Specifies the number of studies evaluated orselected
Steps, and targets of constructing a good review article are listed in Table 3 . To write a good review article the items in Table 3 should be implemented step by step. [ 11 – 13 ]
Steps of a systematic review
Formulation of researchable questions | Select answerable questions |
Disclosure of studies | Databases, and key words |
Evaluation of its quality | Quality criteria during selection of studies |
Synthesis | Methods interpretation, and synthesis of outcomes |
It might be helpful to divide the research question into components. The most prevalently used format for questions related to the treatment is PICO (P - Patient, Problem or Population; I-Intervention; C-appropriate Comparisons, and O-Outcome measures) procedure. For example In female patients (P) with stress urinary incontinence, comparisons (C) between transobturator, and retropubic midurethral tension-free band surgery (I) as for patients’ satisfaction (O).
In a systematic review on a focused question, methods of investigation used should be clearly specified.
Ideally, research methods, investigated databases, and key words should be described in the final report. Different databases are used dependent on the topic analyzed. In most of the clinical topics, Medline should be surveyed. However searching through Embase and CINAHL can be also appropriate.
While determining appropriate terms for surveying, PICO elements of the issue to be sought may guide the process. Since in general we are interested in more than one outcome, P, and I can be key elements. In this case we should think about synonyms of P, and I elements, and combine them with a conjunction AND.
One method which might alleviate the workload of surveying process is “methodological filter” which aims to find the best investigation method for each research question. A good example of this method can be found in PubMed interface of Medline. The Clinical Queries tool offers empirically developed filters for five different inquiries as guidelines for etiology, diagnosis, treatment, prognosis or clinical prediction.
As an indispensable component of the review process is to discriminate good, and bad quality researches from each other, and the outcomes should be based on better qualified researches, as far as possible. To achieve this goal you should know the best possible evidence for each type of question The first component of the quality is its general planning/design of the study. General planning/design of a cohort study, a case series or normal study demonstrates variations.
A hierarchy of evidence for different research questions is presented in Table 4 . However this hierarchy is only a first step. After you find good quality research articles, you won’t need to read all the rest of other articles which saves you tons of time. [ 14 ]
Determination of levels of evidence based on the type of the research question
I | Systematic review of Level II studies | Systematic review of Level II studies | Systematic review of Level II studies | Systematic review of Level II studies |
II | Randomized controlled study | Crross-sectional study in consecutive patients | Initial cohort study | Prospective cohort study |
III | One of the following: Non-randomized experimental study (ie. controlled pre-, and post-test intervention study) Comparative studies with concurrent control groups (observational study) (ie. cohort study, case-control study) | One of the following: Cross-sectional study in non-consecutive case series; diagnostic case-control study | One of the following: Untreated control group patients in a randomized controlled study, integrated cohort study | One of the following: Retrospective cohort study, case-control study (Note: these are most prevalently used types of etiological studies; for other alternatives, and interventional studies see Level III |
IV | Case series | Case series | Case series or cohort studies with patients at different stages of their disease states |
Rarely all researches arrive at the same conclusion. In this case a solution should be found. However it is risky to make a decision based on the votes of absolute majority. Indeed, a well-performed large scale study, and a weakly designed one are weighed on the same scale. Therefore, ideally a meta-analysis should be performed to solve apparent differences. Ideally, first of all, one should be focused on the largest, and higher quality study, then other studies should be compared with this basic study.
In conclusion, during writing process of a review article, the procedures to be achieved can be indicated as follows: 1) Get rid of fixed ideas, and obsessions from your head, and view the subject from a large perspective. 2) Research articles in the literature should be approached with a methodological, and critical attitude and 3) finally data should be explained in an attractive way.
If 2023 was the year the world discovered generative AI (gen AI) , 2024 is the year organizations truly began using—and deriving business value from—this new technology. In the latest McKinsey Global Survey on AI, 65 percent of respondents report that their organizations are regularly using gen AI, nearly double the percentage from our previous survey just ten months ago. Respondents’ expectations for gen AI’s impact remain as high as they were last year , with three-quarters predicting that gen AI will lead to significant or disruptive change in their industries in the years ahead.
