthcare, AI in personalized medicine, AI in patient monitoring, AI ethics in healthcare, predictive analytics in healthcare, AI in medical diagnosis, and AI applications in healthcare. By imposing language restrictions, the authors ensured a comprehensive analysis of the topic.

Data extraction

Publications were screened through a meticulous review of titles and abstracts. Only those that met the specific criteria were included. Any disagreements or concerns about the literature or methodology were discussed in detail among the authors.

AI assistance in diagnostics

Diagnosis accuracy.

With all the advances in medicine, effective disease diagnosis is still considered a challenge on a global scale. The development of early diagnostic tools is an ongoing challenge due to the complexity of the various disease mechanisms and the underlying symptoms. AI can revolutionize different aspects of health care, including diagnosis. ML is an area of AI that uses data as an input resource in which the accuracy is highly dependent on the quantity as well as the quality of the input data that can combat some of the challenges and complexity of diagnosis [ 9 ]. ML, in short, can assist in decision-making, manage workflow, and automate tasks in a timely and cost-effective manner. Also, deep learning added layers utilizing Convolutional Neural Networks (CNN) and data mining techniques that help identify data patterns. These are highly applicable in identifying key disease detection patterns among big datasets. These tools are highly applicable in healthcare systems for diagnosing, predicting, or classifying diseases [ 10 ].

AI is still in its early stages of being fully utilized for medical diagnosis. However, more data are emerging for the application of AI in diagnosing different diseases, such as cancer. A study was published in the UK where authors input a large dataset of mammograms into an AI system for breast cancer diagnosis. This study showed that utilizing an AI system to interpret mammograms had an absolute reduction in false positives and false negatives by 5.7% and 9.4%, respectively [ 11 ]. Another study was conducted in South Korea, where authors compared AI diagnoses of breast cancer versus radiologists. The AI-utilized diagnosis was more sensitive to diagnose breast cancer with mass compared to radiologists, 90% vs. 78%, respectively. Also, AI was better at detecting early breast cancer (91%) than radiologists 74% [ 12 ].

Furthermore, a study utilized deep learning to detect skin cancer which showed that an AI using CNN accurately diagnosed melanoma cases compared to dermatologists and recommended treatment options [ 13 , 14 ]. Researchers utilized AI technology in many other disease states, such as detecting diabetic retinopathy [ 15 ] and EKG abnormality and predicting risk factors for cardiovascular diseases [ 16 , 17 ]. Furthermore, deep learning algorithms are used to detect pneumonia from chest radiography with sensitivity and specificity of 96% and 64% compared to radiologists 50% and 73%, respectively [ 18 ]. Also, a study was done on a dataset of 625 cases to diagnose acute appendicitis early to predict the need for appendix surgery using various ML techniques; the results showed that the random forest algorithm achieved the highest performance, accurately predicting appendicitis in 83.75% of cases, with a precision of 84.11%, sensitivity of 81.08%, and specificity of 81.01%. The improved method aids healthcare specialists in making informed decisions for appendicitis diagnoses and treatment. Furthermore, the authors suggest that similar techniques can be utilized to analyze images of patients with appendicitis or even to detect infections such as COVID-19 using blood specimens or images [ 19 ].

AI tools can improve accuracy, reduce costs, and save time compared to traditional diagnostic methods. Additionally, AI can reduce the risk of human errors and provide more accurate results in less time. In the future, AI technology could be used to support medical decisions by providing clinicians with real-time assistance and insights. Researchers continue exploring ways to use AI in medical diagnosis and treatment, such as analyzing medical images, X-rays, CT scans, and MRIs. By leveraging ML techniques, AI can also help identify abnormalities, detect fractures, tumors, or other conditions, and provide quantitative measurements for faster and more accurate medical diagnosis.

Clinical laboratory testing provides critical information for diagnosing, treating, and monitoring diseases. It is an essential part of modern healthcare which continuously incorporates new technology to support clinical decision-making and patient safety [ 20 ]. AI has the potential to transform clinical laboratory testing by improving the accuracy, speed, and efficiency of laboratory processes. The role of AI in clinical microbiology is currently progressing and expanding. Several ML systems were developed to detect, identify, and quantify microorganisms, diagnose and classify diseases, and predict clinical outcomes. These ML systems used data from various sources to build the AI diagnosis such as genomic data of microorganisms, gene sequencing, metagenomic sequencing results of the original specimen, and microscopic imaging [ 21 ]. Moreover, gram stain classification to gram positives/negatives and cocci/rods is another essential application of using deep convolutional neural networks that reveal high sensitivity and specificity [ 22 ]. A published systematic review showed that numerous MLs were evaluated for microorganism identification and antibiotic susceptibility testing; however, several limitations are associated with the current models that must be addressed before incorporating them into clinical practice [ 23 ]. For malaria, Taesik et al. found that using ML algorithms combined with digital in-line holographic microscopy (DIHM) effectively detected malaria-infected red blood cells without staining. This AI technology is rapid, sensitive, and cost-effective in diagnosing malaria [ 24 ].

The projected benefits of using AI in clinical laboratories include but are not limited to, increased efficacy and precision. Automated techniques in blood cultures, susceptibility testing, and molecular platforms have become standard in numerous laboratories globally, contributing significantly to laboratory efficiency [ 21 , 25 ]. Automation and AI have substantially improved laboratory efficiency in areas like blood cultures, susceptibility testing, and molecular platforms. This allows for a result within the first 24 to 48 h, facilitating the selection of suitable antibiotic treatment for patients with positive blood cultures [ 21 , 26 ]. Consequently, incorporating AI in clinical microbiology laboratories can assist in choosing appropriate antibiotic treatment regimens, a critical factor in achieving high cure rates for various infectious diseases [ 21 , 26 ].

