Journal of Educational Evaluation for Health Professions

Educational/Faculty development material

The role of large language models in the peer-review process: opportunities and challenges for medical journal reviewers and editors: Jisoo Lee, Jieun Lee, Jeong-Ju Yoo; J Educ Eval Health Prof. 2025;22:4. Published online January 16, 2025; DOI: https://doi.org/10.3352/jeehp.2025.22.4 [Epub ahead of print]

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Abstract PDF: The peer review process ensures the integrity of scientific research. This is particularly important in the medical field, where research findings directly impact patient care. However, the rapid growth of publications has strained reviewers, causing delays and potential declines in quality. Generative artificial intelligence, especially large language models (LLMs) such as ChatGPT, may assist researchers with efficient, high-quality reviews. This review explores the integration of LLMs into peer review, highlighting their strengths in linguistic tasks and challenges in assessing scientific validity, particularly in clinical medicine. Key points for integration include initial screening, reviewer matching, feedback support, and language review. However, implementing LLMs for these purposes will necessitate addressing biases, privacy concerns, and data confidentiality. We recommend using LLMs as complementary tools under clear guidelines to support, not replace, human expertise in maintaining rigorous peer review standards.

Research article

Effectiveness of ChatGPT-4o in developing continuing professional development plans for graduate radiographers: a descriptive study: Minh Chau, Elio Stefan Arruzza, Kelly Spuur; J Educ Eval Health Prof. 2024;21:34. Published online November 18, 2024; DOI: https://doi.org/10.3352/jeehp.2024.21.34

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Purpose
This study evaluates the use of ChatGPT-4o in creating tailored continuing professional development (CPD) plans for radiography students, addressing the challenge of aligning CPD with Medical Radiation Practice Board of Australia (MRPBA) requirements. We hypothesized that ChatGPT-4o could support students in CPD planning while meeting regulatory standards.
Methods
A descriptive, experimental design was used to generate 3 unique CPD plans using ChatGPT-4o, each tailored to hypothetical graduate radiographers in varied clinical settings. Each plan followed MRPBA guidelines, focusing on computed tomography specialization by the second year. Three MRPBA-registered academics assessed the plans using criteria of appropriateness, timeliness, relevance, reflection, and completeness from October 2024 to November 2024. Ratings underwent analysis using the Friedman test and intraclass correlation coefficient (ICC) to measure consistency among evaluators.
Results
ChatGPT-4o generated CPD plans generally adhered to regulatory standards across scenarios. The Friedman test indicated no significant differences among raters (P=0.420, 0.761, and 0.807 for each scenario), suggesting consistent scores within scenarios. However, ICC values were low (–0.96, 0.41, and 0.058 for scenarios 1, 2, and 3), revealing variability among raters, particularly in timeliness and completeness criteria, suggesting limitations in the ChatGPT-4o’s ability to address individualized and context-specific needs.
Conclusion
ChatGPT-4o demonstrates the potential to ease the cognitive demands of CPD planning, offering structured support in CPD development. However, human oversight remains essential to ensure plans are contextually relevant and deeply reflective. Future research should focus on enhancing artificial intelligence’s personalization for CPD evaluation, highlighting ChatGPT-4o’s potential and limitations as a tool in professional education.

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Halted medical education and medical residents’ training in Korea, journal metrics, and appreciation to reviewers and volunteers
Sun Huh
Journal of Educational Evaluation for Health Professions.2025; 22: 1. CrossRef

Educational/Faculty development material

The performance of ChatGPT-4.0o in medical imaging evaluation: a cross-sectional study: Elio Stefan Arruzza, Carla Marie Evangelista, Minh Chau; J Educ Eval Health Prof. 2024;21:29. Published online October 31, 2024; DOI: https://doi.org/10.3352/jeehp.2024.21.29

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Abstract PDF Supplementary Material

This study investigated the performance of ChatGPT-4.0o in evaluating the quality of positioning in radiographic images. Thirty radiographs depicting a variety of knee, elbow, ankle, hand, pelvis, and shoulder projections were produced using anthropomorphic phantoms and uploaded to ChatGPT-4.0o. The model was prompted to provide a solution to identify any positioning errors with justification and offer improvements. A panel of radiographers assessed the solutions for radiographic quality based on established positioning criteria, with a grading scale of 1–5. In only 20% of projections, ChatGPT-4.0o correctly recognized all errors with justifications and offered correct suggestions for improvement. The most commonly occurring score was 3 (9 cases, 30%), wherein the model recognized at least 1 specific error and provided a correct improvement. The mean score was 2.9. Overall, low accuracy was demonstrated, with most projections receiving only partially correct solutions. The findings reinforce the importance of robust radiography education and clinical experience.

