From articles published in Journal of Educational Evaluation for Health Professions during the past two years (2023 ~ ).
Brief report
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Are ChatGPT’s knowledge and interpretation ability comparable to those of medical students in Korea for taking a parasitology examination?: a descriptive study
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Sun Huh
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J Educ Eval Health Prof. 2023;20:1. Published online January 11, 2023
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DOI: https://doi.org/10.3352/jeehp.2023.20.1
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Abstract
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Supplementary Material
- This study aimed to compare the knowledge and interpretation ability of ChatGPT, a language model of artificial general intelligence, with those of medical students in Korea by administering a parasitology examination to both ChatGPT and medical students. The examination consisted of 79 items and was administered to ChatGPT on January 1, 2023. The examination results were analyzed in terms of ChatGPT’s overall performance score, its correct answer rate by the items’ knowledge level, and the acceptability of its explanations of the items. ChatGPT’s performance was lower than that of the medical students, and ChatGPT’s correct answer rate was not related to the items’ knowledge level. However, there was a relationship between acceptable explanations and correct answers. In conclusion, ChatGPT’s knowledge and interpretation ability for this parasitology examination were not yet comparable to those of medical students in Korea.
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Citations
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Review
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Can an artificial intelligence chatbot be the author of a scholarly article?
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Ju Yoen Lee
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J Educ Eval Health Prof. 2023;20:6. Published online February 27, 2023
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DOI: https://doi.org/10.3352/jeehp.2023.20.6
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11,713
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810
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58
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56
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Abstract
PDF
Supplementary Material
- At the end of 2022, the appearance of ChatGPT, an artificial intelligence (AI) chatbot with amazing writing ability, caused a great sensation in academia. The chatbot turned out to be very capable, but also capable of deception, and the news broke that several researchers had listed the chatbot (including its earlier version) as co-authors of their academic papers. In response, Nature and Science expressed their position that this chatbot cannot be listed as an author in the papers they publish. Since an AI chatbot is not a human being, in the current legal system, the text automatically generated by an AI chatbot cannot be a copyrighted work; thus, an AI chatbot cannot be an author of a copyrighted work. Current AI chatbots such as ChatGPT are much more advanced than search engines in that they produce original text, but they still remain at the level of a search engine in that they cannot take responsibility for their writing. For this reason, they also cannot be authors from the perspective of research ethics.
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Editorial
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Issues in the 3rd year of the COVID-19 pandemic, including computer-based testing, study design, ChatGPT, journal metrics, and appreciation to reviewers
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Sun Huh
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J Educ Eval Health Prof. 2023;20:5. Published online January 31, 2023
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DOI: https://doi.org/10.3352/jeehp.2023.20.5
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- LIMITED EVIDENCE SUGGESTS THAT CHATGPT MAY HAVE ACCURATE RESPONSES IN MEDICAL AND DENTAL RESEARCH
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Research article
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Performance of ChatGPT, Bard, Claude, and Bing on the Peruvian National Licensing Medical Examination: a cross-sectional study
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Betzy Clariza Torres-Zegarra
, Wagner Rios-Garcia
, Alvaro Micael Ñaña-Cordova
, Karen Fatima Arteaga-Cisneros
, Xiomara Cristina Benavente Chalco
, Marina Atena Bustamante Ordoñez
, Carlos Jesus Gutierrez Rios
, Carlos Alberto Ramos Godoy
, Kristell Luisa Teresa Panta Quezada
, Jesus Daniel Gutierrez-Arratia
, Javier Alejandro Flores-Cohaila
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J Educ Eval Health Prof. 2023;20:30. Published online November 20, 2023
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DOI: https://doi.org/10.3352/jeehp.2023.20.30
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Abstract
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Supplementary Material
- Purpose
We aimed to describe the performance and evaluate the educational value of justifications provided by artificial intelligence chatbots, including GPT-3.5, GPT-4, Bard, Claude, and Bing, on the Peruvian National Medical Licensing Examination (P-NLME).
Methods
This was a cross-sectional analytical study. On July 25, 2023, each multiple-choice question (MCQ) from the P-NLME was entered into each chatbot (GPT-3, GPT-4, Bing, Bard, and Claude) 3 times. Then, 4 medical educators categorized the MCQs in terms of medical area, item type, and whether the MCQ required Peru-specific knowledge. They assessed the educational value of the justifications from the 2 top performers (GPT-4 and Bing).
Results
GPT-4 scored 86.7% and Bing scored 82.2%, followed by Bard and Claude, and the historical performance of Peruvian examinees was 55%. Among the factors associated with correct answers, only MCQs that required Peru-specific knowledge had lower odds (odds ratio, 0.23; 95% confidence interval, 0.09–0.61), whereas the remaining factors showed no associations. In assessing the educational value of justifications provided by GPT-4 and Bing, neither showed any significant differences in certainty, usefulness, or potential use in the classroom.
Conclusion
Among chatbots, GPT-4 and Bing were the top performers, with Bing performing better at Peru-specific MCQs. Moreover, the educational value of justifications provided by the GPT-4 and Bing could be deemed appropriate. However, it is essential to start addressing the educational value of these chatbots, rather than merely their performance on examinations.