ML research in medicine has rapidly expanded, which could greatly help the healthcare providers in the emergency department (ED) as they face challenging difficulties from the rising burden of diseases, greater demand for time and health services, higher societal expectations, and increasing health expenditures [ 27 ]. Emergency department providers understand that integrating AI into their work processes is necessary for solving these problems by enhancing efficiency, and accuracy, and improving patient outcomes [ 28 , 29 ]. Additionally, there may be a chance for algorithm support and automated decision-making to optimize ED flow measurements and resource allocation [ 30 ]. AI algorithms can analyze patient data to assist with triaging patients based on urgency; this helps prioritize high-risk cases, reducing waiting times and improving patient flow [ 31 ]. Introducing a reliable symptom assessment tool can rule out other causes of illness to reduce the number of unnecessary visits to the ED. A series of AI-enabled machines can directly question the patient, and a sufficient explanation is provided at the end to ensure appropriate assessment and plan.

Moreover, AI-powered decision support systems can provide real-time suggestions to healthcare providers, aiding diagnosis, and treatment decisions. Patients are evaluated in the ED with little information, and physicians frequently must weigh probabilities when risk stratifying and making decisions. Faster clinical data interpretation is crucial in ED to classify the seriousness of the situation and the need for immediate intervention. The risk of misdiagnosing patients is one of the most critical problems affecting medical practitioners and healthcare systems. Diagnostic mistakes in the healthcare sector can be expensive and fatal. A study found that diagnostic errors, particularly in patients who visit the ED, directly contribute to a greater mortality rate and a more extended hospital stay [ 32 ]. Fortunately, AI can assist in the early detection of patients with life-threatening diseases and promptly alert clinicians so the patients can receive immediate attention. Lastly, AI can help optimize health care sources in the ED by predicting patient demand, optimizing therapy selection (medication, dose, route of administration, and urgency of intervention), and suggesting emergency department length of stay. By analyzing patient-specific data, AI systems can offer insights into optimal therapy selection, improving efficiency and reducing overcrowding.

AI in genomic medicine

The fusion of AI and genotype analysis holds immense promise in the realms of disease surveillance, prediction, and personalized medicine [ 33 ]. When applied to large populations, AI can effectively monitor for emerging disease threats (such as COVID-19), while genomic data can provide valuable insights into genetic markers associated with increased susceptibility to specific diseases [ 34 ] By training ML algorithms to identify these markers in real-time data, we can facilitate the early detection of potential outbreaks. Moreover, the use of genotype data can aid in refining disease risk predictions, as ML algorithms can recognize complex patterns of genetic variations linked with disease susceptibility that might elude traditional statistical methods as summarized in Fig.  2 [ 35 , 36 ]. The prediction of phenotypes, or observable characteristics shaped by genes and environmental factors, also becomes possible with this combination.

Schematic representation of the process starting with the extraction of DNA/RNA, followed by sequencing. The subsequent genotypic alignment is performed using neural networks and deep learning. Probability calculations are achieved through applying statistical methods and M: The graph’s Y-axis denotes the probability (expressed in percentage) of a particular type of disease (hypertension, depression, breast cancer, and Alzheimer’s disease), while the X-axis signifies the count of gene mutations. Negative numbers indicate gene deletions, whereas positive values represent gene additions or nucleic acid mutations

ML algorithms make it feasible to predict a spectrum of phenotypes ranging from simple traits like eye color to more intricate ones like the response to certain medications or disease susceptibility. A specific area where AI and ML have demonstrated significant efficacy is the identification of genetic variants associated with distinctive traits or pathologies. Examining extensive genomic datasets allows these techniques to detect intricate patterns often elusive to manual analysis. For instance, a groundbreaking study employed a deep neural network to identify genetic variants associated with autism spectrum disorder (ASD), successfully predicting ASD status by relying solely on genomic data [ 37 ]. In the field of oncology, categorizing cancers into clinically relevant molecular subtypes can be accomplished using transcriptomic profiling. Such molecular classifications, first developed for breast cancer and later extended to other cancers like colorectal, ovarian, and sarcomas, hold substantial implications for diagnosis, prognosis, and treatment selection [ 38 , 39 ]. Traditional computational methods for subtyping cancers, such as support vector machines (SVMs) or k-nearest neighbors, are susceptible to errors due to batch effects and may only focus on a small set of signature genes, thus neglecting vital biological information [ 40 ].

The advent of high-throughput genomic sequencing technologies, combined with advancements in AI and ML, has laid a strong foundation for accelerating personalized medicine and drug discovery [ 41 ]. Despite being a treasure trove of valuable insights, the complex nature of extensive genomic data presents substantial obstacles to its interpretation. The field of drug discovery has dramatically benefited from the application of AI and ML. The simultaneous analysis of extensive genomic data and other clinical parameters, such as drug efficacy or adverse effects, facilitates the identification of novel therapeutic targets or the repurposing of existing drugs for new applications [ 42 , 43 , 44 , 45 , 46 ]. One of the prevalent challenges in drug development is non-clinical toxicity, which leads to a significant percentage of drug failures during clinical trials. However, the rise of computational modeling is opening up the feasibility of predicting drug toxicity, which can be instrumental in improving the drug development process [ 46 ]. This capability is particularly vital for addressing common types of drug toxicity, such as cardiotoxicity and hepatotoxicity, which often lead to post-market withdrawal of drugs.

AI assistance in treatment

Precision medicine and clinical decision support.

Personalized treatment, also known as precision medicine or personalized medicine, is an approach that tailors medical care to individual patients based on their unique characteristics, such as genetics, environment, lifestyle, and biomarkers [ 47 ]. This individualized approach aims to improve patient outcomes by providing targeted interventions that are more effective, efficient, and safe. AI has emerged as a valuable tool in advancing personalized treatment, offering the potential to analyze complex datasets, predict outcomes, and optimize treatment strategies [ 47 , 48 ]. Personalized treatment represents a pioneering field that demonstrates the potential of precision medicine on a large scale [ 49 ]. Nevertheless, the ability to provide real-time recommendations relies on the advancement of ML algorithms capable of predicting patients who may require specific medications based on genomic information. The key to tailoring medications and dosages to patients lies in the pre-emptive genotyping of patients prior to the actual need for such information [ 49 , 50 ].