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Conversational LLM Chatbot ChatGPT-4 for Colonoscopy Boston Bowel Preparation Scoring: An Artificial Intelligence-to-Head Concordance Analysis
Raffaele Pellegrino, Alessandro Federico, Antonietta Gerarda Gravina
Diagnostics.2024; 14(22): 2537. CrossRef
Effectiveness of ChatGPT-4o in developing continuing professional development plans for graduate radiographers: a descriptive study
Minh Chau, Elio Stefan Arruzza, Kelly Spuur
Journal of Educational Evaluation for Health Professions.2024; 21: 34. CrossRef

Research articles

GPT-4o’s competency in answering the simulated written European Board of Interventional Radiology exam compared to a medical student and experts in Germany and its ability to generate exam items on interventional radiology: a descriptive study: Sebastian Ebel, Constantin Ehrengut, Timm Denecke, Holger Gößmann, Anne Bettina Beeskow; J Educ Eval Health Prof. 2024;21:21. Published online August 20, 2024; DOI: https://doi.org/10.3352/jeehp.2024.21.21

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Purpose
This study aimed to determine whether ChatGPT-4o, a generative artificial intelligence (AI) platform, was able to pass a simulated written European Board of Interventional Radiology (EBIR) exam and whether GPT-4o can be used to train medical students and interventional radiologists of different levels of expertise by generating exam items on interventional radiology.
Methods
GPT-4o was asked to answer 370 simulated exam items of the Cardiovascular and Interventional Radiology Society of Europe (CIRSE) for EBIR preparation (CIRSE Prep). Subsequently, GPT-4o was requested to generate exam items on interventional radiology topics at levels of difficulty suitable for medical students and the EBIR exam. Those generated items were answered by 4 participants, including a medical student, a resident, a consultant, and an EBIR holder. The correctly answered items were counted. One investigator checked the answers and items generated by GPT-4o for correctness and relevance. This work was done from April to July 2024.
Results
GPT-4o correctly answered 248 of the 370 CIRSE Prep items (67.0%). For 50 CIRSE Prep items, the medical student answered 46.0%, the resident 42.0%, the consultant 50.0%, and the EBIR holder 74.0% correctly. All participants answered 82.0% to 92.0% of the 50 GPT-4o generated items at the student level correctly. For the 50 GPT-4o items at the EBIR level, the medical student answered 32.0%, the resident 44.0%, the consultant 48.0%, and the EBIR holder 66.0% correctly. All participants could pass the GPT-4o-generated items for the student level; while the EBIR holder could pass the GPT-4o-generated items for the EBIR level. Two items (0.3%) out of 150 generated by the GPT-4o were assessed as implausible.
Conclusion
GPT-4o could pass the simulated written EBIR exam and create exam items of varying difficulty to train medical students and interventional radiologists.

Citations

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Evaluating the performance of ChatGPT in patient consultation and image-based preliminary diagnosis in thyroid eye disease
Yue Wang, Shuo Yang, Chengcheng Zeng, Yingwei Xie, Ya Shen, Jian Li, Xiao Huang, Ruili Wei, Yuqing Chen
Frontiers in Medicine.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
Performance of ChatGPT and Bard on the medical licensing examinations varies across different cultures: a comparison study
Yikai Chen, Xiujie Huang, Fangjie Yang, Haiming Lin, Haoyu Lin, Zhuoqun Zheng, Qifeng Liang, Jinhai Zhang, Xinxin Li
BMC Medical Education.2024;[Epub] CrossRef