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Reviews
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Application of artificial intelligence chatbots, including ChatGPT, in education, scholarly work, programming, and content generation and its prospects: a narrative review
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Tae Won Kim
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J Educ Eval Health Prof. 2023;20:38. Published online December 27, 2023
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DOI: https://doi.org/10.3352/jeehp.2023.20.38
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9,886
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Abstract
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Supplementary Material
- This study aims to explore ChatGPT’s (GPT-3.5 version) functionalities, including reinforcement learning, diverse applications, and limitations. ChatGPT is an artificial intelligence (AI) chatbot powered by OpenAI’s Generative Pre-trained Transformer (GPT) model. The chatbot’s applications span education, programming, content generation, and more, demonstrating its versatility. ChatGPT can improve education by creating assignments and offering personalized feedback, as shown by its notable performance in medical exams and the United States Medical Licensing Exam. However, concerns include plagiarism, reliability, and educational disparities. It aids in various research tasks, from design to writing, and has shown proficiency in summarizing and suggesting titles. Its use in scientific writing and language translation is promising, but professional oversight is needed for accuracy and originality. It assists in programming tasks like writing code, debugging, and guiding installation and updates. It offers diverse applications, from cheering up individuals to generating creative content like essays, news articles, and business plans. Unlike search engines, ChatGPT provides interactive, generative responses and understands context, making it more akin to human conversation, in contrast to conventional search engines’ keyword-based, non-interactive nature. ChatGPT has limitations, such as potential bias, dependence on outdated data, and revenue generation challenges. Nonetheless, ChatGPT is considered to be a transformative AI tool poised to redefine the future of generative technology. In conclusion, advancements in AI, such as ChatGPT, are altering how knowledge is acquired and applied, marking a shift from search engines to creativity engines. This transformation highlights the increasing importance of AI literacy and the ability to effectively utilize AI in various domains of life.
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Journal of General Internal Medicine.2024; 39(16): 3282. CrossRef - ChatGPT vs. sleep disorder specialist responses to common sleep queries: Ratings by experts and laypeople
Jiyoung Kim, Seo-Young Lee, Jee Hyun Kim, Dong-Hyeon Shin, Eun Hye Oh, Jin A Kim, Jae Wook Cho
Sleep Health.2024; 10(6): 665. CrossRef - Technology integration into Chinese as a foreign language learning in higher education: An integrated bibliometric analysis and systematic review (2000–2024)
Binze Xu
Language Teaching Research.2024;[Epub] CrossRef - The Transformative Power of Generative Artificial Intelligence for Achieving the Sustainable Development Goal of Quality Education
Prema Nedungadi, Kai-Yu Tang, Raghu Raman
Sustainability.2024; 16(22): 9779. CrossRef - Is AI the new course creator
Sheri Conklin, Tom Dorgan, Daisyane Barreto
Discover Education.2024;[Epub] CrossRef
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Opportunities, challenges, and future directions of large language models, including ChatGPT in medical education: a systematic scoping review
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Xiaojun Xu
, Yixiao Chen
, Jing Miao
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J Educ Eval Health Prof. 2024;21:6. Published online March 15, 2024
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DOI: https://doi.org/10.3352/jeehp.2024.21.6
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6,055
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Abstract
PDF
Supplementary Material
- Background
ChatGPT is a large language model (LLM) based on artificial intelligence (AI) capable of responding in multiple languages and generating nuanced and highly complex responses. While ChatGPT holds promising applications in medical education, its limitations and potential risks cannot be ignored.
Methods
A scoping review was conducted for English articles discussing ChatGPT in the context of medical education published after 2022. A literature search was performed using PubMed/MEDLINE, Embase, and Web of Science databases, and information was extracted from the relevant studies that were ultimately included.
Results
ChatGPT exhibits various potential applications in medical education, such as providing personalized learning plans and materials, creating clinical practice simulation scenarios, and assisting in writing articles. However, challenges associated with academic integrity, data accuracy, and potential harm to learning were also highlighted in the literature. The paper emphasizes certain recommendations for using ChatGPT, including the establishment of guidelines. Based on the review, 3 key research areas were proposed: cultivating the ability of medical students to use ChatGPT correctly, integrating ChatGPT into teaching activities and processes, and proposing standards for the use of AI by medical students.
Conclusion
ChatGPT has the potential to transform medical education, but careful consideration is required for its full integration. To harness the full potential of ChatGPT in medical education, attention should not only be given to the capabilities of AI but also to its impact on students and teachers.