The potential applications of AI in assisting clinicians with treatment decisions, particularly in predicting therapy response, have gained recognition [ 49 ]. A study conducted by Huang et al. where authors utilized patients’ gene expression data for training a support ML, successfully predicted the response to chemotherapy [ 51 ]. In this study, the authors included 175 cancer patients incorporating their gene-expression profiles to predict the patients’ responses to various standard-of-care chemotherapies. Notably, the research showed encouraging outcomes, achieving a prediction accuracy of over 80% across multiple drugs. These findings demonstrate the promising role of AI in treatment response prediction. In another study performed by Sheu et al., the authors aimed to predict the response to different classes of antidepressants using electronic health records (EHR) of 17,556 patients and AI [ 52 ]. The AI models considered features predictive of treatment selection to minimize confounding factors and showed good prediction performance. The study demonstrated that antidepressant response could be accurately predicted using real-world EHR data with AI modeling, suggesting the potential for developing clinical decision support systems for more effective treatment selection. While considerable progress has been made in leveraging AI techniques and genomics to forecast treatment outcomes, it is essential to conduct further prospective and retrospective clinical research and studies [ 47 , 50 ]. These endeavors are necessary for generating the comprehensive data required to train the algorithms effectively, ensure their reliability in real-world settings, and further develop AI-based clinical decision tools.

Dose optimization and therapeutic drug monitoring

AI plays a crucial role in dose optimization and adverse drug event prediction, offering significant benefits in enhancing patient safety and improving treatment outcomes [ 53 ]. By leveraging AI algorithms, healthcare providers can optimize medication dosages tailored to individual patients and predict potential adverse drug events, thereby reducing risks and improving patient care. In a study that aimed to develop an AI-based prediction model for prothrombin time international normalized ratio (PT/INR) and a decision support system for warfarin maintenance dose optimization [ 54 ] The authors analyzed data from 19,719 inpatients across three institutions, and the algorithm outperformed expert physicians with significant differences in predicting future PT/INRs and the generated individualized warfarin dose was reliable.

On the contrary, a novel dose optimization system—CURATE.AI—is an AI-derived platform for dynamically optimizing chemotherapy doses based on individual patient data [ 55 ]. A study was conducted to validate this system as an open-label, prospective trial in patients with advanced solid tumors treated with three different chemotherapy regimens. CURATE.AI generated personalized doses for subsequent cycles based on the correlation between chemotherapy dose variation and tumor marker readouts. The integration of CURATE.AI into the clinical workflow showed successful incorporation and potential benefits in terms of reducing chemotherapy dose and improving patient response rates and durations compared to the standard of care. These findings support the need for prospective validation through randomized clinical trials and indicate the potential of AI in optimizing chemotherapy dosing and lowering the risk of adverse drug events.

Therapeutic drug monitoring (TDM) is a process used to optimize drug dosing in individual patients. It is predominantly utilized for drugs with a narrow therapeutic index to avoid both underdosing insufficiently medicating as well as toxic levels. TDM aims to ensure that patients receive the right drug, at the right dose, at the right time, to achieve the desired therapeutic outcome while minimizing adverse effects [ 56 ]. The use of AI in TDM has the potential to revolutionize how drugs are monitored and prescribed. AI algorithms can be trained to predict an individual’s response to a given drug based on their genetic makeup, medical history, and other factors. This personalized approach to drug therapy can lead to more effective treatments and better patient outcomes [ 57 , 58 ].

One example of AI in TDM is using ML algorithms to predict drug-drug interactions. By analyzing large datasets of patient data, these algorithms can identify potential drug interactions. This can help to reduce the risk of adverse drug reactions, and cost and improve patient outcomes [ 59 ]. Another application of AI in TDM using predictive analytics to identify patients at high risk of developing adverse drug reactions. By analyzing patient data and identifying potential risk factors, healthcare providers can take proactive steps to prevent adverse events before they occur [ 60 ]. Overall, the use of AI in TDM has the potential to improve patient outcomes, reduce healthcare costs, and enhance the accuracy and efficiency of drug dosing. As this technology continues to evolve, AI will likely play an increasingly important role in the field of TDM.

AI assistance in population health management

Predictive analytics and risk assessment.

Population health management increasingly uses predictive analytics to identify and guide health initiatives. In data analytics, predictive analytics is a discipline that significantly utilizes modeling, data mining, AI, and ML. In order to anticipate the future, it analyzes historical and current data [ 61 , 62 ]. ML algorithms and other technologies are used to analyze data and develop predictive models to improve patient outcomes and reduce costs. One area where predictive analytics can be instrumental is in identifying patients at risk of developing chronic diseases such as endocrine or cardiac diseases. By analyzing data such as medical history, demographics, and lifestyle factors, predictive models can identify patients at higher risk of developing these conditions and target interventions to prevent or treat them [ 61 ]. Predicting hospital readmissions is another area where predictive analytics can be applied. By analyzing patient demographics, medical history, and social health factors, predictive models can identify patients at higher risk of hospital readmissions and target interventions to prevent readmissions (Fig.  3 ) [ 62 , 63 , 64 ]; this can help reduce healthcare costs and improve patient outcomes which is the reason behind launching new companies such as “Reveal ®” [ 65 ].

Unlocking the Power of Patient Data with AI-Driven Predictive Analytics

AI can be used to optimize healthcare by improving the accuracy and efficiency of predictive models. AI algorithms can analyze large amounts of data and identify patterns and relationships that may not be obvious to human analysts; this can help improve the accuracy of predictive models and ensure that patients receive the most appropriate interventions. AI can also automate specific public health management tasks, such as patient outreach and care coordination [ 61 , 62 ]. Which can help reduce healthcare costs and improve patient outcomes by ensuring patients receive timely and appropriate care. However, it is pivotal to note that the success of predictive analytics in public health management depends on the quality of data and the technological infrastructure used to develop and implement predictive models. In addition, human supervision is vital to ensure the appropriateness and effectiveness of interventions for at-risk patients. In summary, predictive analytics plays an increasingly important role in population health. Using ML algorithms and other technologies, healthcare organizations can develop predictive models that identify patients at risk for chronic disease or readmission to the hospital [ 61 , 62 , 63 , 64 ].

