The Advent of Generative Language Models in Medical Education
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Abstract
Artificial intelligence (AI) and generative language models (GLMs) present significant opportunities for enhancing medical education, including the provision of realistic simulations, digital patients, personalized feedback, evaluation methods, and the elimination of language barriers. These advanced technologies can facilitate immersive learning environments and enhance medical students' educational outcomes. However, ensuring content quality, addressing biases, and managing ethical and legal concerns present obstacles. To mitigate these challenges, it is necessary to evaluate the accuracy and relevance of AI-generated content, address potential biases, and develop guidelines and policies governing the use of AI-generated content in medical education. Collaboration among educators, researchers, and practitioners is essential for developing best practices, guidelines, and transparent AI models that encourage the ethical and responsible use of GLMs and AI in medical education. By sharing information about the data used for training, obstacles encountered, and evaluation methods, developers can increase their credibility and trustworthiness within the medical community. In order to realize the full potential of AI and GLMs in medical education while mitigating potential risks and obstacles, ongoing research and interdisciplinary collaboration are necessary. By collaborating, medical professionals can ensure that these technologies are effectively and responsibly integrated, contributing to enhanced learning experiences and patient care.
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This reliance on AI for completing academic tasks without active participation in critical thinking and learning can undermine the development of essential skills such as problem-solving, writing, and analytical reasoning-competencies that are crucial in healthcare education.
“This reliance on AI for completing academic tasks without active participation in critical thinking and learning can undermine the development of essential skills such as problem-solving, writing, and analytical reasoning—competencies that are crucial in healthcare education [21].”
From Health profession students' perceptions of ChatGPT in healthcare and education: insights from a mixed-methods study (2025) · cites this paper as [21]
In “Challenges and Ethical Considerations” (page unavailable), the paper says students could use generative language models to produce essays or assignment responses, bypassing the learning process and devaluing their education. That supports the concern about relying on AI for academic tasks, but the passage does not specifically say this undermines the development of problem-solving, writing, or analytical-reasoning skills. (LLM, conf=0.84)
Experimental — accuracy not yet measuredAI plagiarism detection tools are not entirely reliable and carry a substantial risk of unjust accusations of academic dishonesty.
“AI plagiarism detection tools are not entirely reliable and carry a substantial risk of unjust accusations of academic dishonesty [188].”
From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [188]
The “Challenges and Ethical Considerations” section (page not provided) says AI-content detectors are not always accurate or reliable and pose a substantial risk of unjust accusations of academic dishonesty. It explicitly distinguishes these tools from plagiarism detectors: they are intended to identify AI-generated content, not to detect plagiarism. (LLM, conf=0.88)
Experimental — accuracy not yet measuredGenerative AI may produce biased content or lack representation of all populations.
“Moreover, concerns have arisen that generative AI may produce biased content or lack representation of all populations [8,11-15,19,20,22,24,32,35,41,42].”
From Opportunities, Challenges, and Future Directions of Generative Artificial Intelligence in Medical Education: Scoping Review (2023) · cites this paper as [14]
The Challenges and Ethical Considerations section says AI-generated content can be biased and that training data can lead AI systems to reinforce stereotypes. The paper does not specifically say that generated content lacks representation of all populations; its discussion of diverse training data does not establish that part of the claim. (LLM, conf=0.86)
Experimental — accuracy not yet measuredMany authors emphasize the need for establishing clear-cut policies on the acceptable uses of generative AI within medical education.
“Many authors emphasize the need for establishing clear-cut policies on the acceptable uses of generative AI within the realm of medical education [14,22,40,42,43].”
From Opportunities, Challenges, and Future Directions of Generative Artificial Intelligence in Medical Education: Scoping Review (2023) · cites this paper as [14]
The precision and trustworthiness of generative AI are fundamental concerns elaborated in many publications.
“The precision and trustworthiness of generative AI are fundamental concerns thoroughly elaborated in many publications [8,11-15,20,22,24,32,35,41,42].”
From Opportunities, Challenges, and Future Directions of Generative Artificial Intelligence in Medical Education: Scoping Review (2023) · cites this paper as [14]
A significant worry cited by numerous authors is the potential threat to academic integrity and possible misuse of generative AI in medical education.