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- AI-assisted patient education: Challenges and solutions in pediatric kidney transplantation
MZ Ihsan, Dony Apriatama, Pithriani, Riza Amalia
Patient Education and Counseling.2025; 131: 108575. CrossRef - Exploring predictors of AI chatbot usage intensity among students: Within- and between-person relationships based on the technology acceptance model
Anne-Kathrin Kleine, Insa Schaffernak, Eva Lermer
Computers in Human Behavior: Artificial Humans.2025; 3: 100113. CrossRef - Chatbots in neurology and neuroscience: Interactions with students, patients and neurologists
Stefano Sandrone
Brain Disorders.2024; 15: 100145. CrossRef - ChatGPT in education: unveiling frontiers and future directions through systematic literature review and bibliometric analysis
Buddhini Amarathunga
Asian Education and Development Studies.2024; 13(5): 412. CrossRef - Evaluating the performance of ChatGPT-3.5 and ChatGPT-4 on the Taiwan plastic surgery board examination
Ching-Hua Hsieh, Hsiao-Yun Hsieh, Hui-Ping Lin
Heliyon.2024; 10(14): e34851. CrossRef - Preparing for Artificial General Intelligence (AGI) in Health Professions Education: AMEE Guide No. 172
Ken Masters, Anne Herrmann-Werner, Teresa Festl-Wietek, David Taylor
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Gülcan Gencer, Kerem Gencer
Cureus.2024;[Epub] CrossRef - Research ethics and issues regarding the use of ChatGPT-like artificial intelligence platforms by authors and reviewers: a narrative review
Sang-Jun Kim
Science Editing.2024; 11(2): 96. CrossRef - Innovation Off the Bat: Bridging the ChatGPT Gap in Digital Competence among English as a Foreign Language Teachers
Gulsara Urazbayeva, Raisa Kussainova, Aikumis Aibergen, Assel Kaliyeva, Gulnur Kantayeva
Education Sciences.2024; 14(9): 946. CrossRef - Exploring the perceptions of Chinese pre-service teachers on the integration of generative AI in English language teaching: Benefits, challenges, and educational implications
Ji Young Chung, Seung-Hoon Jeong
Online Journal of Communication and Media Technologies.2024; 14(4): e202457. CrossRef - Unveiling the bright side and dark side of AI-based ChatGPT : a bibliographic and thematic approach
Chandan Kumar Tiwari, Mohd. Abass Bhat, Abel Dula Wedajo, Shagufta Tariq Khan
Journal of Decision Systems.2024; : 1. CrossRef - Artificial Intelligence in Medical Education and Mentoring in Rehabilitation Medicine
Julie K. Silver, Mustafa Reha Dodurgali, Nara Gavini
American Journal of Physical Medicine & Rehabilitation.2024; 103(11): 1039. CrossRef - The Potential of Artificial Intelligence Tools for Reducing Uncertainty in Medicine and Directions for Medical Education
Sauliha Rabia Alli, Soaad Qahhār Hossain, Sunit Das, Ross Upshur
JMIR Medical Education.2024; 10: e51446. CrossRef - A Systematic Literature Review of Empirical Research on Applying Generative Artificial Intelligence in Education
Xin Zhang, Peng Zhang, Yuan Shen, Min Liu, Qiong Wang, Dragan Gašević, Yizhou Fan
Frontiers of Digital Education.2024; 1(3): 223. CrossRef - Artificial intelligence in medical problem-based learning: opportunities and challenges
Yaoxing Chen, Hong Qi, Yu Qiu, Juan Li, Liang Zhu, Xiaoling Gao, Hao Wang, Gan Jiang
Global Medical Education.2024;[Epub] CrossRef
Research article
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Efficacy and limitations of ChatGPT as a biostatistical problem-solving tool in medical education in Serbia: a descriptive study
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Aleksandra Ignjatović
, Lazar Stevanović
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J Educ Eval Health Prof. 2023;20:28. Published online October 16, 2023
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DOI: https://doi.org/10.3352/jeehp.2023.20.28
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4,015
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218
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10
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Abstract
PDF
Supplementary Material
- Purpose
This study aimed to assess the performance of ChatGPT (GPT-3.5 and GPT-4) as a study tool in solving biostatistical problems and to identify any potential drawbacks that might arise from using ChatGPT in medical education, particularly in solving practical biostatistical problems.
Methods
ChatGPT was tested to evaluate its ability to solve biostatistical problems from the Handbook of Medical Statistics by Peacock and Peacock in this descriptive study. Tables from the problems were transformed into textual questions. Ten biostatistical problems were randomly chosen and used as text-based input for conversation with ChatGPT (versions 3.5 and 4).
Results
GPT-3.5 solved 5 practical problems in the first attempt, related to categorical data, cross-sectional study, measuring reliability, probability properties, and the t-test. GPT-3.5 failed to provide correct answers regarding analysis of variance, the chi-square test, and sample size within 3 attempts. GPT-4 also solved a task related to the confidence interval in the first attempt and solved all questions within 3 attempts, with precise guidance and monitoring.
Conclusion
The assessment of both versions of ChatGPT performance in 10 biostatistical problems revealed that GPT-3.5 and 4’s performance was below average, with correct response rates of 5 and 6 out of 10 on the first attempt. GPT-4 succeeded in providing all correct answers within 3 attempts. These findings indicate that students must be aware that this tool, even when providing and calculating different statistical analyses, can be wrong, and they should be aware of ChatGPT’s limitations and be careful when incorporating this model into medical education.
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- From statistics to deep learning: Using large language models in psychiatric research
Yining Hua, Andrew Beam, Lori B. Chibnik, John Torous
International Journal of Methods in Psychiatric Research.2025;[Epub] CrossRef - Assessing the Current Limitations of Large Language Models in Advancing Health Care Education
JaeYong Kim, Bathri Narayan Vajravelu
JMIR Formative Research.2025; 9: e51319. CrossRef - Can Generative AI and ChatGPT Outperform Humans on Cognitive-Demanding Problem-Solving Tasks in Science?