Performance of GPT-3.5 and GPT-4 on standardized urology knowledge assessment items in the United States: a descriptive study: Max Samuel Yudovich, Elizaveta Makarova, Christian Michael Hague, Jay Dilip Raman; J Educ Eval Health Prof. 2024;21:17. Published online July 8, 2024; DOI: https://doi.org/10.3352/jeehp.2024.21.17

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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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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
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Markus Kipp
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Artificial Intelligence can Facilitate Application of Risk Stratification Algorithms to Bladder Cancer Patient Case Scenarios
Max S Yudovich, Ahmad N Alzubaidi, Jay D Raman
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Review

Opportunities, challenges, and future directions of large language models, including ChatGPT in medical education: a systematic scoping review: Xiaojun Xu, Yixiao Chen, Jing Miao; J Educ Eval Health Prof. 2024;21:6. Published online March 15, 2024; DOI: https://doi.org/10.3352/jeehp.2024.21.6

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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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Research articles

ChatGPT (GPT-4) passed the Japanese National License Examination for Pharmacists in 2022, answering all items including those with diagrams: a descriptive study: Hiroyasu Sato, Katsuhiko Ogasawara; J Educ Eval Health Prof. 2024;21:4. Published online February 28, 2024; DOI: https://doi.org/10.3352/jeehp.2024.21.4

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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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ChatGPT (GPT-4V) Performance on the Healthcare Information Technologist Examination in Japan
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Medication counseling for OTC drugs using customized ChatGPT-4: Comparison with ChatGPT-3.5 and ChatGPT-4o
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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; J Educ Eval Health Prof. 2023;20:39. Published online December 28, 2023; DOI: https://doi.org/10.3352/jeehp.2023.20.39

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Abstract PDF Supplementary Material

Purpose
This study assessed the performance of 6 generative artificial intelligence (AI) platforms on the learning objectives of medical arthropodology in a parasitology class in Korea. We examined the AI platforms’ performance by querying in Korean and English to determine their information amount, accuracy, and relevance in prompts in both languages.
Methods
From December 15 to 17, 2023, 6 generative AI platforms—Bard, Bing, Claude, Clova X, GPT-4, and Wrtn—were tested on 7 medical arthropodology learning objectives in English and Korean. Clova X and Wrtn are platforms from Korean companies. Responses were evaluated using specific criteria for the English and Korean queries.
Results
Bard had abundant information but was fourth in accuracy and relevance. GPT-4, with high information content, ranked first in accuracy and relevance. Clova X was 4th in amount but 2nd in accuracy and relevance. Bing provided less information, with moderate accuracy and relevance. Wrtn’s answers were short, with average accuracy and relevance. Claude AI had reasonable information, but lower accuracy and relevance. The responses in English were superior in all aspects. Clova X was notably optimized for Korean, leading in relevance.
Conclusion
In a study of 6 generative AI platforms applied to medical arthropodology, GPT-4 excelled overall, while Clova X, a Korea-based AI product, achieved 100% relevance in Korean queries, the highest among its peers. Utilizing these AI platforms in classrooms improved the authors’ self-efficacy and interest in the subject, offering a positive experience of interacting with generative AI platforms to question and receive information.

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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
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Review

Application of artificial intelligence chatbots, including ChatGPT, in education, scholarly work, programming, and content generation and its prospects: a narrative review: Tae Won Kim; J Educ Eval Health Prof. 2023;20:38. Published online December 27, 2023; DOI: https://doi.org/10.3352/jeehp.2023.20.38

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Abstract PDF 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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Emergency Medicine Assistants in the Field of Toxicology, Comparison of ChatGPT-3.5 and GEMINI Artificial Intelligence Systems
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Brief report

ChatGPT (GPT-3.5) as an assistant tool in microbial pathogenesis studies in Sweden: a cross-sectional comparative study: Catharina Hultgren, Annica Lindkvist, Volkan Özenci, Sophie Curbo; J Educ Eval Health Prof. 2023;20:32. Published online November 22, 2023; DOI: https://doi.org/10.3352/jeehp.2023.20.32

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Abstract PDF Supplementary Material

ChatGPT (GPT-3.5) has entered higher education and there is a need to determine how to use it effectively. This descriptive study compared the ability of GPT-3.5 and teachers to answer questions from dental students and construct detailed intended learning outcomes. When analyzed according to a Likert scale, we found that GPT-3.5 answered the questions from dental students in a similar or even more elaborate way compared to the answers that had previously been provided by a teacher. GPT-3.5 was also asked to construct detailed intended learning outcomes for a course in microbial pathogenesis, and when these were analyzed according to a Likert scale they were, to a large degree, found irrelevant. Since students are using GPT-3.5, it is important that instructors learn how to make the best use of it both to be able to advise students and to benefit from its potential.