“As touched upon in the preceding paragraph, a significant worry cited by numerous authors is the potential threat to academic integrity and the possible misuse of this technology [12,14,15,19,20,22,23,25,40-43].”
From Opportunities, Challenges, and Future Directions of Generative Artificial Intelligence in Medical Education: Scoping Review (2023) · cites this paper as [14]
Numerous studies underscored the potential of generative AI models to adapt to individual learners' requirements, offering a customized learning experience.
“Numerous studies underscored the potential of these AI models to adapt to individual learners' requirements, offering a customized learning experience [1,14,15,20,22,34].”
From Opportunities, Challenges, and Future Directions of Generative Artificial Intelligence in Medical Education: Scoping Review (2023) · cites this paper as [14]
Numerous articles underscored the potential of generative AI to assist with literature reviews and summarizations, but cautioned against the possibility of generative AI fabricating references and information (hallucination).
“Numerous articles underscored the potential of generative AI to assist with literature reviews and summarizations [1,12,14,20,22,25]. However, they cautioned against the possibility of generative AI fabricating references and information, a pitfall commonly referred to as “hallucination.””
From Opportunities, Challenges, and Future Directions of Generative Artificial Intelligence in Medical Education: Scoping Review (2023) · cites this paper as [14]
Several papers underscore the importance of developing new skills and competencies related to AI.
“Several papers underscore the importance of developing new skills and competencies related to AI [14,19,42,43].”
From Opportunities, Challenges, and Future Directions of Generative Artificial Intelligence in Medical Education: Scoping Review (2023) · cites this paper as [14]
Various studies raise concerns regarding model bias and the potential for perpetuating stereotypes.
“Various studies raise concerns regarding model bias and the potential for perpetuating stereotypes [14,15,19,22].”
From Opportunities, Challenges, and Future Directions of Generative Artificial Intelligence in Medical Education: Scoping Review (2023) · cites this paper as [14]
Several articles voice concerns about potential academic dishonesty related to generative AI.
“Several articles voice concerns about potential academic dishonesty [12,14,15,19,20,22,23,25,40-43].”
From Opportunities, Challenges, and Future Directions of Generative Artificial Intelligence in Medical Education: Scoping Review (2023) · cites this paper as [14]
AI might facilitate broader dissemination or more seamless communication across language barriers.
“However, not all outcomes are negative, as AI might facilitate broader dissemination or more seamless communication across language barriers [14,20,25].”
From Opportunities, Challenges, and Future Directions of Generative Artificial Intelligence in Medical Education: Scoping Review (2023) · cites this paper as [14]
A few studies outline potential scenarios in which educators or learners might interact with AI systems, such as in self-directed learning, simulation environments, and writing assistance.
“A few studies outline potential scenarios in which educators or learners might interact with AI systems, such as in self-directed learning, simulation environments, and writing assistance [8,11,12,14,19,22,23,25,28,34,35,38,40,64].”
From Opportunities, Challenges, and Future Directions of Generative Artificial Intelligence in Medical Education: Scoping Review (2023) · cites this paper as [14]
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- Authors
- Karabacak, M., Ozkara, B. B., Margetis, K., Wintermark, M., & Bisdas, S.
- Affiliations
- Mount Sinai Health System (US)
- The University of Texas MD Anderson Cancer Center (US)
- University College London Hospitals NHS Foundation Trust (GB)
- Corresponding author
- Bisdas, Sotirios
- Venue
- JMIR Medical Education
- Publisher
- JMIR Publications Inc.
- Published
- 2023-06-06
- Volume
- 9
- Pages
- e48163
- ISSN
- 2369-3762 (ISSN-L)
- PMC
- PMC10282912
- OpenAlex
- W4379508361
- Cited by
- 208
- References
- 15
- Citations by year
- 2026: 42 · 2025: 60 · 2024: 78 · 2023: 28
- FWCI
- 8.31
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR Medical Education (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Stryker
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
- Keywords
- generative language models, artificial intelligence in medical education, simulation-based medical education, digital patient, personalized feedback, language barriers, AI-generated content quality, algorithmic bias, AI ethics, responsible AI use, medical students
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