%0 Journal Article
        
%@ 2369-3762
%I JMIR Publications
%V 11
%N 
%P e70420
%T Enhancing AI-Driven Medical Translations: Considerations for Language Concordance
%A Quon,Stephanie
%A Zhou,Sarah
%K letter to the editor
%K ChatGPT
%K AI
%K artificial intelligence
%K language
%K translation
%K health care disparity
%K natural language model
%K survey
%K patient education
%K accessibility
%K preference
%K human language
%K communication
%K language-concordant care
%D 2025

%7 11.4.2025
%9 
%J JMIR Med Educ
%G English
                
%X 
%R 10.2196/70420
%U https://mededu.jmir.org/2025/1/e70420
%U https://doi.org/10.2196/70420

%0 Journal Article
        
%@ 2369-3762
%I JMIR Publications
%V 11
%N 
%P e71721
%T Authors’ Reply: Enhancing AI-Driven Medical Translations: Considerations for Language Concordance
%A Teng,Joyce
%A Novoa,Roberto Andres
%A Aleshin,Maria Alexandrovna
%A Lester,Jenna
%A Seiger,Kira
%A Dzuali,Fiatsogbe
%A Daneshjou,Roxana
%K ChatGPT
%K artificial intelligence
%K language
%K translation
%K health care disparity
%K natural language model
%K survey
%K patient education
%K accessibility
%K preference
%K human language
%K communication
%K language-concordant care
%D 2025

%7 11.4.2025
%9 
%J JMIR Med Educ
%G English
                
%X 
%R 10.2196/71721
%U https://mededu.jmir.org/2025/1/e71721
%U https://doi.org/10.2196/71721

%0 Journal Article
        
%@ 2369-3762
%I JMIR Publications
%V 11
%N 
%P e72998
%T Citation Accuracy Challenges Posed by Large Language Models
%A Zhang,Manlin
%A Zhao,Tianyu
%K chatGPT
%K medical education
%K Saudi Arabia
%K perceptions
%K knowledge
%K medical students
%K faculty
%K chatbot
%K qualitative study
%K artificial intelligence
%K AI
%K AI-based tools
%K universities
%K thematic analysis
%K learning
%K satisfaction
%K LLM
%K large language model
%D 2025

%7 2.4.2025
%9 
%J JMIR Med Educ
%G English
                
%X 
%R 10.2196/72998
%U https://mededu.jmir.org/2025/1/e72998
%U https://doi.org/10.2196/72998

%0 Journal Article
        
%@ 2369-3762
%I JMIR Publications
%V 11
%N 
%P e73698
%T Authors’ Reply: Citation Accuracy Challenges Posed by Large Language Models
%A Temsah,Mohamad-Hani
%A Al-Eyadhy,Ayman
%A Jamal,Amr
%A Alhasan,Khalid
%A Malki,Khalid H
%K ChatGPT
%K Gemini
%K DeepSeek
%K medical education
%K AI
%K artificial intelligence
%K Saudi Arabia
%K perceptions
%K medical students
%K faculty
%K LLM
%K chatbot
%K qualitative study
%K thematic analysis
%K satisfaction
%K RAG retrieval-augmented generation
%D 2025

%7 2.4.2025
%9 
%J JMIR Med Educ
%G English
                
%X 
%R 10.2196/73698
%U https://mededu.jmir.org/2025/1/e73698
%U https://doi.org/10.2196/73698

%0 Journal Article
        
%@ 2369-3762
%I JMIR Publications
%V 11
%N 
%P e72336
%T Author’s Reply: Examining Multimodal AI Resources in Medical Education: The Role of Immersion, Motivation, and Fidelity in AI Narrative Learning
%A Bland,Tyler
%K artificial intelligence
%K cinematic clinical narrative
%K cinemeducation
%K medical education
%K narrative learning
%K pharmacology
%K AI
%K medical students
%K preclinical education
%K long-term retention
%K AI tools
%K GPT-4
%K image
%K applicability
%K CCN
%D 2025

%7 18.3.2025
%9 
%J JMIR Med Educ
%G English
                
%X 
%R 10.2196/72336
%U https://mededu.jmir.org/2025/1/e72336
%U https://doi.org/10.2196/72336

%0 Journal Article
        
%@ 2369-3762
%I JMIR Publications
%V 11
%N 
%P e72190
%T Examining Multimodal AI Resources in Medical Education: The Role of Immersion, Motivation, and Fidelity in AI Narrative Learning
%A Jacobs,Chris
%K artificial intelligence
%K cinematic clinical narrative
%K cinemeducation
%K medical education
%K narrative learning
%K AI
%K medical students
%K preclinical education
%K long-term retention
%K pharmacology
%K AI tools
%K GPT-4
%K image
%K applicability
%K CCN
%D 2025

%7 18.3.2025
%9 
%J JMIR Med Educ
%G English
                
%X 
%R 10.2196/72190
%U https://mededu.jmir.org/2025/1/e72190
%U https://doi.org/10.2196/72190

%0 Journal Article
        
%@ 2369-3762
%I 
%V 10
%N 
%P e56117
%T A Use Case for Generative AI in Medical Education
%A Sekhar,Tejas C
%A Nayak,Yash R
%A Abdoler,Emily A
%K medical education
%K med ed
%K generative artificial intelligence
%K artificial intelligence
%K GAI
%K AI
%K Anki
%K flashcard
%K undergraduate medical education
%K UME
%D 2024

%7 7.6.2024
%9 
%J JMIR Med Educ
%G English
                
%X 
%R 10.2196/56117
%U https://mededu.jmir.org/2024/1/e56117
%U https://doi.org/10.2196/56117

%0 Journal Article
        
%@ 2369-3762
%I 
%V 10
%N 
%P e58370
%T Authors’ Reply: A Use Case for Generative AI in Medical Education
%A Pendergrast,Tricia
%A Chalmers,Zachary
%K ChatGPT
%K undergraduate medical education
%K large language models
%D 2024

%7 7.6.2024
%9 
%J JMIR Med Educ
%G English
                
%X 
%R 10.2196/58370
%U https://mededu.jmir.org/2024/1/e58370
%U https://doi.org/10.2196/58370

%0 Journal Article
        
%@ 2369-3762
%I 
%V 10
%N 
%P e58743
%T Digital Skills to Improve Levels of Care and Renew Health Care Professions
%A De Martinis,Massimo
%A Ginaldi,Lia
%K digital competence
%K telehealth
%K nursing
%K health care workforce
%K health care professionals
%K informatics
%K education
%K curriculum
%K interdisciplinary education
%K health care education
%D 2024

%7 1.5.2024
%9 
%J JMIR Med Educ
%G English
                
%X 
%R 10.2196/58743
%U https://mededu.jmir.org/2024/1/e58743
%U https://doi.org/10.2196/58743

%0 Journal Article
        
%@ 2369-3762
%I 
%V 10
%N 
%P e57696
%T A Student’s Viewpoint on ChatGPT Use and Automation Bias in Medical Education
%A Dsouza,Jeanne Maria
%K AI
%K artificial intelligence
%K ChatGPT
%K medical education
%D 2024

%7 15.4.2024
%9 
%J JMIR Med Educ
%G English
                
%X 
%R 10.2196/57696
%U https://mededu.jmir.org/2024/1/e57696
%U https://doi.org/10.2196/57696

%0 Journal Article
        
%@ 2369-3762
%I JMIR Publications
%V 9
%N 
%P e50902
%T Authors’ Response to the Validity of Cortisol and Galvanic Skin Responses for Measuring Student Stress During Training
%A Toohey,Shannon
%A Wray,Alisa
%A Hunter,John
%A Saadat,Soheil
%A Boysen-Osborn,Megan
%A Smart,Jonathan
%A Wiechmann,Warren
%A Pressman,Sarah D
%+ Department of Emergency Medicine, University of California, Irvine, 3800 Chapman Avenue, Suite 3200, Orange, CA, 92868, United States, 1 8055019674, stoohey@hs.uci.edu
%K augmented reality
%K AR
%K salivary cortisol
%K galvanic skin conductance
%K medical simulation
%K medical education
%D 2023

%7 18.8.2023
%9 Letter to the Editor
%J JMIR Med Educ
%G English
                
%X 
%M 37594800
%R 10.2196/50902
%U https://mededu.jmir.org/2023/1/e50902
%U https://doi.org/10.2196/50902
%U http://www.ncbi.nlm.nih.gov/pubmed/37594800

%0 Journal Article
        
%@ 2369-3762
%I JMIR Publications
%V 9
%N 
%P e45340
%T How Valid Are Cortisol and Galvanic Skin Responses in Measuring Student Stress During Training? Comment on the Psychological Effects of Simulation Training
%A Sonawane,Urvi
%A Kasetti,Pragna
%+ Imperial College London, Exhibition Road, South Kensington, London, SW7 2BX, United Kingdom, 44 020 7589 5111, urvi.sonawane13@gmail.com
%K augmented reality
%K AR
%K salivary cortisol
%K galvanic skin conductance
%K medical simulation
%K medical education
%D 2023

%7 18.8.2023
%9 Letter to the Editor
%J JMIR Med Educ
%G English
                
%X 
%M 37594784
%R 10.2196/45340
%U https://mededu.jmir.org/2023/1/e45340
%U https://doi.org/10.2196/45340
%U http://www.ncbi.nlm.nih.gov/pubmed/37594784

%0 Journal Article
        
%@ 2369-3762
%I JMIR Publications
%V 9
%N 
%P e50109
%T Authors’ Reply to: Additional Considerations for US Residency Selection After Pass/Fail USMLE Step 1. Comment on “The US Residency Selection Process After the United States Medical Licensing Examination Step 1 Pass/Fail Change: Overview for Applicants and Educators”
%A Ozair,Ahmad
%A Bhat,Vivek
%A Detchou,Donald K E
%+ Bloomberg School of Public Health, Johns Hopkins University, 615 North Wolfe Street, Baltimore, MD, 21205, United States, 1 410 516 8070, aozair1@jh.edu
%K admission
%K assessment
%K postgraduate training
%K selection
%K standardized testing
%K graduate medical education
%K medical education
%D 2023

%7 17.8.2023
%9 Letter to the Editor
%J JMIR Med Educ
%G English
                
%X 
%M 37590044
%R 10.2196/50109
%U https://mededu.jmir.org/2023/1/e50109
%U https://doi.org/10.2196/50109
%U http://www.ncbi.nlm.nih.gov/pubmed/37590044

%0 Journal Article
        
%@ 2369-3762
%I JMIR Publications
%V 9
%N 
%P e47763
%T Additional Considerations for US Residency Selection After Pass/Fail USMLE Step 1. Comment on “The US Residency Selection Process After the United States Medical Licensing Examination Step 1 Pass/Fail Change: Overview for Applicants and Educators”
%A Sow,Yacine
%A Gangal,Ameya
%A Yeung,Howa
%A Blalock,Travis
%A Stoff,Benjamin
%+ Morehouse School of Medicine, 720 Westview Drive SW, Atlanta, GA, 30310, United States, 1 678 900 3441, yacinenellysow@gmail.com
%K admission
%K assessment
%K postgraduate training
%K selection
%K standardized testing
%K USMLE
%K medical school
%K medical students
%K residency application
%K research training
%D 2023

%7 17.8.2023
%9 Letter to the Editor
%J JMIR Med Educ
%G English
                
%X 
%M 37590047
%R 10.2196/47763
%U https://mededu.jmir.org/2023/1/e47763
%U https://doi.org/10.2196/47763
%U http://www.ncbi.nlm.nih.gov/pubmed/37590047

%0 Journal Article
        
%@ 2369-3762
%I JMIR Publications
%V 9
%N 
%P e50336
%T Authors’ Reply to: Variability in Large Language Models’ Responses to Medical Licensing and Certification Examinations
%A Gilson,Aidan
%A Safranek,Conrad W
%A Huang,Thomas
%A Socrates,Vimig
%A Chi,Ling
%A Taylor,Richard Andrew
%A Chartash,David
%+ Section for Biomedical Informatics and Data Science, Yale University School of Medicine, 100 College Street, 9th Fl, New Haven, CT, 06510, United States, 1 203 737 5379, david.chartash@yale.edu
%K natural language processing
%K NLP
%K MedQA
%K generative pre-trained transformer
%K GPT
%K medical education
%K chatbot
%K artificial intelligence
%K AI
%K education technology
%K ChatGPT
%K conversational agent
%K machine learning
%K large language models
%K knowledge assessment
%D 2023

%7 13.7.2023
%9 Letter to the Editor
%J JMIR Med Educ
%G English
                
%X 
%M 37440299
%R 10.2196/50336
%U https://mededu.jmir.org/2023/1/e50336
%U https://doi.org/10.2196/50336
%U http://www.ncbi.nlm.nih.gov/pubmed/37440299

%0 Journal Article
        
%@ 2369-3762
%I JMIR Publications
%V 9
%N 
%P e48305
%T Variability in Large Language Models’ Responses to Medical Licensing and Certification Examinations. Comment on “How Does ChatGPT Perform on the United States Medical Licensing Examination? The Implications of Large Language Models for Medical Education and Knowledge Assessment”
%A Epstein,Richard H
%A Dexter,Franklin
%+ Department of Anesthesiology, Perioperative Medicine and Pain Management, University of Miami Miller School of Medicine, 1400 NW 12th Ave, Suite 4022F, Miami, FL, 33136, United States, 1 215 896 7850, repstein@med.miami.edu
%K natural language processing
%K NLP
%K MedQA
%K generative pre-trained transformer
%K GPT
%K medical education
%K chatbot
%K artificial intelligence
%K AI
%K education technology
%K ChatGPT
%K Google Bard
%K conversational agent
%K machine learning
%K large language models
%K knowledge assessment
%D 2023

%7 13.7.2023
%9 Letter to the Editor
%J JMIR Med Educ
%G English
                
%X 
%M 37440293
%R 10.2196/48305
%U https://mededu.jmir.org/2023/1/e48305
%U https://doi.org/10.2196/48305
%U http://www.ncbi.nlm.nih.gov/pubmed/37440293

%0 Journal Article
        
%@ 2369-3762
%I JMIR Publications
%V 9
%N 
%P e46876
%T ChatGPT in Clinical Toxicology
%A Sabry Abdel-Messih,Mary
%A Kamel Boulos,Maged N
%+ School of Medicine, University of Lisbon, Av Prof Egas Moniz MB, Lisbon, 1649-028, Portugal, 351 92 053 1573, mnkboulos@ieee.org
%K ChatGPT
%K clinical toxicology
%K organophosphates
%K artificial intelligence
%K AI
%K medical education
%D 2023

%7 8.3.2023
%9 Letter to the Editor
%J JMIR Med Educ
%G English
                
%X ChatGPT has recently been shown to pass the United States Medical Licensing Examination (USMLE). We tested ChatGPT (Feb 13, 2023 release) using a typical clinical toxicology case of acute organophosphate poisoning. ChatGPT fared well in answering all of our queries regarding it.
%M 36867743
%R 10.2196/46876
%U https://mededu.jmir.org/2023/1/e46876
%U https://doi.org/10.2196/46876
%U http://www.ncbi.nlm.nih.gov/pubmed/36867743

%0 Journal Article
        
%@ 2369-3762
%I JMIR Publications
%V 8
%N 1
%P e37401
%T Authors’ Reply to: Techniques to Teach Students Effectively Using Telemedicine. Comment on “Incorporating Medical Students Into Primary Care Telehealth Visits: Tutorial”
%A Balaji,Aanika
%A Clever,Sarah Lou
%+ Johns Hopkins University School of Medicine, 1600 McElderry, Baltimore, MD, 21287, United States, 1 4109555000, sclever@jhmi.edu
%K medical student
%K education
%K primary care
%K telehealth
%K video visits
%K internal medicine
%K medical education
%K teleconsultation
%K digital health
%K COVID-19
%K teaching
%K telemedicine
%K clerkships
%D 2022

%7 11.3.2022
%9 Letter to the Editor
%J JMIR Med Educ
%G English
                
%X  
%M 35191840
%R 10.2196/37401
%U https://mededu.jmir.org/2022/1/e37401
%U https://doi.org/10.2196/37401
%U http://www.ncbi.nlm.nih.gov/pubmed/35191840

%0 Journal Article
        
%@ 2369-3762
%I JMIR Publications
%V 8
%N 1
%P e30703
%T Techniques to Teach Students Effectively Using Telemedicine. Comment on “Incorporating Medical Students Into Primary Care Telehealth Visits: Tutorial”
%A Kandola,Hardeep
%A Minhas,Sonica
%+ Barts and the London School of Medicine and Dentistry, Queen Mary University of London, Turner Street, London, E1 2AD, United Kingdom, 44 02078822239, h.kandola@smd18.qmul.ac.uk
%K medical student
%K education
%K primary care
%K telehealth
%K video visits
%K internal medicine
%K medical education
%K teleconsultation
%K digital health
%K COVID-19
%K teaching
%K telemedicine
%K clerkships
%D 2022

%7 11.3.2022
%9 Letter to the Editor
%J JMIR Med Educ
%G English
                
%X 
%M 35191846
%R 10.2196/30703
%U https://mededu.jmir.org/2022/1/e30703
%U https://doi.org/10.2196/30703
%U http://www.ncbi.nlm.nih.gov/pubmed/35191846

%0 Journal Article
        
%@ 2369-3762
%I JMIR Publications
%V 7
%N 4
%P e26790
%T Innovation and Inequality: A Medical Student Perspective. Comment on "The Present and Future Applications of Technology in Adapting Medical Education Amidst the COVID-19 Pandemic"
%A Pan,Myat
%A San,Myat
%+ Cardiff University School of Medicine, Neuadd Meirionnydd, Cardiff, CF14 4YS, United Kingdom, 44 (0)29 2068 8113, panmp@cardiff.ac.uk
%K medical education
%K technology
%K coronavirus
%K medical students
%K COVID-19
%K pandemic
%K online lecture
%K virtual reality
%K education
%D 2021

%7 4.10.2021
%9 Letter to the Editor
%J JMIR Med Educ
%G English
                
%X 
%M 34081609
%R 10.2196/26790
%U https://mededu.jmir.org/2021/4/e26790
%U https://doi.org/10.2196/26790
%U http://www.ncbi.nlm.nih.gov/pubmed/34081609

%0 Journal Article
        
%@ 2369-3762
%I JMIR Publications
%V 7
%N 2
%P e29335
%T Author’s Reply to: Virtual vs Online: Insight From Medical Students. Comment on “Effectiveness of Virtual Medical Teaching During the COVID-19 Crisis: Systematic Review”
%A Wilcha,Robyn-Jenia
%+ Faculty of Biology, Medicine and Health, University of Manchester, Oxford Road, Manchester, M13 9PL, United Kingdom, 44 (0)161 306 0211, robynwilcha05@gmail.com
%K virtual teaching
%K medical student
%K medical education
%K COVID-19
%K review
%K search term
%K virus
%K pandemic
%K quarantine
%D 2021

%7 14.5.2021
%9 Letter to the Editor
%J JMIR Med Educ
%G English
                
%X 
%M 33852412
%R 10.2196/29335
%U https://mededu.jmir.org/2021/2/e29335
%U https://doi.org/10.2196/29335
%U http://www.ncbi.nlm.nih.gov/pubmed/33852412

%0 Journal Article
        
%@ 2369-3762
%I JMIR Publications
%V 7
%N 2
%P e27020
%T Virtual vs Online: Insight From Medical Students. Comment on “Effectiveness of Virtual Medical Teaching During the COVID-19 Crisis: Systematic Review”
%A Kaini,Shahil
%A Motie,Lucinda Zahrah
%+ University College London, Gower St, Bloomsbury, London, WC1E 6BT, United Kingdom, 44 020 7679 2000, shahilkaini@live.co.uk
%K virtual teaching
%K medical student
%K medical education
%K COVID-19
%K review
%K search term
%K virus
%K pandemic
%K quarantine
%D 2021

%7 14.5.2021
%9 Letter to the Editor
%J JMIR Med Educ
%G English
                
%X 
%M 33988518
%R 10.2196/27020
%U https://mededu.jmir.org/2021/2/e27020
%U https://doi.org/10.2196/27020
%U http://www.ncbi.nlm.nih.gov/pubmed/33988518

%0 Journal Article
        
%@ 2369-3762
%I JMIR Publications
%V 7
%N 1
%P e24993
%T Medical Students Respond: Question Precision and Gender Differentiation. Comment on “Understanding Medical Students’ Attitudes Toward Learning eHealth: Questionnaire Study”
%A Almohtadi,Ahmad
%A Van,Minh
%A Seyedzenouzi,Golnoush
%+ St George's University of London, Cranmer Terrace, Tooting, London, SW170RE, United Kingdom, 44 020 8672 9944, ahmad.almohtadi97@gmail.com
%K eHealth
%K medical students
%K medical education
%D 2021

%7 11.2.2021
%9 Letter to the Editor
%J JMIR Med Educ
%G English
                
%X 
%M 33570498
%R 10.2196/24993
%U https://mededu.jmir.org/2021/1/e24993
%U https://doi.org/10.2196/24993
%U http://www.ncbi.nlm.nih.gov/pubmed/33570498