@Article{info:doi/10.2196/70420,
author="Quon, Stephanie
and Zhou, Sarah",
title="Enhancing AI-Driven Medical Translations: Considerations for Language Concordance",
journal="JMIR Med Educ",
year="2025",
month="Apr",
day="11",
volume="11",
pages="e70420",
keywords="letter to the editor",
keywords="ChatGPT",
keywords="AI",
keywords="artificial intelligence",
keywords="language",
keywords="translation",
keywords="health care disparity",
keywords="natural language model",
keywords="survey",
keywords="patient education",
keywords="accessibility",
keywords="preference",
keywords="human language",
keywords="communication",
keywords="language-concordant care",
doi="10.2196/70420",
url="https://mededu.jmir.org/2025/1/e70420"
}


@Article{info:doi/10.2196/71721,
author="Teng, Joyce
and Novoa, Andres Roberto
and Aleshin, Alexandrovna Maria
and Lester, Jenna
and Seiger, Kira
and Dzuali, Fiatsogbe
and Daneshjou, Roxana",
title="Authors' Reply: Enhancing AI-Driven Medical Translations: Considerations for Language Concordance",
journal="JMIR Med Educ",
year="2025",
month="Apr",
day="11",
volume="11",
pages="e71721",
keywords="ChatGPT",
keywords="artificial intelligence",
keywords="language",
keywords="translation",
keywords="health care disparity",
keywords="natural language model",
keywords="survey",
keywords="patient education",
keywords="accessibility",
keywords="preference",
keywords="human language",
keywords="communication",
keywords="language-concordant care",
doi="10.2196/71721",
url="https://mededu.jmir.org/2025/1/e71721"
}


@Article{info:doi/10.2196/72998,
author="Zhang, Manlin
and Zhao, Tianyu",
title="Citation Accuracy Challenges Posed by Large Language Models",
journal="JMIR Med Educ",
year="2025",
month="Apr",
day="2",
volume="11",
pages="e72998",
keywords="chatGPT",
keywords="medical education",
keywords="Saudi Arabia",
keywords="perceptions",
keywords="knowledge",
keywords="medical students",
keywords="faculty",
keywords="chatbot",
keywords="qualitative study",
keywords="artificial intelligence",
keywords="AI",
keywords="AI-based tools",
keywords="universities",
keywords="thematic analysis",
keywords="learning",
keywords="satisfaction",
keywords="LLM",
keywords="large language model",
doi="10.2196/72998",
url="https://mededu.jmir.org/2025/1/e72998"
}


@Article{info:doi/10.2196/73698,
author="Temsah, Mohamad-Hani
and Al-Eyadhy, Ayman
and Jamal, Amr
and Alhasan, Khalid
and Malki, H. Khalid",
title="Authors' Reply: Citation Accuracy Challenges Posed by Large Language Models",
journal="JMIR Med Educ",
year="2025",
month="Apr",
day="2",
volume="11",
pages="e73698",
keywords="ChatGPT",
keywords="Gemini",
keywords="DeepSeek",
keywords="medical education",
keywords="AI",
keywords="artificial intelligence",
keywords="Saudi Arabia",
keywords="perceptions",
keywords="medical students",
keywords="faculty",
keywords="LLM",
keywords="chatbot",
keywords="qualitative study",
keywords="thematic analysis",
keywords="satisfaction",
keywords="RAG retrieval-augmented generation",
doi="10.2196/73698",
url="https://mededu.jmir.org/2025/1/e73698"
}


@Article{info:doi/10.2196/72336,
author="Bland, Tyler",
title="Author's Reply: Examining Multimodal AI Resources in Medical Education: The Role of Immersion, Motivation, and Fidelity in AI Narrative Learning",
journal="JMIR Med Educ",
year="2025",
month="Mar",
day="18",
volume="11",
pages="e72336",
keywords="artificial intelligence",
keywords="cinematic clinical narrative",
keywords="cinemeducation",
keywords="medical education",
keywords="narrative learning",
keywords="pharmacology",
keywords="AI",
keywords="medical students",
keywords="preclinical education",
keywords="long-term retention",
keywords="AI tools",
keywords="GPT-4",
keywords="image",
keywords="applicability",
keywords="CCN",
doi="10.2196/72336",
url="https://mededu.jmir.org/2025/1/e72336"
}


@Article{info:doi/10.2196/72190,
author="Jacobs, Chris",
title="Examining Multimodal AI Resources in Medical Education: The Role of Immersion, Motivation, and Fidelity in AI Narrative Learning",
journal="JMIR Med Educ",
year="2025",
month="Mar",
day="18",
volume="11",
pages="e72190",
keywords="artificial intelligence",
keywords="cinematic clinical narrative",
keywords="cinemeducation",
keywords="medical education",
keywords="narrative learning",
keywords="AI",
keywords="medical students",
keywords="preclinical education",
keywords="long-term retention",
keywords="pharmacology",
keywords="AI tools",
keywords="GPT-4",
keywords="image",
keywords="applicability",
keywords="CCN",
doi="10.2196/72190",
url="https://mededu.jmir.org/2025/1/e72190"
}


@Article{info:doi/10.2196/56117,
author="Sekhar, C. Tejas
and Nayak, R. Yash
and Abdoler, A. Emily",
title="A Use Case for Generative AI in Medical Education",
journal="JMIR Med Educ",
year="2024",
month="Jun",
day="7",
volume="10",
pages="e56117",
keywords="medical education",
keywords="med ed",
keywords="generative artificial intelligence",
keywords="artificial intelligence",
keywords="GAI",
keywords="AI",
keywords="Anki",
keywords="flashcard",
keywords="undergraduate medical education",
keywords="UME",
doi="10.2196/56117",
url="https://mededu.jmir.org/2024/1/e56117"
}


@Article{info:doi/10.2196/58370,
author="Pendergrast, Tricia
and Chalmers, Zachary",
title="Authors' Reply: A Use Case for Generative AI in Medical Education",
journal="JMIR Med Educ",
year="2024",
month="Jun",
day="7",
volume="10",
pages="e58370",
keywords="ChatGPT",
keywords="undergraduate medical education",
keywords="large language models",
doi="10.2196/58370",
url="https://mededu.jmir.org/2024/1/e58370"
}


@Article{info:doi/10.2196/58743,
author="De Martinis, Massimo
and Ginaldi, Lia",
title="Digital Skills to Improve Levels of Care and Renew Health Care Professions",
journal="JMIR Med Educ",
year="2024",
month="May",
day="1",
volume="10",
pages="e58743",
keywords="digital competence",
keywords="telehealth",
keywords="nursing",
keywords="health care workforce",
keywords="health care professionals",
keywords="informatics",
keywords="education",
keywords="curriculum",
keywords="interdisciplinary education",
keywords="health care education",
doi="10.2196/58743",
url="https://mededu.jmir.org/2024/1/e58743"
}


@Article{info:doi/10.2196/57696,
author="Dsouza, Maria Jeanne",
title="A Student's Viewpoint on ChatGPT Use and Automation Bias in Medical Education",
journal="JMIR Med Educ",
year="2024",
month="Apr",
day="15",
volume="10",
pages="e57696",
keywords="AI",
keywords="artificial intelligence",
keywords="ChatGPT",
keywords="medical education",
doi="10.2196/57696",
url="https://mededu.jmir.org/2024/1/e57696"
}


@Article{info:doi/10.2196/50902,
author="Toohey, Shannon
and Wray, Alisa
and Hunter, John
and Saadat, Soheil
and Boysen-Osborn, Megan
and Smart, Jonathan
and Wiechmann, Warren
and Pressman, D. Sarah",
title="Authors' Response to the Validity of Cortisol and Galvanic Skin Responses for Measuring Student Stress During Training",
journal="JMIR Med Educ",
year="2023",
month="Aug",
day="18",
volume="9",
pages="e50902",
keywords="augmented reality",
keywords="AR",
keywords="salivary cortisol",
keywords="galvanic skin conductance",
keywords="medical simulation",
keywords="medical education",
doi="10.2196/50902",
url="https://mededu.jmir.org/2023/1/e50902",
url="http://www.ncbi.nlm.nih.gov/pubmed/37594800"
}


@Article{info:doi/10.2196/45340,
author="Sonawane, Urvi
and Kasetti, Pragna",
title="How Valid Are Cortisol and Galvanic Skin Responses in Measuring Student Stress During Training? Comment on the Psychological Effects of Simulation Training",
journal="JMIR Med Educ",
year="2023",
month="Aug",
day="18",
volume="9",
pages="e45340",
keywords="augmented reality",
keywords="AR",
keywords="salivary cortisol",
keywords="galvanic skin conductance",
keywords="medical simulation",
keywords="medical education",
doi="10.2196/45340",
url="https://mededu.jmir.org/2023/1/e45340",
url="http://www.ncbi.nlm.nih.gov/pubmed/37594784"
}


@Article{info:doi/10.2196/50109,
author="Ozair, Ahmad
and Bhat, Vivek
and Detchou, E. Donald K.",
title="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''",
journal="JMIR Med Educ",
year="2023",
month="Aug",
day="17",
volume="9",
pages="e50109",
keywords="admission",
keywords="assessment",
keywords="postgraduate training",
keywords="selection",
keywords="standardized testing",
keywords="graduate medical education",
keywords="medical education",
doi="10.2196/50109",
url="https://mededu.jmir.org/2023/1/e50109",
url="http://www.ncbi.nlm.nih.gov/pubmed/37590044"
}


@Article{info:doi/10.2196/47763,
author="Sow, Yacine
and Gangal, Ameya
and Yeung, Howa
and Blalock, Travis
and Stoff, Benjamin",
title="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''",
journal="JMIR Med Educ",
year="2023",
month="Aug",
day="17",
volume="9",
pages="e47763",
keywords="admission",
keywords="assessment",
keywords="postgraduate training",
keywords="selection",
keywords="standardized testing",
keywords="USMLE",
keywords="medical school",
keywords="medical students",
keywords="residency application",
keywords="research training",
doi="10.2196/47763",
url="https://mededu.jmir.org/2023/1/e47763",
url="http://www.ncbi.nlm.nih.gov/pubmed/37590047"
}


@Article{info:doi/10.2196/50336,
author="Gilson, Aidan
and Safranek, W. Conrad
and Huang, Thomas
and Socrates, Vimig
and Chi, Ling
and Taylor, Andrew Richard
and Chartash, David",
title="Authors' Reply to: Variability in Large Language Models' Responses to Medical Licensing and Certification Examinations",
journal="JMIR Med Educ",
year="2023",
month="Jul",
day="13",
volume="9",
pages="e50336",
keywords="natural language processing",
keywords="NLP",
keywords="MedQA",
keywords="generative pre-trained transformer",
keywords="GPT",
keywords="medical education",
keywords="chatbot",
keywords="artificial intelligence",
keywords="AI",
keywords="education technology",
keywords="ChatGPT",
keywords="conversational agent",
keywords="machine learning",
keywords="large language models",
keywords="knowledge assessment",
doi="10.2196/50336",
url="https://mededu.jmir.org/2023/1/e50336",
url="http://www.ncbi.nlm.nih.gov/pubmed/37440299"
}


@Article{info:doi/10.2196/48305,
author="Epstein, H. Richard
and Dexter, Franklin",
title="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''",
journal="JMIR Med Educ",
year="2023",
month="Jul",
day="13",
volume="9",
pages="e48305",
keywords="natural language processing",
keywords="NLP",
keywords="MedQA",
keywords="generative pre-trained transformer",
keywords="GPT",
keywords="medical education",
keywords="chatbot",
keywords="artificial intelligence",
keywords="AI",
keywords="education technology",
keywords="ChatGPT",
keywords="Google Bard",
keywords="conversational agent",
keywords="machine learning",
keywords="large language models",
keywords="knowledge assessment",
doi="10.2196/48305",
url="https://mededu.jmir.org/2023/1/e48305",
url="http://www.ncbi.nlm.nih.gov/pubmed/37440293"
}


@Article{info:doi/10.2196/46876,
author="Sabry Abdel-Messih, Mary
and Kamel Boulos, N. Maged",
title="ChatGPT in Clinical Toxicology",
journal="JMIR Med Educ",
year="2023",
month="Mar",
day="8",
volume="9",
pages="e46876",
keywords="ChatGPT",
keywords="clinical toxicology",
keywords="organophosphates",
keywords="artificial intelligence",
keywords="AI",
keywords="medical education",
doi="10.2196/46876",
url="https://mededu.jmir.org/2023/1/e46876",
url="http://www.ncbi.nlm.nih.gov/pubmed/36867743"
}


@Article{info:doi/10.2196/37401,
author="Balaji, Aanika
and Clever, Lou Sarah",
title="Authors' Reply to: Techniques to Teach Students Effectively Using Telemedicine. Comment on ``Incorporating Medical Students Into Primary Care Telehealth Visits: Tutorial''",
journal="JMIR Med Educ",
year="2022",
month="Mar",
day="11",
volume="8",
number="1",
pages="e37401",
keywords="medical student",
keywords="education",
keywords="primary care",
keywords="telehealth",
keywords="video visits",
keywords="internal medicine",
keywords="medical education",
keywords="teleconsultation",
keywords="digital health",
keywords="COVID-19",
keywords="teaching",
keywords="telemedicine",
keywords="clerkships",
doi="10.2196/37401",
url="https://mededu.jmir.org/2022/1/e37401",
url="http://www.ncbi.nlm.nih.gov/pubmed/35191840"
}


@Article{info:doi/10.2196/30703,
author="Kandola, Hardeep
and Minhas, Sonica",
title="Techniques to Teach Students Effectively Using Telemedicine. Comment on ``Incorporating Medical Students Into Primary Care Telehealth Visits: Tutorial''",
journal="JMIR Med Educ",
year="2022",
month="Mar",
day="11",
volume="8",
number="1",
pages="e30703",
keywords="medical student",
keywords="education",
keywords="primary care",
keywords="telehealth",
keywords="video visits",
keywords="internal medicine",
keywords="medical education",
keywords="teleconsultation",
keywords="digital health",
keywords="COVID-19",
keywords="teaching",
keywords="telemedicine",
keywords="clerkships",
doi="10.2196/30703",
url="https://mededu.jmir.org/2022/1/e30703",
url="http://www.ncbi.nlm.nih.gov/pubmed/35191846"
}


@Article{info:doi/10.2196/26790,
author="Pan, Myat
and San, Myat",
title="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''",
journal="JMIR Med Educ",
year="2021",
month="Oct",
day="4",
volume="7",
number="4",
pages="e26790",
keywords="medical education",
keywords="technology",
keywords="coronavirus",
keywords="medical students",
keywords="COVID-19",
keywords="pandemic",
keywords="online lecture",
keywords="virtual reality",
keywords="education",
doi="10.2196/26790",
url="https://mededu.jmir.org/2021/4/e26790",
url="http://www.ncbi.nlm.nih.gov/pubmed/34081609"
}


@Article{info:doi/10.2196/29335,
author="Wilcha, Robyn-Jenia",
title="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''",
journal="JMIR Med Educ",
year="2021",
month="May",
day="14",
volume="7",
number="2",
pages="e29335",
keywords="virtual teaching",
keywords="medical student",
keywords="medical education",
keywords="COVID-19",
keywords="review",
keywords="search term",
keywords="virus",
keywords="pandemic",
keywords="quarantine",
doi="10.2196/29335",
url="https://mededu.jmir.org/2021/2/e29335",
url="http://www.ncbi.nlm.nih.gov/pubmed/33852412"
}


@Article{info:doi/10.2196/27020,
author="Kaini, Shahil
and Motie, Zahrah Lucinda",
title="Virtual vs Online: Insight From Medical Students. Comment on ``Effectiveness of Virtual Medical Teaching During the COVID-19 Crisis: Systematic Review''",
journal="JMIR Med Educ",
year="2021",
month="May",
day="14",
volume="7",
number="2",
pages="e27020",
keywords="virtual teaching",
keywords="medical student",
keywords="medical education",
keywords="COVID-19",
keywords="review",
keywords="search term",
keywords="virus",
keywords="pandemic",
keywords="quarantine",
doi="10.2196/27020",
url="https://mededu.jmir.org/2021/2/e27020",
url="http://www.ncbi.nlm.nih.gov/pubmed/33988518"
}


@Article{info:doi/10.2196/24993,
author="Almohtadi, Ahmad
and Van, Minh
and Seyedzenouzi, Golnoush",
title="Medical Students Respond: Question Precision and Gender Differentiation. Comment on ``Understanding Medical Students' Attitudes Toward Learning eHealth: Questionnaire Study''",
journal="JMIR Med Educ",
year="2021",
month="Feb",
day="11",
volume="7",
number="1",
pages="e24993",
keywords="eHealth",
keywords="medical students",
keywords="medical education",
doi="10.2196/24993",
url="https://mededu.jmir.org/2021/1/e24993",
url="http://www.ncbi.nlm.nih.gov/pubmed/33570498"
}