Xiaoming Zhai, Matthew Nyaaba, Wenchao Ma
Science & Education.2024;[Epub] CrossRef - Opportunities, challenges, and future directions of large language models, including ChatGPT in medical education: a systematic scoping review
Xiaojun Xu, Yixiao Chen, Jing Miao
Journal of Educational Evaluation for Health Professions.2024; 21: 6. CrossRef - Comparing the Performance of ChatGPT-4 and Medical Students on MCQs at Varied Levels of Bloom’s Taxonomy
Ambadasu Bharatha, Nkemcho Ojeh, Ahbab Mohammad Fazle Rabbi, Michael Campbell, Kandamaran Krishnamurthy, Rhaheem Layne-Yarde, Alok Kumar, Dale Springer, Kenneth Connell, Md Anwarul Majumder
Advances in Medical Education and Practice.2024; Volume 15: 393. CrossRef - Revolutionizing Cardiology With Words: Unveiling the Impact of Large Language Models in Medical Science Writing
Abhijit Bhattaru, Naveena Yanamala, Partho P. Sengupta
Canadian Journal of Cardiology.2024; 40(10): 1950. CrossRef - ChatGPT in medicine: prospects and challenges: a review article
Songtao Tan, Xin Xin, Di Wu
International Journal of Surgery.2024;[Epub] CrossRef - In-depth analysis of ChatGPT’s performance based on specific signaling words and phrases in the question stem of 2377 USMLE step 1 style questions
Leonard Knoedler, Samuel Knoedler, Cosima C. Hoch, Lukas Prantl, Konstantin Frank, Laura Soiderer, Sebastian Cotofana, Amir H. Dorafshar, Thilo Schenck, Felix Vollbach, Giuseppe Sofo, Michael Alfertshofer
Scientific Reports.2024;[Epub] CrossRef - Evaluating the quality of responses generated by ChatGPT
Danimir Mandić, Gordana Miščević, Ljiljana Bujišić
Metodicka praksa.2024; 27(1): 5. CrossRef - A Comparative Evaluation of Statistical Product and Service Solutions (SPSS) and ChatGPT-4 in Statistical Analyses
Al Imran Shahrul, Alizae Marny F Syed Mohamed
Cureus.2024;[Epub] CrossRef - ChatGPT and Other Large Language Models in Medical Education — Scoping Literature Review
Alexandra Aster, Matthias Carl Laupichler, Tamina Rockwell-Kollmann, Gilda Masala, Ebru Bala, Tobias Raupach
Medical Science Educator.2024;[Epub] CrossRef - Exploring the potential of large language models for integration into an academic statistical consulting service–the EXPOLS study protocol
Urs Alexander Fichtner, Jochen Knaus, Erika Graf, Georg Koch, Jörg Sahlmann, Dominikus Stelzer, Martin Wolkewitz, Harald Binder, Susanne Weber, Bekalu Tadesse Moges
PLOS ONE.2024; 19(12): e0308375. CrossRef
Review
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How to review and assess a systematic review and meta-analysis article: a methodological study (secondary publication)
-
Seung-Kwon Myung
-
J Educ Eval Health Prof. 2023;20:24. Published online August 27, 2023
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DOI: https://doi.org/10.3352/jeehp.2023.20.24
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11,488
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819
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13
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12
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Abstract
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Supplementary Material
- Systematic reviews and meta-analyses have become central in many research fields, particularly medicine. They offer the highest level of evidence in evidence-based medicine and support the development and revision of clinical practice guidelines, which offer recommendations for clinicians caring for patients with specific diseases and conditions. This review summarizes the concepts of systematic reviews and meta-analyses and provides guidance on reviewing and assessing such papers. A systematic review refers to a review of a research question that uses explicit and systematic methods to identify, select, and critically appraise relevant research. In contrast, a meta-analysis is a quantitative statistical analysis that combines individual results on the same research question to estimate the common or mean effect. Conducting a meta-analysis involves defining a research topic, selecting a study design, searching literature in electronic databases, selecting relevant studies, and conducting the analysis. One can assess the findings of a meta-analysis by interpreting a forest plot and a funnel plot and by examining heterogeneity. When reviewing systematic reviews and meta-analyses, several essential points must be considered, including the originality and significance of the work, the comprehensiveness of the database search, the selection of studies based on inclusion and exclusion criteria, subgroup analyses by various factors, and the interpretation of the results based on the levels of evidence. This review will provide readers with helpful guidance to help them read, understand, and evaluate these articles.
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Sustainability.2024; 16(3): 1242. CrossRef - The association between long noncoding RNA ABHD11-AS1 and malignancy prognosis: a meta-analysis
Guangyao Lin, Tao Ye, Jing Wang
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Research articles
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Medical students’ patterns of using ChatGPT as a feedback tool and perceptions of ChatGPT in a Leadership and Communication course in Korea: a cross-sectional study
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Janghee Park
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J Educ Eval Health Prof. 2023;20:29. Published online November 10, 2023
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DOI: https://doi.org/10.3352/jeehp.2023.20.29
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3,319
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6
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8
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Abstract
PDF
Supplementary Material
- Purpose
This study aimed to analyze patterns of using ChatGPT before and after group activities and to explore medical students’ perceptions of ChatGPT as a feedback tool in the classroom.
Methods
The study included 99 2nd-year pre-medical students who participated in a “Leadership and Communication” course from March to June 2023. Students engaged in both individual and group activities related to negotiation strategies. ChatGPT was used to provide feedback on their solutions. A survey was administered to assess students’ perceptions of ChatGPT’s feedback, its use in the classroom, and the strengths and challenges of ChatGPT from May 17 to 19, 2023.
Results
The students responded by indicating that ChatGPT’s feedback was helpful, and revised and resubmitted their group answers in various ways after receiving feedback. The majority of respondents expressed agreement with the use of ChatGPT during class. The most common response concerning the appropriate context of using ChatGPT’s feedback was “after the first round of discussion, for revisions.” There was a significant difference in satisfaction with ChatGPT’s feedback, including correctness, usefulness, and ethics, depending on whether or not ChatGPT was used during class, but there was no significant difference according to gender or whether students had previous experience with ChatGPT. The strongest advantages were “providing answers to questions” and “summarizing information,” and the worst disadvantage was “producing information without supporting evidence.”
Conclusion
The students were aware of the advantages and disadvantages of ChatGPT, and they had a positive attitude toward using ChatGPT in the classroom.