Furthermore, AI is needed to address these challenges regarding vaccine production and supply chain bottlenecks. Testing algorithms on real-time vaccine supply chains can be challenging. To overcome this, investing in research and development is essential to create robust algorithms that can accurately predict and optimize vaccine supply chains. Edge analytics can also detect anomalies and predict Disease X events and associated risks to the healthcare system [ 66 ].

From a Saudi perspective, Sehaa, a big data analytics tool in Saudi Arabia, uses Twitter data to detect diseases, and it found that dermal diseases, heart diseases, hypertension, cancer, and diabetes are the top five diseases in the country [ 67 ]. Riyadh has the highest awareness-to-afflicted ratio for six of the fourteen diseases detected, while Taif is the healthiest city with the lowest number of disease cases and a high number of awareness activities. These findings highlight the potential of predictive analytics in population health management and the need for targeted interventions to prevent and treat chronic diseases in Saudi Arabia [ 67 ]. AI can optimize health care by improving the accuracy and efficiency of predictive models and automating certain tasks in population health management [ 62 ]. However, successfully implementing predictive analytics requires high-quality data, advanced technology, and human oversight to ensure appropriate and effective interventions for patients.

Establishment of working groups, guidelines, and frameworks

AI is transforming how guidelines are established in various fields. In healthcare, guidelines usually take much time, from establishing the knowledge gap that needs to be fulfilled to publishing and disseminating these guidelines. AI can help identify newly published data based on data from clinical trials and real-world patient outcomes within the same area of interest which can then facilitate the first stage of mining information. Then, under the supervision of scientists and experts in the field, AI algorithms can analyze vast amounts of data to identify patterns and trends that can inform the development of evidence-based guidelines in real-time, which allows for a fast exchange of information with essential supervision clinicians for its clinical and ethical implications [ 68 , 69 , 70 , 71 , 72 , 73 ].

Several professional organizations have developed frameworks for addressing concerns unique to developing, reporting, and validating AI in medicine [ 69 , 70 , 71 , 72 , 73 ]. Instead of focusing on the clinical application of AI, these frameworks are more concerned with educating the technological creators of AI by providing instructions on encouraging transparency in the design and reporting of AI algorithms [ 69 ]. Additionally, regulatory regulation of AI is still in its infancy. The US Food and Drug Administration (FDA) is now developing guidelines on critically assessing real-world applications of AI in medicine while publishing a framework to guide the role of AI and ML in software as medical devices [ 74 ]. The European Commission has spearheaded a multidisciplinary effort to improve the credibility of AI [ 75 ], and the European Medicines Agency (EMA) has deemed the regulation of AI a strategic priority [ 76 ]. These legislative efforts are meant to shape the healthcare future to be better equipped to be a technology-driven sector. Overall, the role of AI in establishing guidelines is to provide data-driven insights and recommendations based on vast amounts of information, which can lead to more efficient and effective decision-making, better outcomes, and reduced costs. However, it is crucial to ensure that AI-based guidelines are transparent, fair, unbiased, and informed by human expertise and ethical considerations [ 68 ].

AI in drug information and consultation

AI would propose a new support system to assist practical decision-making tools for healthcare providers. In recent years, healthcare institutions have provided a greater leveraging capacity of utilizing automation-enabled technologies to boost workflow effectiveness and reduce costs while promoting patient safety, accuracy, and efficiency [ 77 ]. By introducing advanced technologies like NLP, ML, and data analytics, AI can significantly provide real-time, accurate, and up-to-date information for practitioners at the hospital. According to the McKinsey Global Institute, ML and AI in the pharmaceutical sector have the potential to contribute approximately $100 billion annually to the US healthcare system [ 78 ]. Researchers claim that these technologies enhance decision-making, maximize creativity, increase the effectiveness of research and clinical trials, and produce new tools that benefit healthcare providers, patients, insurers, and regulators [ 78 ]. AI enables quick and comprehensive retrieval of drug-related information from different resources through its ability to analyze the current medical literature, drug databases, and clinical guidelines to provide accurate and evidence-based decisions for healthcare providers. Using automated response systems, AI-powered virtual assistants can handle common questions and provide detailed medical information to healthcare providers [ 79 ]. AI-powered chatbots help reduce the workload on healthcare providers, allowing them to focus on more complicated cases that require their expertise. Also, AI algorithms can generate specific recommendations for individual patients, considering factors like health conditions, past medical and medication history, and social/lifestyle preferences, allowing healthcare professionals to optimize medication choices and dosages [ 80 , 81 ].

AI-powered patient care

Ai virtual healthcare assistance.

With continuously increasing demands of health care services and limited resources worldwide, finding solutions to overcome these challenges is essential [ 82 ]. Virtual health assistants are a new and innovative technology transforming the healthcare industry to support healthcare professionals. It is designed to simulate human conversation to offer personalized patient care based on input from the patient [ 83 ]. These digital assistants use AI-powered applications, chatbots, sounds, and interfaces. Virtual assistants can help patients with tasks such as identifying the underlying problem based on the patient’s symptoms, providing medical advice, reminding patients to take their medications, scheduling doctor appointments, and monitoring vital signs. In addition, digital assistants can collect information daily regarding patients’ health and forward the reports to the assigned physician. By taking off some of these responsibilities from human healthcare providers, virtual assistants can help to reduce their workload and improve patient outcomes.

Furthermore, these tools can always be available, making it easier for patients to access healthcare when needed [ 84 ]. Another medical service that an AI-driven phone application can provide is triaging patients and finding out how urgent their problem is, based on the entered symptoms into the app. The National Health Service (NHS) has tested this app in north London, and now about 1.2 million people are using this AI chatbot to answer their questions instead of calling the NHS non-emergency number [ 85 ]. In addition, introducing intelligent speakers into the market has a significant benefit in the lives of elderly and chronically ill patients who are unable to use smartphone apps efficiently [ 86 ]. Overall, virtual health assistants have the potential to significantly improve the quality, efficiency, and cost of healthcare delivery while also increasing patient engagement and providing a better experience for them.

