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Most-download articles are from the articles published in 2022 during the last three month.

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
  • 963 View
  • 199 Download
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 ChatGPT has limitations, such as potential bias, dependence on outdated data, and revenue generation challenges.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.
Research articles
Importance, performance frequency, and predicted future importance of dietitians’ jobs by practicing dietitians in Korea: a survey study
Cheongmin Sohn, Sooyoun Kwon, Won Gyoung Kim, Kyung-Eun Lee, Sun-Young Lee, Seungmin Lee
J Educ Eval Health Prof. 2024;21:1.   Published online January 2, 2024
DOI: https://doi.org/10.3352/jeehp.2024.21.1
  • 475 View
  • 128 Download
AbstractAbstract PDFSupplementary Material
Purpose
This study aimed to explore the perceptions held by practicing dietitians of the importance of their tasks performed in current work environments, the frequency at which those tasks are performed, and predictions about the importance of those tasks in future work environments.
Methods
This was a cross-sectional survey study. An online survey was administered to 350 practicing dietitians. They were asked to assess the importance, performance frequency, and predicted changes in the importance of 27 tasks using a 5-point scale. Descriptive statistics were calculated, and the means of the variables were compared across categorized work environments using analysis of variance.
Results
The importance scores of all surveyed tasks were higher than 3.0, except for the marketing management task. Self-development, nutrition education/counseling, menu planning, food safety management, and documentation/data management were all rated higher than 4.0. The highest performance frequency score was related to documentation/data management. The importance scores of all duties, except for professional development, differed significantly by workplace. As for predictions about the future importance of the tasks surveyed, dietitians responded that the importance of all 27 tasks would either remain at current levels or increase in the future.
Conclusion
Twenty-seven tasks were confirmed to represent dietitians’ job functions in various workplaces. These tasks can be used to improve the test specifications of the Korean Dietitian Licensing Examination and the curriculum of dietetic education programs.
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
  • 785 View
  • 123 Download
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.
Educational/Faculty development material
Common models and approaches for the clinical educator to plan effective feedback encounters  
Cesar Orsini, Veena Rodrigues, Jorge Tricio, Margarita Rosel
J Educ Eval Health Prof. 2022;19:35.   Published online December 19, 2022
DOI: https://doi.org/10.3352/jeehp.2022.19.35
  • 3,676 View
  • 577 Download
  • 1 Web of Science
  • 2 Crossref
AbstractAbstract PDFSupplementary Material
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.

Citations

Citations to this article as recorded by  
  • Navigating power dynamics between pharmacy preceptors and learners
    Shane Tolleson, Mabel Truong, Natalie Rosario
    Exploratory Research in Clinical and Social Pharmacy.2024; 13: 100408.     CrossRef
  • Feedback conversations: First things first?
    Katharine A. Robb, Marcy E. Rosenbaum, Lauren Peters, Susan Lenoch, Donna Lancianese, Jane L. Miller
    Patient Education and Counseling.2023; 115: 107849.     CrossRef
Research article
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
  • 297 View
  • 85 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.
Editorial
Review
How to review and assess a systematic review and meta-analysis article: a methodological study (secondary publication)  
Seung-Kwon Myung
J Educ Eval Health Prof. 2023;20:24.   Published online August 27, 2023
DOI: https://doi.org/10.3352/jeehp.2023.20.24
  • 1,826 View
  • 221 Download
  • 1 Web of Science
  • 1 Crossref
AbstractAbstract PDFSupplementary Material
Systematic reviews and meta-analyses have become central in many research fields, particularly medicine. They offer the highest level of evidence in evidence-based medicine and support the development and revision of clinical practice guidelines, which offer recommendations for clinicians caring for patients with specific diseases and conditions. This review summarizes the concepts of systematic reviews and meta-analyses and provides guidance on reviewing and assessing such papers. A systematic review refers to a review of a research question that uses explicit and systematic methods to identify, select, and critically appraise relevant research. In contrast, a meta-analysis is a quantitative statistical analysis that combines individual results on the same research question to estimate the common or mean effect. Conducting a meta-analysis involves defining a research topic, selecting a study design, searching literature in electronic databases, selecting relevant studies, and conducting the analysis. One can assess the findings of a meta-analysis by interpreting a forest plot and a funnel plot and by examining heterogeneity. When reviewing systematic reviews and meta-analyses, several essential points must be considered, including the originality and significance of the work, the comprehensiveness of the database search, the selection of studies based on inclusion and exclusion criteria, subgroup analyses by various factors, and the interpretation of the results based on the levels of evidence. This review will provide readers with helpful guidance to help them read, understand, and evaluate these articles.