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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
Unlocking learning: exploring take-home examinations and viva voce examinations in microbiology education for biomedical laboratory science students
Sophie Curbo, Annica Lindkvist, Catharina Hultgren, Jorge Cervantes
Journal of Microbiology & Biology Education.2024;[Epub] 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

Research articles

Performance of ChatGPT, Bard, Claude, and Bing on the Peruvian National Licensing Medical Examination: a cross-sectional study: 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; J Educ Eval Health Prof. 2023;20:30. Published online November 20, 2023; DOI: https://doi.org/10.3352/jeehp.2023.20.30

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Abstract PDF 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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Lucija Gosak, Gregor Štiglic, Lisiane Pruinelli, Dominika Vrbnjak
Journal of Nursing Scholarship.2025; 57(1): 5. CrossRef
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Capable exam-taker and question-generator: the dual role of generative AI in medical education assessment
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Comparison of artificial intelligence systems in answering prosthodontics questions from the dental specialty exam in Turkey
Busra Tosun, Zeynep Sen Yilmaz
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Benchmarking LLM chatbots’ oncological knowledge with the Turkish Society of Medical Oncology’s annual board examination questions
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Performance of GPT-4V in Answering the Japanese Otolaryngology Board Certification Examination Questions: Evaluation Study
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JMIR Medical Education.2024; 10: e57054. CrossRef
Response to Letter to the Editor re: “Artificial Intelligence Versus Expert Plastic Surgeon: Comparative Study Shows ChatGPT ‘Wins' Rhinoplasty Consultations: Should We Be Worried? [1]” by Durairaj et al
Kay Durairaj, Omer Baker
Facial Plastic Surgery & Aesthetic Medicine.2024; 26(3): 276. 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
Performance of ChatGPT Across Different Versions in Medical Licensing Examinations Worldwide: Systematic Review and Meta-Analysis
Mingxin Liu, Tsuyoshi Okuhara, XinYi Chang, Ritsuko Shirabe, Yuriko Nishiie, Hiroko Okada, Takahiro Kiuchi
Journal of Medical Internet Research.2024; 26: e60807. CrossRef
Comparative accuracy of ChatGPT-4, Microsoft Copilot and Google Gemini in the Italian entrance test for healthcare sciences degrees: a cross-sectional study
Giacomo Rossettini, Lia Rodeghiero, Federica Corradi, Chad Cook, Paolo Pillastrini, Andrea Turolla, Greta Castellini, Stefania Chiappinotto, Silvia Gianola, Alvisa Palese
BMC Medical Education.2024;[Epub] CrossRef
Evaluating the competency of ChatGPT in MRCP Part 1 and a systematic literature review of its capabilities in postgraduate medical assessments
Oliver Vij, Henry Calver, Nikki Myall, Mrinalini Dey, Koushan Kouranloo, Thiago P. Fernandes
PLOS ONE.2024; 19(7): e0307372. CrossRef
Large Language Models in Pediatric Education: Current Uses and Future Potential
Srinivasan Suresh, Sanghamitra M. Misra
Pediatrics.2024;[Epub] CrossRef
Comparison of the Performance of ChatGPT, Claude and Bard in Support of Myopia Prevention and Control
Yan Wang, Lihua Liang, Ran Li, Yihua Wang, Changfu Hao
Journal of Multidisciplinary Healthcare.2024; Volume 17: 3917. CrossRef
Evaluating Large Language Models in Dental Anesthesiology: A Comparative Analysis of ChatGPT-4, Claude 3 Opus, and Gemini 1.0 on the Japanese Dental Society of Anesthesiology Board Certification Exam
Misaki Fujimoto, Hidetaka Kuroda, Tomomi Katayama, Atsuki Yamaguchi, Norika Katagiri, Keita Kagawa, Shota Tsukimoto, Akito Nakano, Uno Imaizumi, Aiji Sato-Boku, Naotaka Kishimoto, Tomoki Itamiya, Kanta Kido, Takuro Sanuki
Cureus.2024;[Epub] CrossRef
Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology
Ka Siu Fan, Ka Hay Fan
Dermato.2024; 4(4): 124. 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
Performance of ChatGPT and Bard on the medical licensing examinations varies across different cultures: a comparison study
Yikai Chen, Xiujie Huang, Fangjie Yang, Haiming Lin, Haoyu Lin, Zhuoqun Zheng, Qifeng Liang, Jinhai Zhang, Xinxin Li
BMC Medical Education.2024;[Epub] 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