AI mental health support

AI has the potential to revolutionize mental health support by providing personalized and accessible care to individuals [ 87 , 88 ]. Several studies showed the effectiveness and accessibility of using Web-based or Internet-based cognitive-behavioral therapy (CBT) as a psychotherapeutic intervention [ 89 , 90 ]. Even though psychiatric practitioners rely on direct interaction and behavioral observation of the patient in clinical practice compared to other practitioners, AI-powered tools can supplement their work in several ways. AI-powered mental health applications can assist in the early detection and diagnosis of mental health conditions, as well as provide tailored treatment and support [ 88 , 89 , 90 , 91 ] These applications can also offer round-the-clock support, reducing the need for in-person appointments and wait times. Furthermore, these digital tools can be used to monitor patient progress and medication adherence, providing valuable insights into treatments’ effectiveness [ 88 ].

The current published studies addressing the applicability of AI in mental health concluded that depression is the most commonly investigated mental disorder [ 88 ]. Moreover, AI-powered apps prove their benefits in patients with substance use disorder. A recent study evaluated the utility of a mental health digital app called Woebot in patients with substance use disorders. This study found that using Woebot was significantly associated with improved substance use, cravings, depression, and anxiety [ 92 ]. While AI-powered mental health diagnosis holds promise, some significant limitations must be addressed. One of the main limitations is the risk of bias in the data and algorithms used in AI-powered diagnosis. If the data used to train AI algorithms does not represent diverse populations, it can lead to biased and inaccurate results. Additionally, AI-powered diagnosis may not take into account the complexity of mental health conditions, which can present differently in different people. Finally, there is a risk that AI-powered diagnosis may lead to a lack of personalization and empathy in mental health care, which is an important aspect of successful treatment [ 93 ]. Therefore, while AI-powered diagnosis can be a valuable tool in mental health care, it should be used as a supplement to, rather than a replacement for, professional diagnosis and treatment.

AI in enhancing patient education and mitigating healthcare provider burnout

One of the emerging applications of AI is patient education [ 94 ]. AI-powered chatbots are being implemented in various healthcare contexts, such as diet recommendations [ 95 , 96 ], smoking cessation, and cognitive-behavioral therapy [ 97 ]. Patient education is integral to healthcare, as it enables individuals to understand their medical diagnosis, treatment options, and preventative measures [ 98 ]. Informed patients are more likely to adhere to their treatment regimens and achieve better health outcomes [ 99 ]. AI has the potential to play a significant role in patient education by providing personalized and interactive information and guidance to patients and their caregivers [ 100 ]. For example, in patients with prostate cancer, introducing a prostate cancer communication assistant (PROSCA) chatbot offered a clear to moderate increase in participants’ knowledge about prostate cancer [ 101 ]. Researchers found that ChatGPT, an AI Chatbot founded by OpenAI, can help patients with diabetes understand their diagnosis and treatment options, monitor their symptoms and adherence, provide feedback and encouragement, and answer their questions [ 102 ]. AI technology can also be applied to rewrite patient education materials into different reading levels. This suggests that AI can empower patients to take greater control of their health by ensuring that patients can understand their diagnosis, treatment options, and self-care instructions [ 103 ]. However, there are also some challenges and limitations that need to be addressed, such as ensuring the accuracy, reliability, and transparency of the information provided by AI, respecting the privacy and confidentiality of the patients’ data, and maintaining a human touch and empathy in the communication [ 104 ]. The use of AI in patient education is still in its early stages, but it has the potential to revolutionize the way that patients learn about their health. As AI technology continues to develop, we can expect to see even more innovative and effective ways to use AI to educate patients.

Are individuals more inclined towards AI than human healthcare providers

Public perception of the benefits and risks of AI in healthcare systems is a crucial factor in determining its adoption and integration. People’s feelings about AI replacing or augmenting human healthcare practitioners, its role in educating and empowering patients, and its impact on the quality and efficiency of care, as well as on the well-being of healthcare workers, are all important considerations. In medicine, patients often trust medical staff unconditionally and believe that their illness will be cured due to a medical phenomenon known as the placebo effect. In other words, patient-physician trust is vital in improving patient care and the effectiveness of their treatment [ 105 ]. For the relationship between patients and an AI-based healthcare delivery system to succeed, building a relationship based on trust is imperative [ 106 ].

Research on whether people prefer AI over healthcare practitioners has shown mixed results depending on the context, type of AI system, and participants’ characteristics [ 107 , 108 ]. Some surveys have indicated that people are generally willing to use or interact with AI for health-related purposes such as diagnosis, treatment, monitoring, or decision support [ 108 , 109 , 110 ]. However, other studies have suggested that people still prefer human healthcare practitioners over AI, especially for complex or sensitive issues such as mental health, chronic diseases, or end-of-life care [ 108 , 111 ]. In a US-based study, 60% of participants expressed discomfort with providers relying on AI for their medical care. However, the same study found that 80% of Americans would be willing to use AI-powered tools to help manage their health [ 109 ]. In another survey, responders’ comfort with AI varied based on clinical application, and most patients felt that AI would improve their healthcare, which suggests that people are generally willing to use AI for healthcare-related purposes and that patient education, concerns, and comfort levels should be accounted for when planning for integration of AI [ 110 ]. Moreover, people’s trust and acceptance of AI may vary depending on their age, gender, education level, cultural background, and previous experience with technology [ 111 , 112 ].

Future directions and considerations for clinical implementation

Obstacles and solutions.