Citations

Citations to this article as recorded by  
  • The Role of BIM in Managing Risks in Sustainability of Bridge Projects: A Systematic Review with Meta-Analysis
    Dema Munef Ahmad, László Gáspár, Zsolt Bencze, Rana Ahmad Maya
    Sustainability.2024; 16(3): 1242.     CrossRef
Research articles
Development and validity evidence for the resident-led large group teaching assessment instrument in the United States: a methodological study  
Ariel Shana Frey-Vogel, Kristina Dzara, Kimberly Anne Gifford, Yoon Soo Park, Justin Berk, Allison Heinly, Darcy Wolcott, Daniel Adam Hall, Shannon Elliott Scott-Vernaglia, Katherine Anne Sparger, Erica Ye-pyng Chung
J Educ Eval Health Prof. 2024;21:3.   Published online February 23, 2024
DOI: https://doi.org/10.3352/jeehp.2024.21.3
  • 154 View
  • 68 Download
AbstractAbstract PDFSupplementary Material
Purpose
Despite educational mandates to assess resident teaching competence, limited instruments with validity evidence exist for this purpose. Existing instruments do not allow faculty to assess resident-led teaching in a large group format or whether teaching was interactive. This study gathers validity evidence on the use of the Resident-led Large Group Teaching Assessment Instrument (Relate), an instrument used by faculty to assess resident teaching competency. Relate comprises 23 behaviors divided into 6 elements: learning environment, goals and objectives, content of talk, promotion of understanding and retention, session management, and closure.
Methods
Messick’s unified validity framework was used for this study. Investigators used video recordings of resident-led teaching from 3 pediatric residency programs to develop Relate and a rater guidebook. Faculty were trained on instrument use through frame-of-reference training. Resident teaching at all sites was video-recorded during 2018–2019. Two trained faculty raters assessed each video. Descriptive statistics on performance were obtained. Validity evidence sources include: rater training effect (response process), reliability and variability (internal structure), and impact on Milestones assessment (relations to other variables).
Results
Forty-eight videos, from 16 residents, were analyzed. Rater training improved inter-rater reliability from 0.04 to 0.64. The Φ-coefficient reliability was 0.50. There was a significant correlation between overall Relate performance and the pediatric teaching Milestone (r=0.34, P=0.019).
Conclusion
Relate provides validity evidence with sufficient reliability to measure resident-led large-group teaching competence.
Discovering social learning ecosystems during clinical clerkship from United States medical students’ feedback encounters: a content analysis  
Anna Therese Cianciolo, Heeyoung Han, Lydia Anne Howes, Debra Lee Klamen, Sophia Matos
J Educ Eval Health Prof. 2024;21:5.   Published online February 28, 2024
DOI: https://doi.org/10.3352/jeehp.2024.21.5
  • 153 View
  • 64 Download
AbstractAbstract PDFSupplementary Material
Purpose
We examined United States medical students’ self-reported feedback encounters during clerkship training to better understand in situ feedback practices. Specifically, we asked: Who do students receive feedback from, about what, when, where, and how do they use it? We explored whether curricular expectations for preceptors’ written commentary aligned with feedback as it occurs naturalistically in the workplace.
Methods
This study occurred from July 2021 to February 2022 at Southern Illinois University School of Medicine. We used qualitative survey-based experience sampling to gather students’ accounts of their feedback encounters in 8 core specialties. We analyzed the who, what, when, where, and why of 267 feedback encounters reported by 11 clerkship students over 30 weeks. Code frequencies were mapped qualitatively to explore patterns in feedback encounters.