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Citations
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- Opportunities, challenges, and future directions of large language models, including ChatGPT in medical education: a systematic scoping review
Xiaojun Xu, Yixiao Chen, Jing Miao
Journal of Educational Evaluation for Health Professions.2024; 21: 6. CrossRef - Embracing ChatGPT for Medical Education: Exploring Its Impact on Doctors and Medical Students
Yijun Wu, Yue Zheng, Baijie Feng, Yuqi Yang, Kai Kang, Ailin Zhao
JMIR Medical Education.2024; 10: e52483. CrossRef - Integration of ChatGPT Into a Course for Medical Students: Explorative Study on Teaching Scenarios, Students’ Perception, and Applications
Anita V Thomae, Claudia M Witt, Jürgen Barth
JMIR Medical Education.2024; 10: e50545. CrossRef - A cross sectional investigation of ChatGPT-like large language models application among medical students in China
Guixia Pan, Jing Ni
BMC Medical Education.2024;[Epub] CrossRef - A Pilot Study of Medical Student Opinions on Large Language Models
Alan Y Xu, Vincent S Piranio, Skye Speakman, Chelsea D Rosen, Sally Lu, Chris Lamprecht, Robert E Medina, Maisha Corrielus, Ian T Griffin, Corinne E Chatham, Nicolas J Abchee, Daniel Stribling, Phuong B Huynh, Heather Harrell, Benjamin Shickel, Meghan Bre
Cureus.2024;[Epub] CrossRef - The intent of ChatGPT usage and its robustness in medical proficiency exams: a systematic review
Tatiana Chaiban, Zeinab Nahle, Ghaith Assi, Michelle Cherfane
Discover Education.2024;[Epub] CrossRef - ChatGPT and Clinical Training: Perception, Concerns, and Practice of Pharm-D Students
Mohammed Zawiah, Fahmi Al-Ashwal, Lobna Gharaibeh, Rana Abu Farha, Karem Alzoubi, Khawla Abu Hammour, Qutaiba A Qasim, Fahd Abrah
Journal of Multidisciplinary Healthcare.2023; Volume 16: 4099. CrossRef - Information amount, accuracy, and relevance of generative artificial intelligence platforms’ answers regarding learning objectives of medical arthropodology evaluated in English and Korean queries in December 2023: a descriptive study
Hyunju Lee, Soobin Park
Journal of Educational Evaluation for Health Professions.2023; 20: 39. CrossRef
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ChatGPT (GPT-4) passed the Japanese National License Examination for Pharmacists in 2022, answering all items including those with diagrams: a descriptive study
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Hiroyasu Sato
, Katsuhiko Ogasawara
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J Educ Eval Health Prof. 2024;21:4. Published online February 28, 2024
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DOI: https://doi.org/10.3352/jeehp.2024.21.4
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2,637
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285
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4
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Abstract
PDF
Supplementary Material
- Purpose
The objective of this study was to assess the performance of ChatGPT (GPT-4) on all items, including those with diagrams, in the Japanese National License Examination for Pharmacists (JNLEP) and compare it with the previous GPT-3.5 model’s performance.
Methods
The 107th JNLEP, conducted in 2022, with 344 items input into the GPT-4 model, was targeted for this study. Separately, 284 items, excluding those with diagrams, were entered into the GPT-3.5 model. The answers were categorized and analyzed to determine accuracy rates based on categories, subjects, and presence or absence of diagrams. The accuracy rates were compared to the main passing criteria (overall accuracy rate ≥62.9%).
Results
The overall accuracy rate for all items in the 107th JNLEP in GPT-4 was 72.5%, successfully meeting all the passing criteria. For the set of items without diagrams, the accuracy rate was 80.0%, which was significantly higher than that of the GPT-3.5 model (43.5%). The GPT-4 model demonstrated an accuracy rate of 36.1% for items that included diagrams.
Conclusion
Advancements that allow GPT-4 to process images have made it possible for LLMs to answer all items in medical-related license examinations. This study’s findings confirm that ChatGPT (GPT-4) possesses sufficient knowledge to meet the passing criteria.