This article is a collaborative effort by Alex Singla , Alexander Sukharevsky , Lareina Yee , and Michael Chui , with Bryce Hall , representing views from QuantumBlack, AI by McKinsey, and McKinsey Digital.
Organizations are already seeing material benefits from gen AI use, reporting both cost decreases and revenue jumps in the business units deploying the technology. The survey also provides insights into the kinds of risks presented by gen AI—most notably, inaccuracy—as well as the emerging practices of top performers to mitigate those challenges and capture value.
Interest in generative AI has also brightened the spotlight on a broader set of AI capabilities. For the past six years, AI adoption by respondents’ organizations has hovered at about 50 percent. This year, the survey finds that adoption has jumped to 72 percent (Exhibit 1). And the interest is truly global in scope. Our 2023 survey found that AI adoption did not reach 66 percent in any region; however, this year more than two-thirds of respondents in nearly every region say their organizations are using AI. 1 Organizations based in Central and South America are the exception, with 58 percent of respondents working for organizations based in Central and South America reporting AI adoption. Looking by industry, the biggest increase in adoption can be found in professional services. 2 Includes respondents working for organizations focused on human resources, legal services, management consulting, market research, R&D, tax preparation, and training.
Also, responses suggest that companies are now using AI in more parts of the business. Half of respondents say their organizations have adopted AI in two or more business functions, up from less than a third of respondents in 2023 (Exhibit 2).
Most respondents now report that their organizations—and they as individuals—are using gen AI. Sixty-five percent of respondents say their organizations are regularly using gen AI in at least one business function, up from one-third last year. The average organization using gen AI is doing so in two functions, most often in marketing and sales and in product and service development—two functions in which previous research determined that gen AI adoption could generate the most value 3 “ The economic potential of generative AI: The next productivity frontier ,” McKinsey, June 14, 2023. —as well as in IT (Exhibit 3). The biggest increase from 2023 is found in marketing and sales, where reported adoption has more than doubled. Yet across functions, only two use cases, both within marketing and sales, are reported by 15 percent or more of respondents.
Gen AI also is weaving its way into respondents’ personal lives. Compared with 2023, respondents are much more likely to be using gen AI at work and even more likely to be using gen AI both at work and in their personal lives (Exhibit 4). The survey finds upticks in gen AI use across all regions, with the largest increases in Asia–Pacific and Greater China. Respondents at the highest seniority levels, meanwhile, show larger jumps in the use of gen Al tools for work and outside of work compared with their midlevel-management peers. Looking at specific industries, respondents working in energy and materials and in professional services report the largest increase in gen AI use.
The latest survey also shows how different industries are budgeting for gen AI. Responses suggest that, in many industries, organizations are about equally as likely to be investing more than 5 percent of their digital budgets in gen AI as they are in nongenerative, analytical-AI solutions (Exhibit 5). Yet in most industries, larger shares of respondents report that their organizations spend more than 20 percent on analytical AI than on gen AI. Looking ahead, most respondents—67 percent—expect their organizations to invest more in AI over the next three years.
Where are those investments paying off? For the first time, our latest survey explored the value created by gen AI use by business function. The function in which the largest share of respondents report seeing cost decreases is human resources. Respondents most commonly report meaningful revenue increases (of more than 5 percent) in supply chain and inventory management (Exhibit 6). For analytical AI, respondents most often report seeing cost benefits in service operations—in line with what we found last year —as well as meaningful revenue increases from AI use in marketing and sales.
As businesses begin to see the benefits of gen AI, they’re also recognizing the diverse risks associated with the technology. These can range from data management risks such as data privacy, bias, or intellectual property (IP) infringement to model management risks, which tend to focus on inaccurate output or lack of explainability. A third big risk category is security and incorrect use.
Respondents to the latest survey are more likely than they were last year to say their organizations consider inaccuracy and IP infringement to be relevant to their use of gen AI, and about half continue to view cybersecurity as a risk (Exhibit 7).
Conversely, respondents are less likely than they were last year to say their organizations consider workforce and labor displacement to be relevant risks and are not increasing efforts to mitigate them.
