From articles published in Journal of Educational Evaluation for Health Professions during the past two years (2022 ~ ).
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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13,704
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Abstract
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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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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10,022
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730
Download
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54
Web of Science
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49
Crossref
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Abstract
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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Application of computer-based testing in the Korean Medical Licensing Examination, the emergence of the metaverse in medical education, journal metrics and statistics, and appreciation to reviewers and volunteers
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Sun Huh
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J Educ Eval Health Prof. 2022;19:2. Published online January 13, 2022
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DOI: https://doi.org/10.3352/jeehp.2022.19.2
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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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Reviews
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Prevalence of burnout and related factors in nursing faculty members: a systematic review
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Marziyeh Hosseini, Mitra Soltanian, Camellia Torabizadeh, Zahra Hadian Shirazi
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J Educ Eval Health Prof. 2022;19:16. Published online July 14, 2022
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DOI: https://doi.org/10.3352/jeehp.2022.19.16
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5,756
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476
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13
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15
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Abstract
PDFSupplementary Material
- Purpose
The current study aimed to identify the prevalence of burnout and related factors in nursing faculty members through a systematic review of the literature.
Methods
A comprehensive search of electronic databases, including Scopus, PubMed, Web of Science, Iranmedex, and Scientific Information Database was conducted via keywords extracted from Medical Subject Headings, including burnout and nursing faculty, for studies published from database inception to April 1, 2022. The quality of the included studies in this review was assessed using the appraisal tool for cross-sectional studies.
Results
A total of 2,551 nursing faculty members were enrolled in 11 studies. The mean score of burnout in nursing faculty members based on the Maslach Burnout Inventory (MBI) was 59.28 out of 132. The burnout score in this study was presented in 3 MBI subscales: emotional exhaustion, 21.24 (standard deviation [SD]=9.70) out of 54; depersonalization, 5.88 (SD=4.20) out of 30; and personal accomplishment, 32.16 (SD=6.45) out of 48. Several factors had significant relationships with burnout in nursing faculty members, including gender, level of education, hours of work, number of classroom, students taught, full-time work, job pressure, perceived stress, subjective well-being, marital status, job satisfaction, work setting satisfaction, workplace empowerment, collegial support, management style, fulfillment of self-expectation, communication style, humor, and academic position.
Conclusion
Overall, the mean burnout scores in nursing faculty members were moderate. Therefore, health policymakers and managers can reduce the likelihood of burnout in nursing faculty members by using psychosocial interventions and support.
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Medical students’ satisfaction level with e-learning during the COVID-19 pandemic and its related factors: a systematic review
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Mahbubeh Tabatabaeichehr, Samane Babaei, Mahdieh Dartomi, Peiman Alesheikh, Amir Tabatabaee, Hamed Mortazavi, Zohreh Khoshgoftar
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J Educ Eval Health Prof. 2022;19:37. Published online December 20, 2022
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DOI: https://doi.org/10.3352/jeehp.2022.19.37
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3,195
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10
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Abstract
PDFSupplementary Material
- Purpose
This review investigated medical students’ satisfaction level with e-learning during the coronavirus disease 2019 (COVID-19) pandemic and its related factors.
Methods
A comprehensive systematic search was performed of international literature databases, including Scopus, PubMed, Web of Science, and Persian databases such as Iranmedex and Scientific Information Database using keywords extracted from Medical Subject Headings such as “Distance learning,” “Distance education,” “Online learning,” “Online education,” and “COVID-19” from the earliest date to July 10, 2022. The quality of the studies included in this review was evaluated using the appraisal tool for cross-sectional studies (AXIS tool).
Results
A total of 15,473 medical science students were enrolled in 24 studies. The level of satisfaction with e-learning during the COVID-19 pandemic among medical science students was 51.8%. Factors such as age, gender, clinical year, experience with e-learning before COVID-19, level of study, adaptation content of course materials, interactivity, understanding of the content, active participation of the instructor in the discussion, multimedia use in teaching sessions, adequate time dedicated to the e-learning, stress perception, and convenience had significant relationships with the satisfaction of medical students with e-learning during the COVID-19 pandemic.
Conclusion
Therefore, due to the inevitability of online education and e-learning, it is suggested that educational managers and policymakers choose the best online education method for medical students by examining various studies in this field to increase their satisfaction with e-learning.
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Irma Uliano Effting Zoch de Moura, Valentina Coutinho Baldoto Gava Chakr
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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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5,707
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Abstract
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.
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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 - 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 - Authentic assessment in medical education: exploring AI integration and student-as-partners collaboration
Syeda Sadia Fatima, Nabeel Ashfaque Sheikh, Athar Osama
Postgraduate Medical Journal.2024;[Epub] CrossRef - Comparative performance analysis of large language models: ChatGPT-3.5, ChatGPT-4 and Google Gemini in glucocorticoid-induced osteoporosis
Linjian Tong, Chaoyang Zhang, Rui Liu, Jia Yang, Zhiming Sun
Journal of Orthopaedic Surgery and Research.2024;[Epub] CrossRef - Can AI-Generated Clinical Vignettes in Japanese Be Used Medically and Linguistically?
Yasutaka Yanagita, Daiki Yokokawa, Shun Uchida, Yu Li, Takanori Uehara, Masatomi Ikusaka
Journal of General Internal Medicine.2024;[Epub] 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;[Epub] CrossRef - Technology integration into Chinese as a foreign language learning in higher education: An integrated bibliometric analysis and systematic review (2000–2024)
Binze Xu
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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
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.