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- Qwen-2.5 Outperforms Other Large Language Models in the Chinese National Nursing Licensing Examination: Retrospective Cross-Sectional Comparative Study
Shiben Zhu, Wanqin Hu, Zhi Yang, Jiani Yan, Fang Zhang
JMIR Medical Informatics.2025; 13: e63731. CrossRef - ChatGPT (GPT-4V) Performance on the Healthcare Information Technologist Examination in Japan
Kai Ishida, Eisuke Hanada
Cureus.2025;[Epub] CrossRef - Potential of ChatGPT to Pass the Japanese Medical and Healthcare Professional National Licenses: A Literature Review
Kai Ishida, Eisuke Hanada
Cureus.2024;[Epub] CrossRef - Performance of Generative Pre-trained Transformer (GPT)-4 and Gemini Advanced on the First-Class Radiation Protection Supervisor Examination in Japan
Hiroki Goto, Yoshioki Shiraishi, Seiji Okada
Cureus.2024;[Epub] CrossRef - Performance of ChatGPT‐3.5 and ChatGPT‐4o in the Japanese National Dental Examination
Osamu Uehara, Tetsuro Morikawa, Fumiya Harada, Nodoka Sugiyama, Yuko Matsuki, Daichi Hiraki, Hinako Sakurai, Takashi Kado, Koki Yoshida, Yukie Murata, Hirofumi Matsuoka, Toshiyuki Nagasawa, Yasushi Furuichi, Yoshihiro Abiko, Hiroko Miura
Journal of Dental Education.2024;[Epub] CrossRef - An exploratory assessment of GPT-4o and GPT-4 performance on the Japanese National Dental Examination
Masaki Morishita, Hikaru Fukuda, Shino Yamaguchi, Kosuke Muraoka, Taiji Nakamura, Masanari Hayashi, Izumi Yoshioka, Kentaro Ono, Shuji Awano
The Saudi Dental Journal.2024; 36(12): 1577. CrossRef - Evaluating the Accuracy of ChatGPT in the Japanese Board-Certified Physiatrist Examination
Yuki Kato, Kenta Ushida, Ryo Momosaki
Cureus.2024;[Epub] CrossRef
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Improvement of the clinical skills of nurse anesthesia students using mini-clinical evaluation exercises in Iran: a randomized controlled study
-
Ali Khalafi
, Yasamin Sharbatdar
, Nasrin Khajeali
, Mohammad Hosein Haghighizadeh
, Mahshid Vaziri
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J Educ Eval Health Prof. 2023;20:12. Published online April 6, 2023
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DOI: https://doi.org/10.3352/jeehp.2023.20.12
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3,528
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137
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Abstract
PDF
Supplementary Material
- Purpose
The present study aimed to investigate the effect of a mini-clinical evaluation exercise (CEX) assessment on improving the clinical skills of nurse anesthesia students at Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
Methods
This study started on November 1, 2022, and ended on December 1, 2022. It was conducted among 50 nurse anesthesia students divided into intervention and control groups. The intervention group’s clinical skills were evaluated 4 times using the mini-CEX method. In contrast, the same skills were evaluated in the control group based on the conventional method—that is, general supervision by the instructor during the internship and a summative evaluation based on a checklist at the end of the course. The intervention group students also filled out a questionnaire to measure their satisfaction with the mini-CEX method.
Results
The mean score of the students in both the control and intervention groups increased significantly on the post-test (P<0.0001), but the improvement in the scores of the intervention group was significantly greater compared with the control group (P<0.0001). The overall mean score for satisfaction in the intervention group was 76.3 out of a maximum of 95.
Conclusion
The findings of this study showed that using mini-CEX as a formative evaluation method to evaluate clinical skills had a significant effect on the improvement of nurse anesthesia students’ clinical skills, and they had a very favorable opinion about this evaluation method.
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Citations
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- Exploring the clinical practice training program for Master of Nursing Specialist in anesthesia: A qualitative study
Huihui Hu, Yanxin Gu, Yi Yang, Rui Gao, Peishuang Wang, Fang Zhou
Nurse Education Today.2025; 147: 106577. CrossRef - Psychometric testing of anesthesia nursing competence scale (AnestComp)
Samira Mahmoudi, Akram Yazdani, Fatemeh Hasanshiri
Perioperative Care and Operating Room Management.2024; 34: 100368. CrossRef - Application of flipped classroom teaching method based on ADDIE concept in clinical teaching for neurology residents
Juan Zhang, Hong Chen, Xie Wang, Xiaofeng Huang, Daojun Xie
BMC Medical Education.2024;[Epub] CrossRef - Impactos do Mini-Cex no ensino-aprendizagem da saúde: uma revisão integrativa
João Henrique Anizio de Farias, Draenne Micarla dos Santos Silva, Clédson Calixto de Oliveira, Elzenir Pereira de Oliveira Almeida
Revista de Gestão e Secretariado.2024; 15(9): e4150. CrossRef - Comparing Satisfaction of Undergraduate Nursing Students`: Mini-CEX vs CIM in Assessing Clinical Competence
Somia Saghir, Anny Ashiq Ali, Kashif Khan, Uzma Bibi, Shafaat Ullah, Rafi Ullah, Zaifullah Khan, Tahir Khan
Pakistan Journal of Health Sciences.2023; : 134. CrossRef - Enhancement of the technical and non-technical skills of nurse anesthesia students using the Anesthetic List Management Assessment Tool in Iran: a quasi-experimental study
Ali Khalafi, Maedeh Kordnejad, Vahid Saidkhani
Journal of Educational Evaluation for Health Professions.2023; 20: 19. CrossRef
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Priorities in updating training paradigms in orthopedic manual therapy: an international Delphi study
-
Damian Keter
, David Griswold
, Kenneth Learman
, Chad Cook
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J Educ Eval Health Prof. 2023;20:4. Published online January 27, 2023
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DOI: https://doi.org/10.3352/jeehp.2023.20.4
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4,238
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4
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6
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Abstract
PDF
Supplementary Material
- Purpose
Orthopedic manual therapy (OMT) education demonstrates significant variability between philosophies and while literature has offered a more comprehensive understanding of the contextual, patient specific, and technique factors which interact to influence outcome, most OMT training paradigms continue to emphasize the mechanical basis for OMT application. The purpose of this study was to establish consensus on modifications & adaptions to training paradigms which need to occur within OMT education to align with current evidence.
Methods
A 3-round Delphi survey instrument designed to identify foundational knowledge to include and omit from OMT education was completed by 28 educators working within high level manual therapy education programs internationally. Round 1 consisted of open-ended questions to identify content in each area. Round 2 and Round 3 allowed participants to rank the themes identified in Round 1.
Results
Consensus was reached on 25 content areas to include within OMT education, 1 content area to omit from OMT education, and 34 knowledge components which should be present in those providing OMT. Support was seen for education promoting understanding the complex psychological, neurophysiological, and biomechanical systems as they relate to both evaluation and treatment effect. While some concepts were more consistently supported there was significant variability in responses which is largely expected to be related to previous training.