In fact, inaccuracy— which can affect use cases across the gen AI value chain , ranging from customer journeys and summarization to coding and creative content—is the only risk that respondents are significantly more likely than last year to say their organizations are actively working to mitigate.
Some organizations have already experienced negative consequences from the use of gen AI, with 44 percent of respondents saying their organizations have experienced at least one consequence (Exhibit 8). Respondents most often report inaccuracy as a risk that has affected their organizations, followed by cybersecurity and explainability.
Our previous research has found that there are several elements of governance that can help in scaling gen AI use responsibly, yet few respondents report having these risk-related practices in place. 4 “ Implementing generative AI with speed and safety ,” McKinsey Quarterly , March 13, 2024. For example, just 18 percent say their organizations have an enterprise-wide council or board with the authority to make decisions involving responsible AI governance, and only one-third say gen AI risk awareness and risk mitigation controls are required skill sets for technical talent.
The latest survey also sought to understand how, and how quickly, organizations are deploying these new gen AI tools. We have found three archetypes for implementing gen AI solutions : takers use off-the-shelf, publicly available solutions; shapers customize those tools with proprietary data and systems; and makers develop their own foundation models from scratch. 5 “ Technology’s generational moment with generative AI: A CIO and CTO guide ,” McKinsey, July 11, 2023. Across most industries, the survey results suggest that organizations are finding off-the-shelf offerings applicable to their business needs—though many are pursuing opportunities to customize models or even develop their own (Exhibit 9). About half of reported gen AI uses within respondents’ business functions are utilizing off-the-shelf, publicly available models or tools, with little or no customization. Respondents in energy and materials, technology, and media and telecommunications are more likely to report significant customization or tuning of publicly available models or developing their own proprietary models to address specific business needs.
Respondents most often report that their organizations required one to four months from the start of a project to put gen AI into production, though the time it takes varies by business function (Exhibit 10). It also depends upon the approach for acquiring those capabilities. Not surprisingly, reported uses of highly customized or proprietary models are 1.5 times more likely than off-the-shelf, publicly available models to take five months or more to implement.
Gen AI is a new technology, and organizations are still early in the journey of pursuing its opportunities and scaling it across functions. So it’s little surprise that only a small subset of respondents (46 out of 876) report that a meaningful share of their organizations’ EBIT can be attributed to their deployment of gen AI. Still, these gen AI leaders are worth examining closely. These, after all, are the early movers, who already attribute more than 10 percent of their organizations’ EBIT to their use of gen AI. Forty-two percent of these high performers say more than 20 percent of their EBIT is attributable to their use of nongenerative, analytical AI, and they span industries and regions—though most are at organizations with less than $1 billion in annual revenue. The AI-related practices at these organizations can offer guidance to those looking to create value from gen AI adoption at their own organizations.
To start, gen AI high performers are using gen AI in more business functions—an average of three functions, while others average two. They, like other organizations, are most likely to use gen AI in marketing and sales and product or service development, but they’re much more likely than others to use gen AI solutions in risk, legal, and compliance; in strategy and corporate finance; and in supply chain and inventory management. They’re more than three times as likely as others to be using gen AI in activities ranging from processing of accounting documents and risk assessment to R&D testing and pricing and promotions. While, overall, about half of reported gen AI applications within business functions are utilizing publicly available models or tools, gen AI high performers are less likely to use those off-the-shelf options than to either implement significantly customized versions of those tools or to develop their own proprietary foundation models.
What else are these high performers doing differently? For one thing, they are paying more attention to gen-AI-related risks. Perhaps because they are further along on their journeys, they are more likely than others to say their organizations have experienced every negative consequence from gen AI we asked about, from cybersecurity and personal privacy to explainability and IP infringement. Given that, they are more likely than others to report that their organizations consider those risks, as well as regulatory compliance, environmental impacts, and political stability, to be relevant to their gen AI use, and they say they take steps to mitigate more risks than others do.
Gen AI high performers are also much more likely to say their organizations follow a set of risk-related best practices (Exhibit 11). For example, they are nearly twice as likely as others to involve the legal function and embed risk reviews early on in the development of gen AI solutions—that is, to “ shift left .” They’re also much more likely than others to employ a wide range of other best practices, from strategy-related practices to those related to scaling.