AI has the potential to revolutionize clinical practice, but several challenges must be addressed to realize its full potential. Among these challenges is the lack of quality medical data, which can lead to inaccurate outcomes. Data privacy, availability, and security are also potential limitations to applying AI in clinical practice. Additionally, determining relevant clinical metrics and selecting an appropriate methodology is crucial to achieving the desired outcomes. Human contribution to the design and application of AI tools is subject to bias and could be amplified by AI if not closely monitored [ 113 ]. The AI-generated data and/or analysis could be realistic and convincing; however, hallucination could also be a major issue which is the tendency to fabricate and create false information that cannot be supported by existing evidence [ 114 ]. This can be particularly problematic regarding sensitive areas such as patient care. Thus, the development of AI tools has implications for current health professions education, highlighting the necessity of recognizing human fallibility in areas including clinical reasoning and evidence-based medicine [ 115 ]. Finally, human expertise and involvement are essential to ensure the appropriate and practical application of AI to meet clinical needs and the lack of this expertise could be a drawback for the practical application of AI.

Addressing these challenges and providing constructive solutions will require a multidisciplinary approach, innovative data annotation methods, and the development of more rigorous AI techniques and models. Creating practical, usable, and successfully implemented technology would be possible by ensuring appropriate cooperation between computer scientists and healthcare providers. By merging current best practices for ethical inclusivity, software development, implementation science, and human-computer interaction, the AI community will have the opportunity to create an integrated best practice framework for implementation and maintenance [ 116 ]. Additionally, a collaboration between multiple health care settings is required to share data and ensure its quality, as well as verify analyzed outcomes which will be critical to the success of AI in clinical practice. Another suggestion is to provide appropriate training and education that starts from the undergraduate level of all the health care practitioners and proceeds to the continuous development and improvement for the practitioners working in the current practice to ensure the proper adaptation which provides the best patient care and avoid any legal or ethical issues or misinterpretation of the outcomes without verifying the results [ 117 ]. Medical schools are encouraged to incorporate AI-related topics into their medical curricula. A study conducted among radiology residents showed that 86% of students agreed that AI would change and improve their practice, and up to 71% felt that AI should be taught at medical schools for better understanding and application [ 118 ]. This integration ensures that future healthcare professionals receive foundational knowledge about AI and its applications from the early stages of their education.

Legal, ethical, and risk associated with AI in healthcare system

Converting AI and big data into secure and efficient practical applications, services, and procedures in healthcare involves significant costs and risks. Consequently, safeguarding the commercial interests of AI and data-driven healthcare technologies has emerged as an increasingly crucial subject [ 119 ]. In the past, only medical professionals could measure vital signs such as blood pressure, glucose levels, and heart rate [ 48 ]. However, contemporary mobile applications now enable the continuous collection of such information. Nevertheless, addressing the ethical risks associated with AI implementation is imperative, particularly concerning data privacy and confidentiality violations, informed consent, and patient autonomy [ 48 , 119 ]. Given the prominence of big data and AI in healthcare and precision medicine, robust data protection legislation becomes paramount to safeguarding individual privacy. Countries around the world have introduced laws to protect the privacy of their citizens, such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States and the General Data Protection Regulation (GDPR) in Europe [ 120 , 121 ]. While HIPAA protects only relevant health information produced by covered entities, the GDPR has implemented extensive data protection law within the EU, creating a significant global shift in data protection [ 120 , 121 ].

One of the major causes that can compromise patient data, disrupt critical healthcare operations, and jeopardize patient safety with the use of AI in the healthcare system is increased cyberattacks [ 66 , 122 ]. Predictive algorithms can be employed to detect and prevent these cyber threats. To safeguard data privacy and maintain system integrity, it’s essential to deeply investigate cybersecurity and the cyber risk landscape of healthcare systems [ 66 , 123 ]. By implementing a variety of robust AI algorithms, the risk associated with relying on a singular solution can be mitigated [ 66 , 123 ]. While data privacy and security breaches are challenges associated with AI in healthcare [ 122 ], it offers significant advantages such as task streamlining, enhanced efficiency, time and resource savings, research support, and reduced physician stress [ 122 ]. In the context of ethical considerations, an epistemological framework for ethical assessment has been proposed to prioritize ethical awareness, transparency, and accountability when evaluating digital technology’s impact on healthcare supply chain participants [ 123 , 124 ].

The integration of AI in healthcare has immense potential to revolutionize patient care and outcomes. AI-driven predictive analytics can enhance the accuracy, efficiency, and cost-effectiveness of disease diagnosis and clinical laboratory testing. Additionally, AI can aid in population health management and guideline establishment, providing real-time, accurate information and optimizing medication choices. Integrating AI in virtual health and mental health support has shown promise in improving patient care. However, it is important to address limitations such as bias and lack of personalization to ensure equitable and effective use of AI.

Several measures must be taken to ensure responsible and effective implementation of AI in healthcare.

Firstly, comprehensive cybersecurity strategies and robust security measures should be developed and implemented to protect patient data and critical healthcare operations. Collaboration between healthcare organizations, AI researchers, and regulatory bodies is crucial to establishing guidelines and standards for AI algorithms and their use in clinical decision-making. Investment in research and development is also necessary to advance AI technologies tailored to address healthcare challenges.

AI algorithms can continuously examine factors such as population demographics, disease prevalence, and geographical distribution. This can identify patients at a higher risk of certain conditions, aiding in prevention or treatment. Edge analytics can also detect irregularities and predict potential healthcare events, ensuring that resources like vaccines are available where most needed.

Public perception of AI in healthcare varies, with individuals expressing willingness to use AI for health purposes while still preferring human practitioners in complex issues. Trust-building and patient education are crucial for the successful integration of AI in healthcare practice. Overcoming challenges like data quality, privacy, bias, and the need for human expertise is essential for responsible and effective AI integration.

Collaboration among stakeholders is vital for robust AI systems, ethical guidelines, and patient and provider trust. Continued research, innovation, and interdisciplinary collaboration are important to unlock the full potential of AI in healthcare. With successful integration, AI is anticipated to revolutionize healthcare, leading to improved patient outcomes, enhanced efficiency, and better access to personalized treatment and quality care.

Data availability

Not applicable.