Conclusion
The results of this study indicate manual therapy educators understanding of evidence-based practice as support for all 3 tiers of evidence were represented. The results of this study should guide OMT training program development and modification.
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- A critical review of the role of manual therapy in the treatment of individuals with low back pain
Jean-Pascal Grenier, Maria Rothmund
Journal of Manual & Manipulative Therapy.2024; 32(5): 464. CrossRef - Development of a basic evaluation model for manual therapy learning in rehabilitation students based on the Delphi method
Wang Ziyi, Zhou Supo, Marcin Białas
BMC Medical Education.2024;[Epub] CrossRef - Delphi Studie zur Modernisierung der Ausbildung für Orthopädische Manuelle Therapie
MSK – Muskuloskelettale Physiotherapie.2024; 28(04): 204. CrossRef - Patient Factors Associated With Treatment Effect of Manual Therapy: A Scoping Review
Damian Keter, David Griswold, Kenneth Learman, Chad E. Cook
JOSPT Open.2024; 2(2): 82. CrossRef - Integrating Person-Centered Concepts and Modern Manual Therapy
Damian Keter, Nathan Hutting, Rebecca Vogsland, Chad E Cook
JOSPT Open.2024; 2(1): 60. CrossRef - Modernizing patient-centered manual therapy: Findings from a Delphi study on orthopaedic manual therapy application
Damian Keter, David Griswold, Kenneth Learman, Chad Cook
Musculoskeletal Science and Practice.2023; 65: 102777. CrossRef
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Mentorship and self-efficacy are associated with lower burnout in physical therapists in the United States: a cross-sectional survey study
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Matthew Pugliese
, Jean-Michel Brismée
, Brad Allen
, Sean Riley
, Justin Tammany
, Paul Mintken
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J Educ Eval Health Prof. 2023;20:27. Published online September 27, 2023
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DOI: https://doi.org/10.3352/jeehp.2023.20.27
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5,915
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432
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4
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5
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Abstract
PDF
Supplementary Material
- Purpose
This study investigated the prevalence of burnout in physical therapists in the United States and the relationships between burnout and education, mentorship, and self-efficacy.
Methods
This was a cross-sectional survey study. An electronic survey was distributed to practicing physical therapists across the United States over a 6-week period from December 2020 to January 2021. The survey was completed by 2,813 physical therapists from all states. The majority were female (68.72%), White or Caucasian (80.13%), and employed full-time (77.14%). Respondents completed questions on demographics, education, mentorship, self-efficacy, and burnout. The Burnout Clinical Subtypes Questionnaire 12 (BCSQ-12) and self-reports were used to quantify burnout, and the General Self-Efficacy Scale (GSES) was used to measure self-efficacy. Descriptive and inferential analyses were performed.
Results
Respondents from home health (median BCSQ-12=42.00) and skilled nursing facility settings (median BCSQ-12=42.00) displayed the highest burnout scores. Burnout was significantly lower among those who provided formal mentorship (median BCSQ-12=39.00, P=0.0001) compared to no mentorship (median BCSQ-12=41.00). Respondents who received formal mentorship (median BCSQ-12=38.00, P=0.0028) displayed significantly lower burnout than those who received no mentorship (median BCSQ-12=41.00). A moderate negative correlation (rho=-0.49) was observed between the GSES and burnout scores. A strong positive correlation was found between self-reported burnout status and burnout scores (rrb=0.61).
Conclusion
Burnout is prevalent in the physical therapy profession, as almost half of respondents (49.34%) reported burnout. Providing or receiving mentorship and higher self-efficacy were associated with lower burnout. Organizations should consider measuring burnout levels, investing in mentorship programs, and implementing strategies to improve self-efficacy.
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- Wellness and Stress Management Practices Among Healthcare Professionals and Health Professional Students
Asli C. Yalim, Katherine Daly, Monica Bailey, Denise Kay, Xiang Zhu, Mohammed Patel, Laurie C. Neely, Desiree A. Díaz, Denyi M. Canario Asencio, Karla Rosario, Melissa Cowan, Magdalena Pasarica
American Journal of Health Promotion.2025; 39(2): 204. CrossRef - Final results of the National Oncology Mentorship Program 2023 and its impact on burnout and professional fulfilment
Udit Nindra, Gowri Shivasabesan, Rhiannon Mellor, Weng Ng, Wei Chua, Deme Karikios, Bethan Richards, Jia Liu
Internal Medicine Journal.2025; 55(2): 233. CrossRef - Incidence of Shared Clinical Instruction in Physical Therapy Clinical Education in the United States
Nicki Silberman, Lori Hochman, Jaya Rachwani
Journal of Physical Therapy Education.2025;[Epub] CrossRef - Interprofessional education to support alcohol use screening and future team-based management of stress-related disorders in vulnerable populations
Taylor Fitzpatrick-Schmidt, Scott Edwards
Frontiers in Education.2024;[Epub] CrossRef - Prevalence of Stress and Burnout in Physical Therapist Clinical Instructors
Ryan J. Pontiff, Peggy Gleeson, Katy Mitchell, Rupal M. Patel
Journal of Physical Therapy Education.2024;[Epub] CrossRef
Brief report
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Comparing ChatGPT’s ability to rate the degree of stereotypes and the consistency of stereotype attribution with those of medical students in New Zealand in developing a similarity rating test: a methodological study
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Chao-Cheng Lin
, Zaine Akuhata-Huntington
, Che-Wei Hsu
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J Educ Eval Health Prof. 2023;20:17. Published online June 12, 2023
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DOI: https://doi.org/10.3352/jeehp.2023.20.17
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3,176
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3
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4
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Abstract
PDF
Supplementary Material
- Learning about one’s implicit bias is crucial for improving one’s cultural competency and thereby reducing health inequity. To evaluate bias among medical students following a previously developed cultural training program targeting New Zealand Māori, we developed a text-based, self-evaluation tool called the Similarity Rating Test (SRT). The development process of the SRT was resource-intensive, limiting its generalizability and applicability. Here, we explored the potential of ChatGPT, an automated chatbot, to assist in the development process of the SRT by comparing ChatGPT’s and students’ evaluations of the SRT. Despite results showing non-significant equivalence and difference between ChatGPT’s and students’ ratings, ChatGPT’s ratings were more consistent than students’ ratings. The consistency rate was higher for non-stereotypical than for stereotypical statements, regardless of rater type. Further studies are warranted to validate ChatGPT’s potential for assisting in SRT development for implementation in medical education and evaluation of ethnic stereotypes and related topics.