In addition to experiencing the risks of gen AI adoption, high performers have encountered other challenges that can serve as warnings to others (Exhibit 12). Seventy percent say they have experienced difficulties with data, including defining processes for data governance, developing the ability to quickly integrate data into AI models, and an insufficient amount of training data, highlighting the essential role that data play in capturing value. High performers are also more likely than others to report experiencing challenges with their operating models, such as implementing agile ways of working and effective sprint performance management.
The online survey was in the field from February 22 to March 5, 2024, and garnered responses from 1,363 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 981 said their organizations had adopted AI in at least one business function, and 878 said their organizations were regularly using gen AI in at least one function. To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP.
Alex Singla and Alexander Sukharevsky are global coleaders of QuantumBlack, AI by McKinsey, and senior partners in McKinsey’s Chicago and London offices, respectively; Lareina Yee is a senior partner in the Bay Area office, where Michael Chui , a McKinsey Global Institute partner, is a partner; and Bryce Hall is an associate partner in the Washington, DC, office.
They wish to thank Kaitlin Noe, Larry Kanter, Mallika Jhamb, and Shinjini Srivastava for their contributions to this work.
This article was edited by Heather Hanselman, a senior editor in McKinsey’s Atlanta office.
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Start your review by referring to the title and author of the article, the title of the journal, and the year of publication in the first paragraph. For example: The article, "Condom use will increase the spread of AIDS," was written by Anthony Zimmerman, a Catholic priest. 4. Write the introduction.
Research Paper, Review Paper Format. Sets the stage with a concise title and a descriptive abstract summarising the review's scope and findings. Lays the groundwork by presenting the research question, justifying the review's importance, and highlighting knowledge gaps. Details the research methods used to select, assess, and synthesise ...
2. Read the article thoroughly: Carefully read the article multiple times to get a complete understanding of its content, arguments, and conclusions. As you read, take notes on key points, supporting evidence, and any areas that require further exploration or clarification. 3. Summarize the main ideas: In your review's introduction, briefly ...
How to review a paper. A good peer review requires disciplinary expertise, a keen and critical eye, and a diplomatic and constructive approach. Credit: dmark/iStockphoto. As junior scientists develop their expertise and make names for themselves, they are increasingly likely to receive invitations to review research manuscripts.
Include this information when writing up the method for your review. 5 Look for previous reviews on the topic. Use them as a springboard for your own review, critiquing the earlier reviews, adding more recently published material, and pos-sibly exploring a different perspective. Exploit their refer-ences as another entry point into the literature.
A well-written review article must summarize key research findings, reference must-read articles, describe current areas of agreement as well as controversies and debates, point out gaps in current knowledge, depict unanswered questions, and suggest directions for future research ( 1 ). During the last decades, there has been a great expansion ...
Examples of literature reviews. Step 1 - Search for relevant literature. Step 2 - Evaluate and select sources. Step 3 - Identify themes, debates, and gaps. Step 4 - Outline your literature review's structure. Step 5 - Write your literature review.
Step 1: Define the right organization for your review. Knowing the future setup of your paper will help you define how you should read the article. Here are the steps to follow: Summarize the article — seek out the main points, ideas, claims, and general information presented in the article.
To write a good critical review, you will have to engage in the mental processes of analyzing (taking apart) the work-deciding what its major components are and determining how these parts (i.e., paragraphs, sections, or chapters) contribute to the work as a whole. Analyzing the work will help you focus on how and why the author makes certain ...
The best proposals are timely and clearly explain why readers should pay attention to the proposed topic. It is not enough for a review to be a summary of the latest growth in the literature: the ...
Think about structuring your review like an inverted pyramid. Put the most important information at the top, followed by details and examples in the center, and any additional points at the very bottom. Here's how your outline might look: 1. Summary of the research and your overall impression. In your own words, summarize what the manuscript ...
Both a reaction paper and an article review will start with a content summary. ️. For scholarly material, you will present a structured review after the summary. ️. For popular magazine content, you will write a response that sums up your emotions, thoughts, and reactions that the material aroused.