Abbreviations

Artificial Intelligence

Autism Spectrum Disorder

Convolutional Neural Networks

Digital in-line holographic microscopy

General Data Protection Regulation

Electronic Health Record

Health Insurance Portability and Accountability Act

Large Language Models

Machine Learning

Natural Language Processing

Prostate cancer communication assistant

Prothrombin Time / International Normalized Ratio

Support vector Machines

Therapeutic drug monitoring

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Shuroug A. Alowais, Nada Alsuhebany, Abdulrahman I. Alshaya, Sumaya N. Almohareb, Atheer Aldairem, Mohammed Alrashed, Khalid Bin Saleh, Hisham A. Badreldin, Majed S. Al Yami, Shmeylan Al Harbi & Abdulkareem M. Albekairy

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Conceptualization: Shuroug A. Alowais; Sahar S. Alghamdi; Hisham A. Badreldin; Tariq Alqahtani.Writing - original draft: Shuroug A. Alowais, Sahar S. Alghamdi, Nada Alsuhebany, Tariq Alqahtani, Abdulrahman I. Alshaya, Sumaya N. Almohareb, Atheer Aldairem, Mohammed Alrashed, Khalid bin Saleh.Writing - review & editing: Shuroug A. Alowais, Sahar S. Alghamdi, Nada Alsuhebany, Tariq Alqahtani, Abdulrahman I. Alshaya, Sumaya N. Almohareb, Atheer Aldairem, Mohammed Alrashed, Khalid bin Saleh, Hisham A. Badreldin, Majed S. Al Yami, Shmeylan Al Harbi, Abdulkareem M. Albekairy.All authors reviewed the manuscript. During the preparation of this work the author(s) used [Chatsonic/ GPT4] to [edit and change format of the paper]. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication.

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Alowais, S.A., Alghamdi, S.S., Alsuhebany, N. et al. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Med Educ 23 , 689 (2023). https://doi.org/10.1186/s12909-023-04698-z

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DOI : https://doi.org/10.1186/s12909-023-04698-z

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Key facts about u.s. latinos for national hispanic heritage month.

National Hispanic Heritage Month, which begins in the United States each year on Sept. 15, celebrates U.S. Latinos , their culture and their history. Started in 1968 by Congress as Hispanic Heritage Week, it was expanded to a month in 1988. The celebration begins in the middle of September to coincide with independence days in several Latin American countries: Guatemala, Honduras, El Salvador, Nicaragua and Costa Rica celebrate theirs on Sept. 15, followed by Mexico on Sept. 16, Chile on Sept. 18 and Belize on Sept. 21.

Here are some key facts about the U.S. Latino population by geography and by characteristics such as language use and origin group.

As part of our ongoing research about Hispanics in the United States, we analyzed how this group has changed over time using data from the U.S. Census Bureau. The decennial census ( PL94-171 census data ) provided some historical state and national population counts, and population estimates provided the latest data on total population, births and immigration.

We also examined characteristics of the U.S. Hispanic population using the American Community Survey (ACS), which provides data for states and the U.S. on Hispanic origin, language use, country of birth and educational attainment. Data from the 2022 ACS and some from the 2010 ACS are from tabulations released by U.S. Census Bureau . Some ACS and census data is from Integrated Public Use Microdata Series (IPUMS) of the University of Minnesota.

The U.S. Hispanic population reached 63.6 million in 2022, up from 50.5 million in 2010. The 26% increase in the Hispanic population was faster than the nation’s 8% growth rate but slower than the 34% increase in the Asian population. In 2022, Hispanics made up nearly one-in-five people in the U.S. (19%), up from 16% in 2010 and just 5% in 1970.

Hispanics have played a major role in U.S. population growth over the past decade. The U.S. population grew by 24.5 million from 2010 to 2022, and Hispanics accounted for 53% of this increase – a greater share than any other racial or ethnic group. The next closest group is non-Hispanic people who identify with two or more races. Their population grew by 8.4 million during this time, accounting for 34% of the overall increase.

The number of Latinos who say they are multiracial has increased dramatically. More than 27 million Latinos identified with more than one race in 2022, up from 3 million in 2010. The increase could be due to several factors, including changes to the census form that make it easier for people to select multiple races and growing racial diversity.

Growth in the number of multiracial Latinos comes primarily from those who identify as at least one specific race and “some other race” (i.e., those who write in a response). This population grew from 2.1 million to 24.9 million between 2010 and 2022 and now represents about 91% of multiracial Latinos. The increase was due almost entirely to growth in the number of people who identified as White and some other race, according to the 2020 census.

At the same time, the number of Latinos who identified as White and no other race declined from 26.7 million in 2010 to 10.7 million in 2022.

The roughly 37.4 million people of Mexican origin in the U.S. represented nearly 60% of the nation’s Hispanic population in 2022. Those of Puerto Rican origin are the next largest group, at 5.9 million, which does not include another roughly 3.2 million Puerto Ricans who lived on the island in 2022. The U.S. population of Puerto Rican origin has grown partly due to people moving from Puerto Rico to the 50 states and the District of Columbia.

Six other Hispanic origin groups in the U.S. each have 1 million or more people: Salvadorans, Cubans, Dominicans, Guatemalans, Colombians and Hondurans. In addition, in 2022, Spaniards accounted for nearly 1 million U.S. Latinos.

Puerto Rico’s population has declined by about 500,000 since 2010, from 3.7 million to 3.2 million. Puerto Rico has experienced a net population loss since at least 2005 , driven by low fertility rates and migration to the U.S. mainland. An ongoing economic recession and devastation from hurricanes Maria and Irma in 2017 have also contributed to the decline.

Venezuelans have seen the fastest population growth among U.S. Latinos. From 2010 to 2022, the Venezuelan-origin population in the U.S. increased by 236% to 815,000. Four other groups saw growth rates exceeding 50%: Hondurans increased by 67%, followed by Guatemalans (62%), Dominicans (59%) and Colombians (51%).

By contrast, the number of people of Mexican origin in the U.S. grew by only 14%, by far the slowest rate among the most populous origin groups.