Results
Clerkship feedback occurs in patterns apparently related to the nature of clinical work in each specialty. These patterns may be attributable to each specialty’s “social learning ecosystem”—the distinctive learning environment shaped by the social and material aspects of a given specialty’s work, which determine who preceptors are, what students do with preceptors, and what skills or attributes matter enough to preceptors to comment on.
Conclusion
Comprehensive, standardized expectations for written feedback across specialties conflict with the reality of workplace-based learning. Preceptors may be better able—and more motivated—to document student performance that occurs as a natural part of everyday work. Nurturing social learning ecosystems could facilitate workplace-based learning such that, across specialties, students acquire a comprehensive clinical skillset appropriate for graduation.
Use of learner-driven, formative, ad-hoc, prospective assessment of competence in physical therapist clinical education in the United States: a prospective cohort study  
Carey Holleran, Jeffrey Konrad, Barbara Norton, Tamara Burlis, Steven Ambler
J Educ Eval Health Prof. 2023;20:36.   Published online December 8, 2023
DOI: https://doi.org/10.3352/jeehp.2023.20.36
  • 483 View
  • 92 Download
AbstractAbstract PDFSupplementary Material
Purpose
The purpose of this project was to implement a process for learner-driven, formative, prospective, ad-hoc, entrustment assessment in Doctor of Physical Therapy clinical education. Our goals were to develop an innovative entrustment assessment tool, and then explore whether the tool detected (1) differences between learners at different stages of development and (2) differences within learners across the course of a clinical education experience. We also investigated whether there was a relationship between the number of assessments and change in performance.
Methods
A prospective, observational, cohort of clinical instructors (CIs) was recruited to perform learner-driven, formative, ad-hoc, prospective, entrustment assessments. Two entrustable professional activities (EPAs) were used: (1) gather a history and perform an examination and (2) implement and modify the plan of care, as needed. CIs provided a rating on the entrustment scale and provided narrative support for their rating.
Results
Forty-nine learners participated across 4 clinical experiences (CEs), resulting in 453 EPA learner-driven assessments. For both EPAs, statistically significant changes were detected both between learners at different stages of development and within learners across the course of a CE. Improvement within each CE was significantly related to the number of feedback opportunities.
Conclusion
The results of this pilot study provide preliminary support for the use of learner-driven, formative, ad-hoc assessments of competence based on EPAs with a novel entrustment scale. The number of formative assessments requested correlated with change on the EPA scale, suggesting that formative feedback may augment performance improvement.
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
  • 10,495 View
  • 994 Download
  • 99 Web of Science
  • 58 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.

Citations

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  • Large Language Models and Artificial Intelligence: A Primer for Plastic Surgeons on the Demonstrated and Potential Applications, Promises, and Limitations of ChatGPT
    Jad Abi-Rafeh, Hong Hao Xu, Roy Kazan, Ruth Tevlin, Heather Furnas
    Aesthetic Surgery Journal.2024; 44(3): 329.     CrossRef
  • Unveiling the ChatGPT phenomenon: Evaluating the consistency and accuracy of endodontic question answers
    Ana Suárez, Víctor Díaz‐Flores García, Juan Algar, Margarita Gómez Sánchez, María Llorente de Pedro, Yolanda Freire
    International Endodontic Journal.2024; 57(1): 108.     CrossRef
  • Bob or Bot: Exploring ChatGPT's Answers to University Computer Science Assessment
    Mike Richards, Kevin Waugh, Mark Slaymaker, Marian Petre, John Woodthorpe, Daniel Gooch
    ACM Transactions on Computing Education.2024; 24(1): 1.     CrossRef