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Citations
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- The Performance of ChatGPT on Short-answer Questions in a Psychiatry Examination: A Pilot Study
Chao-Cheng Lin, Kobus du Plooy, Andrew Gray, Deirdre Brown, Linda Hobbs, Tess Patterson, Valerie Tan, Daniel Fridberg, Che-Wei Hsu
Taiwanese Journal of Psychiatry.2024; 38(2): 94. CrossRef - ChatGPT and Other Large Language Models in Medical Education — Scoping Literature Review
Alexandra Aster, Matthias Carl Laupichler, Tamina Rockwell-Kollmann, Gilda Masala, Ebru Bala, Tobias Raupach
Medical Science Educator.2024;[Epub] CrossRef - Psychiatric Care, Training and Research in Aotearoa New Zealand
Chao-Cheng (Chris) Lin, Charlotte Mentzel, Maria Luz C. Querubin
Taiwanese Journal of Psychiatry.2024; 38(4): 161. CrossRef - Efficacy and limitations of ChatGPT as a biostatistical problem-solving tool in medical education in Serbia: a descriptive study
Aleksandra Ignjatović, Lazar Stevanović
Journal of Educational Evaluation for Health Professions.2023; 20: 28. CrossRef
Research article
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Performance of GPT-3.5 and GPT-4 on standardized urology knowledge assessment items in the United States: a descriptive study
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Max Samuel Yudovich
, Elizaveta Makarova
, Christian Michael Hague
, Jay Dilip Raman
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J Educ Eval Health Prof. 2024;21:17. Published online July 8, 2024
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DOI: https://doi.org/10.3352/jeehp.2024.21.17
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2,079
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312
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2
Web of Science
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3
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Abstract
PDF
Supplementary Material
- Purpose
This study aimed to evaluate the performance of Chat Generative Pre-Trained Transformer (ChatGPT) with respect to standardized urology multiple-choice items in the United States.
Methods
In total, 700 multiple-choice urology board exam-style items were submitted to GPT-3.5 and GPT-4, and responses were recorded. Items were categorized based on topic and question complexity (recall, interpretation, and problem-solving). The accuracy of GPT-3.5 and GPT-4 was compared across item types in February 2024.
Results
GPT-4 answered 44.4% of items correctly compared to 30.9% for GPT-3.5 (P<0.00001). GPT-4 (vs. GPT-3.5) had higher accuracy with urologic oncology (43.8% vs. 33.9%, P=0.03), sexual medicine (44.3% vs. 27.8%, P=0.046), and pediatric urology (47.1% vs. 27.1%, P=0.012) items. Endourology (38.0% vs. 25.7%, P=0.15), reconstruction and trauma (29.0% vs. 21.0%, P=0.41), and neurourology (49.0% vs. 33.3%, P=0.11) items did not show significant differences in performance across versions. GPT-4 also outperformed GPT-3.5 with respect to recall (45.9% vs. 27.4%, P<0.00001), interpretation (45.6% vs. 31.5%, P=0.0005), and problem-solving (41.8% vs. 34.5%, P=0.56) type items. This difference was not significant for the higher-complexity items.
Conclusions
ChatGPT performs relatively poorly on standardized multiple-choice urology board exam-style items, with GPT-4 outperforming GPT-3.5. The accuracy was below the proposed minimum passing standards for the American Board of Urology’s Continuing Urologic Certification knowledge reinforcement activity (60%). As artificial intelligence progresses in complexity, ChatGPT may become more capable and accurate with respect to board examination items. For now, its responses should be scrutinized.
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Citations
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- Evaluating the Performance of ChatGPT4.0 Versus ChatGPT3.5 on the Hand Surgery Self-Assessment Exam: A Comparative Analysis of Performance on Image-Based Questions
Kiera L Vrindten, Megan Hsu, Yuri Han, Brian Rust, Heili Truumees, Brian M Katt
Cureus.2025;[Epub] CrossRef - From GPT-3.5 to GPT-4.o: A Leap in AI’s Medical Exam Performance
Markus Kipp
Information.2024; 15(9): 543. CrossRef - Artificial Intelligence can Facilitate Application of Risk Stratification Algorithms to Bladder Cancer Patient Case Scenarios
Max S Yudovich, Ahmad N Alzubaidi, Jay D Raman
Clinical Medicine Insights: Oncology.2024;[Epub] CrossRef