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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 - 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
Educational/Faculty development material
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Using a virtual flipped classroom model to promote critical thinking in online graduate courses in the United States: a case presentation
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Jennifer Tomesko, Deborah Cohen, Jennifer Bridenbaugh
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J Educ Eval Health Prof. 2022;19:5. Published online February 28, 2022
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DOI: https://doi.org/10.3352/jeehp.2022.19.5
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5,316
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Abstract
PDFSupplementary Material
- Flipped classroom models encourage student autonomy and reverse the order of traditional classroom content such as lectures and assignments. Virtual learning environments are ideal for executing flipped classroom models to improve critical thinking skills. This paper provides health professions faculty with guidance on developing a virtual flipped classroom in online graduate nutrition courses between September 2021 and January 2022 at the School of Health Professions, Rutgers The State University of New Jersey. Examples of pre-class, live virtual face-to-face, and post-class activities are provided. Active learning, immediate feedback, and enhanced student engagement in a flipped classroom may result in a more thorough synthesis of information, resulting in increased critical thinking skills. This article describes how a flipped classroom model design in graduate online courses that incorporate virtual face-to-face class sessions in a virtual learning environment can be utilized to promote critical thinking skills. Health professions faculty who teach online can apply the examples discussed to their online courses.
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Radiography.2024; 30(2): 474. CrossRef - Team- and Problem-Based Learning in Health Services: A Systematic Literature Review of Recent Initiatives in the United States
Eileen Alexander, Ashley White, Ashley Varol, Kacey Appel, Cristian Lieneck
Education Sciences.2024; 14(5): 515. CrossRef - Evaluating a Virtual Flipped Classroom Approach to Nurse Practitioner Telehealth Competency Development
Laurie Posey, Sherrie Wallington, Neal Sikka, Arlene Pericak, Qiuping Zhou, Christine Pintz
Journal of Nursing Education.2024; 63(8): 546. CrossRef - Effectiveness of the Flipped Project-Based Learning Model Based on Moodle LMS to Improve Student Communication and Problem-Solving Skills in Learning Programming
Ruslan Ruslan, Lu’mu Lu’mu, M. Miftach Fakhri, Ansari Saleh Ahmar, Della Fadhilatunisa
Education Sciences.2024; 14(9): 1021. CrossRef - A flipped classroom with whiteboard animation and modules to enhance students' self-regulation, critical thinking and communication skills: a conceptual framework and its implementation
Gede Suwardika, Agus Tatang Sopandi, I. Putu Oktap Indrawan, Kadek Masakazu
Asian Association of Open Universities Journal.2024; 19(2): 135. CrossRef - Inculcating Critical Thinking Skills in Medical Students: Ways and Means
Mandeep Kaur, Rajiv Mahajan
International Journal of Applied & Basic Medical Research.2023; 13(2): 57. CrossRef - Promoting students’ critical thinking and scientific attitudes through socio-scientific issues-based flipped classroom
Nurfatimah Sugrah, Suyanta, Antuni Wiyarsi
LUMAT: International Journal on Math, Science and Technology Education.2023;[Epub] CrossRef - Análisis bibliométrico de la producción científica mundial sobre el aula invertida en la educación médica
Gloria Katty Muñoz-Estrada, Hugo Eladio Chumpitaz Caycho, John Barja-Ore, Natalia Valverde-Espinoza, Liliana Verde-Vargas, Frank Mayta-Tovalino
Educación Médica.2022; 23(5): 100758. CrossRef - Effect of a flipped classroom course to foster medical students’ AI literacy with a focus on medical imaging: a single group pre-and post-test study
Matthias C. Laupichler, Dariusch R. Hadizadeh, Maximilian W. M. Wintergerst, Leon von der Emde, Daniel Paech, Elizabeth A. Dick, Tobias Raupach
BMC Medical Education.2022;[Epub] CrossRef
Review
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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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Abstract
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.
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- 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;[Epub] 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
Medical Teacher.2024; 46(10): 1258. CrossRef - A Comparative Analysis of ChatGPT and Medical Faculty Graduates in Medical Specialization Exams: Uncovering the Potential of Artificial Intelligence in Medical Education
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
Research articles
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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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3,114
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Abstract
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.
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- 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;[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
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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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2,507
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Abstract
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.
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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 - 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 - 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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Priorities in updating training paradigms in orthopedic manual therapy: an international Delphi study
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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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3,752
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Abstract
PDFSupplementary 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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Citations
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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; : 1. 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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Improvement of the clinical skills of nurse anesthesia students using mini-clinical evaluation exercises in Iran: a randomized controlled study
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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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2,860
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Abstract
PDFSupplementary 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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- 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
Educational/Faculty development material
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Common models and approaches for the clinical educator to plan effective feedback encounters
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Cesar Orsini, Veena Rodrigues, Jorge Tricio, Margarita Rosel
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J Educ Eval Health Prof. 2022;19:35. Published online December 19, 2022
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DOI: https://doi.org/10.3352/jeehp.2022.19.35
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8,003
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Abstract
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- Giving constructive feedback is crucial for learners to bridge the gap between their current performance and the desired standards of competence. Giving effective feedback is a skill that can be learned, practiced, and improved. Therefore, our aim was to explore models in clinical settings and assess their transferability to different clinical feedback encounters. We identified the 6 most common and accepted feedback models, including the Feedback Sandwich, the Pendleton Rules, the One-Minute Preceptor, the SET-GO model, the R2C2 (Rapport/Reaction/Content/Coach), and the ALOBA (Agenda Led Outcome-based Analysis) model. We present a handy resource describing their structure, strengths and weaknesses, requirements for educators and learners, and suitable feedback encounters for use for each model. These feedback models represent practical frameworks for educators to adopt but also to adapt to their preferred style, combining and modifying them if necessary to suit their needs and context.
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