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    Michelle W. T. Cheng, Iris H. Y. YIM
    Discover Education.2024;[Epub]     CrossRef
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    Firas Haddad, Joanna S Saade
    JMIR Medical Education.2024; 10: e50842.     CrossRef
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    Mor Saban, Ilana Dubovi
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  • Üniversite Öğrencilerinin ChatGPT 3,5 Deneyimleri: Yapay Zekâyla Yazılmış Masal Varyantları
    Bilge GÖK, Fahri TEMİZYÜREK, Özlem BAŞ
    Korkut Ata Türkiyat Araştırmaları Dergisi.2024; (14): 1040.     CrossRef
  • Tracking ChatGPT Research: Insights From the Literature and the Web
    Omar Mubin, Fady Alnajjar, Zouheir Trabelsi, Luqman Ali, Medha Mohan Ambali Parambil, Zhao Zou
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    Sun Huh
    Journal of Educational Evaluation for Health Professions.2023; 20: 5.     CrossRef
  • Emergence of the metaverse and ChatGPT in journal publishing after the COVID-19 pandemic
    Sun Huh
    Science Editing.2023; 10(1): 1.     CrossRef
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  • Overview of Early ChatGPT’s Presence in Medical Literature: Insights From a Hybrid Literature Review by ChatGPT and Human Experts
    Omar Temsah, Samina A Khan, Yazan Chaiah, Abdulrahman Senjab, Khalid Alhasan, Amr Jamal, Fadi Aljamaan, Khalid H Malki, Rabih Halwani, Jaffar A Al-Tawfiq, Mohamad-Hani Temsah, Ayman Al-Eyadhy
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    Sam Sedaghat
    Clinical Medicine.2023; 23(3): 278.     CrossRef
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    璇 师
    Advances in Education.2023; 13(05): 2617.     CrossRef
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    Soshi Takagi, Takashi Watari, Ayano Erabi, Kota Sakaguchi
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  • ChatGPT’s quiz skills in different otolaryngology subspecialties: an analysis of 2576 single-choice and multiple-choice board certification preparation questions
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    European Archives of Oto-Rhino-Laryngology.2023; 280(9): 4271.     CrossRef
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    Mayank Agarwal, Priyanka Sharma, Ayan Goswami
    Cureus.2023;[Epub]     CrossRef
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    JMIR Medical Education.2023; 9: e47274.     CrossRef
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    Tarık TALAN, Yusuf KALINKARA
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    Journal of Educational Evaluation for Health Professions.2023; 20: 17.     CrossRef
  • Examining Real-World Medication Consultations and Drug-Herb Interactions: ChatGPT Performance Evaluation
    Hsing-Yu Hsu, Kai-Cheng Hsu, Shih-Yen Hou, Ching-Lung Wu, Yow-Wen Hsieh, Yih-Dih Cheng
    JMIR Medical Education.2023; 9: e48433.     CrossRef
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    Arijita Banerjee, Aquil Ahmad, Payal Bhalla, Kavita Goyal
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    Xinyi Wang, Zhenye Gong, Guoxin Wang, Jingdan Jia, Ying Xu, Jialu Zhao, Qingye Fan, Shaun Wu, Weiguo Hu, Xiaoyang Li
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    Doris Ruiz-Talavera, Jaime Enrique De la Cruz-Aguero, Nereo García-Palomino, Renzo Calderón-Espinoza, William Joel Marín-Rodriguez
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    Carl Preiksaitis, Christian Rose
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    Araz Zirar
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    Shani Rosen, Mor Saban
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    Shamima Yesmin
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    Mohd Afjal
    Library Hi Tech.2023;[Epub]     CrossRef
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    Hasan B Ilgaz, Zehra Çelik
    Cureus.2023;[Epub]     CrossRef
  • Is ChatGPT’s Knowledge and Interpretative Ability Comparable to First Professional MBBS (Bachelor of Medicine, Bachelor of Surgery) Students of India in Taking a Medical Biochemistry Examination?