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: Janghee Park; J Educ Eval Health Prof. 2023;20:29. Published online November 10, 2023; DOI: https://doi.org/10.3352/jeehp.2023.20.29

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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.

Citations

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Higher education students’ perceptions of ChatGPT: A global study of early reactions
Dejan Ravšelj, Damijana Keržič, Nina Tomaževič, Lan Umek, Nejc Brezovar, Noorminshah A. Iahad, Ali Abdulla Abdulla, Anait Akopyan, Magdalena Waleska Aldana Segura, Jehan AlHumaid, Mohamed Farouk Allam, Maria Alló, Raphael Papa Kweku Andoh, Octavian Andron
PLOS ONE.2025; 20(2): e0315011. 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
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

Efficacy and limitations of ChatGPT as a biostatistical problem-solving tool in medical education in Serbia: a descriptive study: Aleksandra Ignjatović, Lazar Stevanović; J Educ Eval Health Prof. 2023;20:28. Published online October 16, 2023; DOI: https://doi.org/10.3352/jeehp.2023.20.28

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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
ChatGPT for Univariate Statistics: Validation of AI-Assisted Data Analysis in Healthcare Research
Michael R Ruta, Tony Gaidici, Chase Irwin, Jonathan Lifshitz
Journal of Medical Internet Research.2025; 27: e63550. 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; 110(6): 3701. 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
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Danimir Mandić, Gordana Miščević, Ljiljana Bujišić
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A Comparative Evaluation of Statistical Product and Service Solutions (SPSS) and ChatGPT-4 in Statistical Analyses
Al Imran Shahrul, Alizae Marny F Syed Mohamed
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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
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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

Brief report

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: Chao-Cheng Lin, Zaine Akuhata-Huntington, Che-Wei Hsu; J Educ Eval Health Prof. 2023;20:17. Published online June 12, 2023; DOI: https://doi.org/10.3352/jeehp.2023.20.17

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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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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

Review

Can an artificial intelligence chatbot be the author of a scholarly article?: Ju Yoen Lee; J Educ Eval Health Prof. 2023;20:6. Published online February 27, 2023; DOI: https://doi.org/10.3352/jeehp.2023.20.6