A good review article provides readers with an in-depth understanding of a field and highlights key gaps and challenges to address with future research. Writing a review article also helps to expand the writer's knowledge of their specialist area and to develop their analytical and communication skills, amongst other benefits. Thus, the ...
Step 2: Read the Article Thoroughly. Begin by thoroughly reading the article. Take notes on key points, arguments, and evidence presented by the author. Understand the author's main thesis and the context in which the article was written.
A review article can also be called a literature review, or a review of literature. It is a survey of previously published research on a topic. It should give an overview of current thinking on the topic. And, unlike an original research article, it will not present new experimental results. Writing a review of literature is to provide a ...
The topic must at least be: interesting to you (ideally, you should have come across a series of recent papers related to your line of work that call for a critical summary), an important aspect of the field (so that many readers will be interested in the review and there will be enough material to write it), and.
Basically, the conclusion section of a review article should provide a summary of key findings from the main body of the manuscript. In this section, the author needs to revisit the critical points of the paper as well as highlight the accuracy, validity, and relevance of the inferences drawn in the article review.
Writing a Literature Review. A literature review is a document or section of a document that collects key sources on a topic and discusses those sources in conversation with each other (also called synthesis ). The lit review is an important genre in many disciplines, not just literature (i.e., the study of works of literature such as novels ...
Describe the title, abstract, and introduction: A good starting point to begin structuring your review is by drafting the title, abstract, and introduction. Explicitly writing down what your review aims to address in the field will help shape the rest of your article. Be unbiased and critical: Evaluate every piece of evidence in a critical but ...
You should now have a list of comments and suggestions for a complete peer review. The full peer-review document can comprise the following sections: 1. Introduction: Mirror the article, state ...
Start the first paragraph of your review with concise and clear article identification that specifies its title, author, name of the resource (e.g., journal, web, etc.), and the year of publication. Intro. Following the identification, write a short introductory paragraph.
Briefly summarize what the paper is about and what the findings are. Try to put the findings of the paper into the context of the existing literature and current knowledge. Indicate the significance of the work and if it is novel or mainly confirmatory. Indicate the work's strengths, its quality and completeness.
Crucially, citation practices do not differ between the two styles of paper. However, for your convenience, we have provided two versions of our APA 7 sample paper below: one in student style and one in professional style. Note: For accessibility purposes, we have used "Track Changes" to make comments along the margins of these samples.
1. Research Paper Title. A research paper title is read first, and read the most. The title serves two purposes: informing readers and attracting attention. Therefore, your research paper title should be clear, descriptive, and concise. If you can, avoid technical jargon and abbreviations.
The essays are a place to show us who you are and who you'll be in our community. It's a chance to add depth to something that is important to you and tell the admissions committee more about your background or goals. Below you'll find selected examples of essays that "worked," as nominated by our admissions committee.
Record your expenses. The first step to start saving money is figuring out how much you spend. Keep track of all your expenses—that means every coffee, household item and cash tip as well as regular monthly bills. Record your expenses however is easiest for you—a pencil and paper, a simple spreadsheet or a free online spending tracker or app.
Though starting to write the review article promptly seems to be very alluring, the time you spend for the determination of important issues won't be a waste of time. The PRISMA statement elaborated to write a well-designed review articles contains a 27-item checklist (Table 1). It will be reasonable to fulfill the requirements of these items ...
Respondents most often report that their organizations required one to four months from the start of a project to put gen AI into production, though the time it takes varies by business function (Exhibit 10). It also depends upon the approach for acquiring those capabilities. Not surprisingly, reported uses of highly customized or proprietary ...
Use our Form Filler tool to fill out your child's form on a desktop or laptop computer and then print it.If you are experiencing technical issues with the Form Filler, download a PDF. Tips to complete your child's form:. Do not sign your child's form until asked to do so by a passport acceptance agent or employee.; You can apply for a passport book, a passport card, or both documents.
In Georgia, demand for industrial power is surging to record highs, with the projection of new electricity use for the next decade now 17 times what it was only recently. Arizona Public Service ...