    Abhra Ghosh, Nandita Maini Jindal, Vikram K Gupta, Ekta Bansal, Navjot Kaur Bajwa, Abhishek Sett
    Cureus.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
  • Potential Use of ChatGPT for Patient Information in Periodontology: A Descriptive Pilot Study
    Osman Babayiğit, Zeynep Tastan Eroglu, Dilek Ozkan Sen, Fatma Ucan Yarkac
    Cureus.2023;[Epub]     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
  • Assessing the Performance of ChatGPT in Medical Biochemistry Using Clinical Case Vignettes: Observational Study
    Krishna Mohan Surapaneni
    JMIR Medical Education.2023; 9: e47191.     CrossRef
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    Peng Zhang, Gemma Tur
    European Journal of Education.2023;[Epub]     CrossRef
  • 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
    Journal of Educational Evaluation for Health Professions.2023; 20: 30.     CrossRef
  • ChatGPT’s performance in German OB/GYN exams – paving the way for AI-enhanced medical education and clinical practice
    Maximilian Riedel, Katharina Kaefinger, Antonia Stuehrenberg, Viktoria Ritter, Niklas Amann, Anna Graf, Florian Recker, Evelyn Klein, Marion Kiechle, Fabian Riedel, Bastian Meyer
    Frontiers in Medicine.2023;[Epub]     CrossRef
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    Janghee Park
    Journal of Educational Evaluation for Health Professions.2023; 20: 29.     CrossRef
  • Evaluating ChatGPT as a self‐learning tool in medical biochemistry: A performance assessment in undergraduate medical university examination
    Krishna Mohan Surapaneni, Anusha Rajajagadeesan, Lakshmi Goudhaman, Shalini Lakshmanan, Saranya Sundaramoorthi, Dineshkumar Ravi, Kalaiselvi Rajendiran, Porchelvan Swaminathan
    Biochemistry and Molecular Biology Education.2023;[Epub]     CrossRef
  • FROM TEXT TO DIAGNOSE: CHATGPT’S EFFICACY IN MEDICAL DECISION-MAKING
    Yaroslav Mykhalko, Pavlo Kish, Yelyzaveta Rubtsova, Oleksandr Kutsyn, Valentyna Koval
    Wiadomości Lekarskie.2023; 76(11): 2345.     CrossRef
  • Using ChatGPT for Clinical Practice and Medical Education: Cross-Sectional Survey of Medical Students’ and Physicians’ Perceptions
    Pasin Tangadulrat, Supinya Sono, Boonsin Tangtrakulwanich
    JMIR Medical Education.2023; 9: e50658.     CrossRef
  • Below average ChatGPT performance in medical microbiology exam compared to university students
    Malik Sallam, Khaled Al-Salahat
    Frontiers in Education.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
    Aurelian Anghelescu, Ilinca Ciobanu, Constantin Munteanu, Lucia Ana Maria Anghelescu, Gelu Onose
    Balneo and PRM Research Journal.2023; 14(Vol.14, no): 614.     CrossRef
  • ChatGPT Review: A Sophisticated Chatbot Models in Medical & Health-related Teaching and Learning
    Nur Izah Ab Razak, Muhammad Fawwaz Muhammad Yusoff, Rahmita Wirza O.K. Rahmat
    Malaysian Journal of Medicine and Health Sciences.2023; 19(s12): 98.     CrossRef
  • Application of artificial intelligence chatbots, including ChatGPT, in education, scholarly work, programming, and content generation and its prospects: a narrative review
    Tae Won Kim
    Journal of Educational Evaluation for Health Professions.2023; 20: 38.     CrossRef
  • Trends in research on ChatGPT and adoption-related issues discussed in articles: a narrative review
    Sang-Jun Kim
    Science Editing.2023; 11(1): 3.     CrossRef
  • Information amount, accuracy, and relevance of generative artificial intelligences’ answers to learning objectives of medical arthropodology evaluated in English and Korean queries in December 2023: a descriptive study
    Hyunju Lee, Soo Bin Park
    Journal of Educational Evaluation for Health Professions.2023; 20: 39.     CrossRef
Research article
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
  • 893 View
  • 143 Download
  • 2 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: An evaluation study (Preprint)
    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;[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
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
  • 6,845 View
  • 612 Download
  • 29 Web of Science
  • 34 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.

Citations

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    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
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Research articles
Negative effects on medical students’ scores for clinical performance during the COVID-19 pandemic in Taiwan: a comparative study  
Eunice Jia-Shiow Yuan, Shiau-Shian Huang, Chia-An Hsu, Jiing-Feng Lirng, Tzu-Hao Li, Chia-Chang Huang, Ying-Ying Yang, Chung-Pin Li, Chen-Huan Chen
J Educ Eval Health Prof. 2023;20:37.   Published online December 26, 2023
DOI: https://doi.org/10.3352/jeehp.2023.20.37
  • 582 View
  • 65 Download
AbstractAbstract PDFSupplementary Material
Purpose
Coronavirus disease 2019 (COVID-19) has heavily impacted medical clinical education in Taiwan. Medical curricula have been altered to minimize exposure and limit transmission. This study investigated the effect of COVID-19 on Taiwanese medical students’ clinical performance using online standardized evaluation systems and explored the factors influencing medical education during the pandemic.
Methods
Medical students were scored from 0 to 100 based on their clinical performance from 1/1/2018 to 6/31/2021. The students were placed into pre-COVID-19 (before 2/1/2020) and midst-COVID-19 (on and after 2/1/2020) groups. Each group was further categorized into COVID-19-affected specialties (pulmonary, infectious, and emergency medicine) and other specialties. Generalized estimating equations (GEEs) were used to compare and examine the effects of relevant variables on student performance.
Results
In total, 16,944 clinical scores were obtained for COVID-19-affected specialties and other specialties. For the COVID-19-affected specialties, the midst-COVID-19 score (88.513.52) was significantly lower than the pre-COVID-19 score (90.143.55) (P<0.0001). For the other specialties, the midst-COVID-19 score (88.323.68) was also significantly lower than the pre-COVID-19 score (90.063.58) (P<0.0001). There were 1,322 students (837 males and 485 females). Male students had significantly lower scores than female students (89.333.68 vs. 89.993.66, P=0.0017). GEE analysis revealed that the COVID-19 pandemic (unstandardized beta coefficient=-1.99, standard error [SE]=0.13, P<0.0001), COVID-19-affected specialties (B=0.26, SE=0.11, P=0.0184), female students (B=1.10, SE=0.20, P<0.0001), and female attending physicians (B=-0.19, SE=0.08, P=0.0145) were independently associated with students’ scores.
Conclusion
COVID-19 negatively impacted medical students' clinical performance, regardless of their specialty. Female students outperformed male students, irrespective of the pandemic.
Effect of a transcultural nursing course on improving the cultural competency of nursing graduate students in Korea: a before-and-after study
Kyung Eui Bae, Geum Hee Jeong
J Educ Eval Health Prof. 2023;20:35.   Published online December 4, 2023
DOI: https://doi.org/10.3352/jeehp.2023.20.35
  • 570 View
  • 108 Download
AbstractAbstract PDFSupplementary Material
Purpose
This study aimed to evaluate the impact of a transcultural nursing course on enhancing the cultural competency of graduate nursing students in Korea. We hypothesized that participants’ cultural competency would significantly improve in areas such as communication, biocultural ecology and family, dietary habits, death rituals, spirituality, equity, and empowerment and intermediation after completing the course. Furthermore, we assessed the participants’ overall satisfaction with the course.
Methods
A before-and-after study was conducted with graduate nursing students at Hallym University, Chuncheon, Korea, from March to June 2023. A transcultural nursing course was developed based on Giger & Haddad’s transcultural nursing model and Purnell’s theoretical model of cultural competence. Data was collected using a cultural competence scale for registered nurses developed by Kim and his colleagues. A total of 18 students participated, and the paired t-test was employed to compare pre-and post-intervention scores.
Results
The study revealed significant improvements in all 7 categories of cultural nursing competence (P<0.01). Specifically, the mean differences in scores (pre–post) ranged from 0.74 to 1.09 across the categories. Additionally, participants expressed high satisfaction with the course, with an average score of 4.72 out of a maximum of 5.0.
Conclusion
The transcultural nursing course effectively enhanced the cultural competency of graduate nursing students. Such courses are imperative to ensure quality care for the increasing multicultural population in Korea.

JEEHP : Journal of Educational Evaluation for Health Professions