Hispanics are the largest racial or ethnic group in California and Texas. This demographic milestone in California happened in 2014 and was a first for the state with the nation’s largest Hispanic population . Latinos accounted for 40% of California’s population in 2022, among the greatest shares in the country.

That year, there were about 15.7 million Hispanics in California, up from 14.0 million in 2010. The non-Hispanic White population, the next largest group, declined from 15.0 million to 13.2 million during this time, reflecting a broader national trend .

In Texas, the state with the next largest Latino population (12.1 million), Latinos also made up 40% of the population in 2022 and became the largest racial or ethnic group in 2021. In Florida, the state with the third-largest Latino population (6.0 million), Latinos made up 27% of residents.

Rounding out the top five states with the largest Hispanic populations were New York (3.9 million) and Arizona (2.4 million). Eight more states had 1 million or more Hispanics: Illinois, New Jersey, Colorado, Georgia, Pennsylvania, North Carolina, Washington and New Mexico.

Vermont had the nation’s smallest Latino population (15,000) in 2022, followed by Maine (29,000), West Virginia and North Dakota (34,000 each), and South Dakota (42,000).

In New Mexico, Hispanics have been a majority of the population since 2021 and the state’s largest racial or ethnic group since the early 2000s. In 2022, the state was home to 1.1 million Hispanics.

Three states’ Hispanic populations increased by more than 1 million from 2010 to 2022. Texas (2.5 million increase), Florida (1.8 million) and California (1.6 million) accounted for almost half of the growth nationwide since 2010. Arizona (480,000 increase), New Jersey (464,000) and New York (432,000) had the next-biggest increases. All 50 states and the District of Columbia have seen growth in their Hispanic populations since 2010.

North and South Dakota’s Hispanic populations have grown the fastest since 2010. The number of Hispanics in North and South Dakota more than doubled (146% and 107% increases, respectively) from 2010 to 2022. But even with that growth, these states each had fewer than 45,000 Hispanics in 2022, among the smallest populations in the country.

The slowest growth was in New Mexico (10% increase), California (12%), and Illinois and New York (13% each), all states with significant Hispanic populations.

The makeup of the U.S. Hispanic population varies widely across major metropolitan areas.  Most of the metro areas in the Midwest, West and South with the largest Hispanic populations are predominantly Mexican. About three-quarters of Hispanics in the Chicago (77%) and Los Angeles (75%) areas identify as Mexican, as do 67% in the Houston area.

Metro areas in the Northeast tend to have more diverse Hispanic origins. For example, no origin group makes up more than 30% of the New York and Boston metro areas’ Hispanic populations.

Metro areas in Florida and the nation’s capital have distinctive Hispanic enclaves. Puerto Ricans make up 43% of Hispanics in the Orlando area, while Cubans make up 39% of Hispanics in the Miami area. In the Washington, D.C., metro area, Salvadorans account for 30% of Hispanics.

Catholics remain the largest religious group among Latinos in the U.S., but they have become a smaller share of the Latino population over the past decade. In 2022, 43% of Latinos adults identify as Catholic, down from 67% in 2010. Meanwhile, 30% of Latinos are religiously unaffiliated (describing themselves as atheist, agnostic or “nothing in particular”), up from 10% in 2010. The share of Latinos who identify as Protestants – including evangelical Protestants – has been relatively stable.

Newborns, not immigrants, have driven the recent growth among U.S. Hispanics. During the 2010s, an average of 1 million Hispanic babies were born each year, slightly more than during the 2000s. At the same time, about 350,000 Hispanic immigrants arrived annually, down substantially from the previous two decades.

The recent predominance of new births over immigration as a source of Hispanic population growth is a reversal of historical trends. In the 1980s and 1990s, immigration drove Hispanic population growth.

From 2020 to 2022, average annual births among Hispanics were slightly below the previous decade, but immigration decreased considerably, from 350,000 per year to 270,000. Some of this decline can be attributed to immigration into the U.S. stopping almost entirely during the early stages of the COVID-19 pandemic. With the removal of pandemic-related restrictions , the contribution of immigration to Hispanic growth appears to be returning to early 2010s levels.

The share of Latinos in the U.S. who speak English proficiently is growing. In 2022, 72% of Latinos ages 5 and older spoke English proficiently, up from 59% in 2000. U.S.-born Latinos are driving this growth: The share of U.S.-born Latinos who speak English proficiently increased by 9 percentage points in that span, compared with a 5-point increase among Latino immigrants. All told, 42.3 million Latinos in the U.S. spoke English proficiently in 2022.

At the same time, the share of Latinos who speak Spanish at home declined from 78% in 2000 to 68% in 2022, and most of that decline was among the U.S. born.

Even though the share of Latinos who speak Spanish at home has declined, the number who do so has grown from 24.6 million in 2000 to 39.7 million in 2022 because of the overall growth in the Latino population.

The share of U.S. Hispanics with college experience has increased since 2010. About 45% of U.S. Hispanic adults ages 25 and older had at least some college experience in 2022, up from 36% in 2010. The share of Hispanics with a bachelor’s degree or more education also increased, from 13% to 20%. The share with a bachelor’s degree or higher increased more among Hispanic women (from 14% to 22%) than Hispanic men (12% to 18%).

The number of Latinos enrolled in college or postgraduate education also increased between 2010 and 2022, from 2.9 million to 4.2 million. Among all U.S. undergraduate and graduate students, the share of Latinos increased from 14% in 2010 to 20% in 2022, slightly higher than the Latino share of the total population.

Four-in-five Latinos are U.S. citizens. As of 2022, 81% of Latinos living in the country are U.S. citizens, up from 74% in 2010. This includes people born in the U.S. and its territories (including Puerto Rico), people born abroad to American parents, and immigrants who have become naturalized citizens. The Center recently published citizenship rates among Hispanic origin groups for 2021; this data is not yet available for 2022.

Note: This post has been regularly updated since it was originally published on Sept. 16, 2014.

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Facts on Hispanics of Venezuelan origin in the United States, 2021

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About Pew Research Center Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .