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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
  • 1,222 View
  • 286 Download
  • 1 Crossref
AbstractAbstract PDFSupplementary 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.

Citations

Citations to this article as recorded by  
  • Chatbots in neurology and neuroscience: interactions with students, patients and neurologists
    Stefano Sandrone
    Brain Disorders.2024; : 100145.     CrossRef
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
  • 1,177 View
  • 180 Download
AbstractAbstract PDFSupplementary 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.
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
  • 1,536 View
  • 154 Download
  • 2 Web of Science
  • 2 Crossref
AbstractAbstract PDFSupplementary 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.

Citations

Citations to this article as recorded by  
  • 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
  • The emergence of generative artificial intelligence platforms in 2023, journal metrics, appreciation to reviewers and volunteers, and obituary
    Sun Huh
    Journal of Educational Evaluation for Health Professions.2024; 21: 9.     CrossRef
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
  • 2,579 View
  • 393 Download
  • 3 Web of Science
  • 4 Crossref
AbstractAbstract PDFSupplementary 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.

Citations

Citations to this article as recorded by  
  • 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
  • Artificial Intelligence: Fundamentals and Breakthrough Applications in Epilepsy
    Wesley Kerr, Sandra Acosta, Patrick Kwan, Gregory Worrell, Mohamad A. Mikati
    Epilepsy Currents.2024;[Epub]     CrossRef
  • A Developed Graphical User Interface-Based on Different Generative Pre-trained Transformers Models
    Ekrem Küçük, İpek Balıkçı Çiçek, Zeynep Küçükakçalı, Cihan Yetiş, Cemil Çolak
    ODÜ Tıp Dergisi.2024; 11(1): 18.     CrossRef
  • Art or Artifact: Evaluating the Accuracy, Appeal, and Educational Value of AI-Generated Imagery in DALL·E 3 for Illustrating Congenital Heart Diseases
    Mohamad-Hani Temsah, Abdullah N. Alhuzaimi, Mohammed Almansour, Fadi Aljamaan, Khalid Alhasan, Munirah A. Batarfi, Ibraheem Altamimi, Amani Alharbi, Adel Abdulaziz Alsuhaibani, Leena Alwakeel, Abdulrahman Abdulkhaliq Alzahrani, Khaled B. Alsulaim, Amr Jam
    Journal of Medical Systems.2024;[Epub]     CrossRef
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
  • 1,112 View
  • 100 Download
  • 2 Web of Science
  • 2 Crossref
AbstractAbstract PDFSupplementary 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.

Citations

Citations to this article as recorded by  
  • 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
  • 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
  • 1,610 View
  • 175 Download
  • 5 Web of Science
  • 4 Crossref
AbstractAbstract PDFSupplementary 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.

Citations

Citations to this article as recorded by  
  • Performance of GPT-4V in Answering the Japanese Otolaryngology Board Certification Examination Questions: Evaluation Study
    Masao Noda, Takayoshi Ueno, Ryota Koshu, Yuji Takaso, Mari Dias Shimada, Chizu Saito, Hisashi Sugimoto, Hiroaki Fushiki, Makoto Ito, Akihiro Nomura, Tomokazu Yoshizaki
    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
  • 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
  • 1,861 View
  • 162 Download
  • 4 Web of Science
  • 4 Crossref
AbstractAbstract PDFSupplementary 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

Citations to this article as recorded by  
  • 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
  • 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
  • 2,184 View
  • 173 Download
  • 3 Web of Science
  • 6 Crossref
AbstractAbstract PDFSupplementary 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.

Citations

Citations to this article as recorded by  
  • 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;[Epub]     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
    Scientific Reports.2024;[Epub]     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
  • 2,068 View
  • 132 Download
  • 1 Web of Science
  • 1 Crossref
AbstractAbstract PDFSupplementary 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.

Citations

Citations to this article as recorded by  
  • 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
  • 8,534 View
  • 675 Download
  • 38 Web of Science
  • 42 Crossref
AbstractAbstract PDFSupplementary 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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  • Risks of abuse of large language models, like ChatGPT, in scientific publishing: Authorship, predatory publishing, and paper mills
    Graham Kendall, Jaime A. Teixeira da Silva
    Learned Publishing.2024; 37(1): 55.     CrossRef
  • Can ChatGPT be an author? A study of artificial intelligence authorship policies in top academic journals
    Brady D. Lund, K.T. Naheem
    Learned Publishing.2024; 37(1): 13.     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.2024; 35(1): 274.     CrossRef
  • Artificial Intelligence–Generated Scientific Literature: A Critical Appraisal
    Justyna Zybaczynska, Matthew Norris, Sunjay Modi, Jennifer Brennan, Pooja Jhaveri, Timothy J. Craig, Taha Al-Shaikhly
    The Journal of Allergy and Clinical Immunology: In Practice.2024; 12(1): 106.     CrossRef
  • Does Google’s Bard Chatbot perform better than ChatGPT on the European hand surgery exam?
    Goetsch Thibaut, Armaghan Dabbagh, Philippe Liverneaux
    International Orthopaedics.2024; 48(1): 151.     CrossRef
  • A Brief Review of the Efficacy in Artificial Intelligence and Chatbot-Generated Personalized Fitness Regimens
    Daniel K. Bays, Cole Verble, Kalyn M. Powers Verble
    Strength & Conditioning Journal.2024;[Epub]     CrossRef
  • Academic publisher guidelines on AI usage: A ChatGPT supported thematic analysis
    Mike Perkins, Jasper Roe
    F1000Research.2024; 12: 1398.     CrossRef
  • The Use of Artificial Intelligence in Writing Scientific Review Articles
    Melissa A. Kacena, Lilian I. Plotkin, Jill C. Fehrenbacher
    Current Osteoporosis Reports.2024; 22(1): 115.     CrossRef
  • Using AI to Write a Review Article Examining the Role of the Nervous System on Skeletal Homeostasis and Fracture Healing
    Murad K. Nazzal, Ashlyn J. Morris, Reginald S. Parker, Fletcher A. White, Roman M. Natoli, Jill C. Fehrenbacher, Melissa A. Kacena
    Current Osteoporosis Reports.2024; 22(1): 217.     CrossRef
  • GenAI et al.: Cocreation, Authorship, Ownership, Academic Ethics and Integrity in a Time of Generative AI
    Aras Bozkurt
    Open Praxis.2024; 16(1): 1.     CrossRef
  • An integrative decision-making framework to guide policies on regulating ChatGPT usage
    Umar Ali Bukar, Md Shohel Sayeed, Siti Fatimah Abdul Razak, Sumendra Yogarayan, Oluwatosin Ahmed Amodu
    PeerJ Computer Science.2024; 10: e1845.     CrossRef
  • Artificial Intelligence and Its Role in Medical Research
    Anurag Gola, Ambarish Das, Amar B. Gumataj, S. Amirdhavarshini, J. Venkatachalam
    Current Medical Issues.2024; 22(2): 97.     CrossRef
  • From advancements to ethics: Assessing ChatGPT’s role in writing research paper
    Vasu Gupta, Fnu Anamika, Kinna Parikh, Meet A Patel, Rahul Jain, Rohit Jain
    Turkish Journal of Internal Medicine.2024; 6(2): 74.     CrossRef
  • Yapay Zekânın Edebiyatta Kullanım Serüveni
    Nesime Ceyhan Akça, Serap Aslan Cobutoğlu, Özlem Yeşim Özbek, Mehmet Furkan Akça
    RumeliDE Dil ve Edebiyat Araştırmaları Dergisi.2024; (39): 283.     CrossRef
  • ChatGPT's Gastrointestinal Tumor Board Tango: A limping dance partner?
    Ughur Aghamaliyev, Javad Karimbayli, Clemens Giessen-Jung, Ilmer Matthias, Kristian Unger, Dorian Andrade, Felix O. Hofmann, Maximilian Weniger, Martin K. Angele, C. Benedikt Westphalen, Jens Werner, Bernhard W. Renz
    European Journal of Cancer.2024; 205: 114100.     CrossRef
  • Gout and Gout-Related Comorbidities: Insight and Limitations from Population-Based Registers in Sweden
    Panagiota Drivelegka, Lennart TH Jacobsson, Mats Dehlin
    Gout, Urate, and Crystal Deposition Disease.2024; 2(2): 144.     CrossRef
  • Artificial intelligence in academic cardiothoracic surgery
    Adham AHMED, Irbaz HAMEED
    The Journal of Cardiovascular Surgery.2024;[Epub]     CrossRef
  • The emergence of generative artificial intelligence platforms in 2023, journal metrics, appreciation to reviewers and volunteers, and obituary
    Sun Huh
    Journal of Educational Evaluation for Health Professions.2024; 21: 9.     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
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Brief report
Are ChatGPT’s knowledge and interpretation ability comparable to those of medical students in Korea for taking a parasitology examination?: a descriptive study  
Sun Huh
J Educ Eval Health Prof. 2023;20:1.   Published online January 11, 2023
DOI: https://doi.org/10.3352/jeehp.2023.20.1
  • 12,108 View
  • 1,030 Download
  • 138 Web of Science
  • 72 Crossref
AbstractAbstract PDFSupplementary 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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Review
What should medical students know about artificial intelligence in medicine?  
Seong Ho Park, Kyung-Hyun Do, Sungwon Kim, Joo Hyun Park, Young-Suk Lim
J Educ Eval Health Prof. 2019;16:18.   Published online July 3, 2019
DOI: https://doi.org/10.3352/jeehp.2019.16.18
  • 21,715 View
  • 640 Download
  • 63 Web of Science
  • 74 Crossref
AbstractAbstract PDFSupplementary Material
Artificial intelligence (AI) is expected to affect various fields of medicine substantially and has the potential to improve many aspects of healthcare. However, AI has been creating much hype, too. In applying AI technology to patients, medical professionals should be able to resolve any anxiety, confusion, and questions that patients and the public may have. Also, they are responsible for ensuring that AI becomes a technology beneficial for patient care. These make the acquisition of sound knowledge and experience about AI a task of high importance for medical students. Preparing for AI does not merely mean learning information technology such as computer programming. One should acquire sufficient knowledge of basic and clinical medicines, data science, biostatistics, and evidence-based medicine. As a medical student, one should not passively accept stories related to AI in medicine in the media and on the Internet. Medical students should try to develop abilities to distinguish correct information from hype and spin and even capabilities to create thoroughly validated, trustworthy information for patients and the public.

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Research article
An expert-led and artificial intelligence system-assisted tutoring course to improve the confidence of Chinese medical interns in suturing and ligature skills: a prospective pilot study  
Ying-Ying Yang, Boaz Shulruf
J Educ Eval Health Prof. 2019;16:7.   Published online April 10, 2019
DOI: https://doi.org/10.3352/jeehp.2019.16.7
  • 18,564 View
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  • 13 Web of Science
  • 17 Crossref
AbstractAbstract PDFSupplementary Material
Purpose
Lack of confidence in suturing/ligature skills due to insufficient practice and assessments is common among novice Chinese medical interns. This study aimed to improve the skill acquisition of medical interns through a new intervention program.
Methods
In addition to regular clinical training, expert-led or expert-led plus artificial intelligence (AI) system tutoring courses were implemented during the first 2 weeks of the surgical block. Interns could voluntarily join the regular (no additional tutoring), expert-led tutoring, or expert-led+AI tutoring groups freely. In the regular group, interns (n=25) did not receive additional tutoring. The expert-led group received 3-hour expert-led tutoring and in-training formative assessments after 2 practice sessions. After a similar expert-led course, the expert-led+AI group (n=23) practiced and assessed their skills on an AI system. Through a comparison with the internal standard, the system automatically recorded and evaluated every intern’s suturing/ligature skills. In the expert-led+AI group, performance and confidence were compared between interns who participated in 1, 2, or 3 AI practice sessions.
Results
The end-of-surgical block objective structured clinical examination (OSCE) performance and self-assessed confidence in suturing/ligature skills were highest in the expert-led+AI group. In comparison with the expert-led group, the expert-led+AI group showed similar performance in the in-training assessment and greater improvement in the end-of-surgical block OSCE. In the expert-led+AI group, the best performance and highest post-OSCE confidence were noted in those who engaged in 3 AI practice sessions.
Conclusion
This pilot study demonstrated the potential value of incorporating an additional expert-led+AI system–assisted tutoring course into the regular surgical curriculum.

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JEEHP : Journal of Educational Evaluation for Health Professions