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824 Download
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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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IEEE Access.2024; 12: 95368. CrossRef
ChatGPT or Gemini: Who Makes the Better Scientific Writing Assistant?
Hatoon S. AlSagri, Faiza Farhat, Shahab Saquib Sohail, Abdul Khader Jilani Saudagar
Journal of Academic Ethics.2024;[Epub] CrossRef
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Balaji Arumugam, Arun Murugan, Kirubakaran S., Saranya Rajamanickam
International Journal of Preventative & Evidence Based Medicine.2024; : 1. CrossRef
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Telematics and Informatics.2024; 94: 102187. CrossRef
Leveraging Artificial Intelligence In Project-Based Service Learning To Advance Sustainable Development: A Pedagogical Approach For Marketing Education
C. M. Dubay, Melanie B. Richards
Marketing Education Review.2024; 34(4): 307. CrossRef
Let stochastic parrots squawk: why academic journals should allow large language models to coauthor articles
Nicholas J. Abernethy
AI and Ethics.2024;[Epub] CrossRef
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Paul Formosa, Sarah Bankins, Rita Matulionyte, Omid Ghasemi
AI & SOCIETY.2024;[Epub] CrossRef
Strategies for integrating ChatGPT and generative AI into clinical studies
Jeong-Moo Lee
Blood Research.2024;[Epub] CrossRef
Universal skepticism of ChatGPT: a review of early literature on chat generative pre-trained transformer
Casey Watters, Michal K. Lemanski
Frontiers in Big Data.2023;[Epub] CrossRef
The importance of human supervision in the use of ChatGPT as a support tool in scientific writing
William Castillo-González
Metaverse Basic and Applied Research.2023;[Epub] CrossRef
ChatGPT for Future Medical and Dental Research
Bader Fatani
Cureus.2023;[Epub] CrossRef
Chatbots in Medical Research
Punit Sharma
Clinical Nuclear Medicine.2023; 48(9): 838. CrossRef
Potential applications of ChatGPT in dermatology
Nicolas Kluger
Journal of the European Academy of Dermatology and Venereology.2023;[Epub] CrossRef
The emergent role of artificial intelligence, natural learning processing, and large language models in higher education and research
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Research in Social and Administrative Pharmacy.2023; 19(8): 1236. CrossRef
ChatGPT Performance on the American Urological Association Self-assessment Study Program and the Potential Influence of Artificial Intelligence in Urologic Training
Nicholas A. Deebel, Ryan Terlecki
Urology.2023; 177: 29. CrossRef
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Thomas Ritz
Biological Psychology.2023; 181: 108590. CrossRef
The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts
Mohammad Hosseini, David B Resnik, Kristi Holmes
Research Ethics.2023; 19(4): 449. CrossRef
How trustworthy is ChatGPT? The case of bibliometric analyses
Faiza Farhat, Shahab Saquib Sohail, Dag Øivind Madsen
Cogent Engineering.2023;[Epub] CrossRef
Disclosing use of Artificial Intelligence: Promoting transparency in publishing
Parvaiz A. Koul
Lung India.2023; 40(5): 401. CrossRef
ChatGPT in medical research: challenging time ahead
Daideepya C Bhargava, Devendra Jadav, Vikas P Meshram, Tanuj Kanchan
Medico-Legal Journal.2023; 91(4): 223. CrossRef
Academic publisher guidelines on AI usage: A ChatGPT supported thematic analysis
Mike Perkins, Jasper Roe
F1000Research.2023; 12: 1398. CrossRef
The Role of AI in Writing an Article and Whether it Can Be a Co-author: What if it Gets Support From 2 Different AIs Like ChatGPT and Google Bard for the Same Theme?
İlhan Bahşi, Ayşe Balat
Journal of Craniofacial Surgery.2023;[Epub] CrossRef
Ethical consideration of the use of generative artificial intelligence, including ChatGPT in writing a nursing article
Sun Huh
Child Health Nursing Research.2023; 29(4): 249. CrossRef
Artificial Intelligence-Supported Systems in Anesthesiology and Its Standpoint to Date—A Review
Fiona M. P. Pham
Open Journal of Anesthesiology.2023; 13(07): 140. CrossRef
ChatGPT as an innovative tool for increasing sales in online stores
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Procedia Computer Science.2023; 225: 3450. CrossRef
Intelligent Plagiarism as a Misconduct in Academic Integrity
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Acta Médica Portuguesa.2023; 37(1): 1. CrossRef
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European Journal of Therapeutics.2023;[Epub] CrossRef
May Artificial Intelligence Be a Co-Author on an Academic Paper?
Ayşe Balat, İlhan Bahşi
European Journal of Therapeutics.2023; 29(3): e12. CrossRef
Opportunities and challenges for ChatGPT and large language models in biomedicine and health
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Briefings in Bioinformatics.2023;[Epub] CrossRef
ChatGPT: "To be or not to be" ... in academic research. The human mind's analytical rigor and capacity to discriminate between AI bots' truths and hallucinations
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Balneo and PRM Research Journal.2023; 14(Vol.14, no): 614. CrossRef
Editorial policies of Journal of Educational Evaluation for Health Professions on the use of generative artificial intelligence in article writing and peer review
Sun Huh
Journal of Educational Evaluation for Health Professions.2023; 20: 40. CrossRef
Should We Wait for Major Frauds to Unveil to Plan an AI Use License?
Istemihan Coban
European Journal of Therapeutics.2023; 30(2): 198. CrossRef

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