Large Language Models in Medical Education: Opportunities, Challenges, and Future Directions
By Alaa A. Abd-alrazaq, Rawan AlSaad, Dari Alhuwail, Arfan Ahmed, P. Healy, Syed Latifi, S. Aziz, Rafat R. Damseh, Sadam Alabed Alrazak, Javaid Sheikh. JMIR Medical Education, 2023
570 citations recorded by the source corpus.
The integration of large language models (LLMs), such as those in the Generative Pre-trained Transformers (GPT) series, into medical education has the potential to transform learning experiences for students and elevate their knowledge, skills, and competence. Drawing on a wealth of professional and academic experience, we propose that LLMs hold promise for revolutionizing medical curriculum development, teaching methodologies, personalized study plans and learning materials, student assessments, and more. However, we also critically examine the challenges that such integration might pose by addressing issues of algorithmic bias, overreliance, plagiarism, misinformation, inequity, privacy, and copyright concerns in medical education. As we navigate the shift from an information-driven educational paradigm to an artificial intelligence (AI)–driven educational paradigm, we argue that it is paramount to understand both the potential and the pitfalls of LLMs in medical education. This paper thus offers our perspective on the opportunities and challenges of using LLMs in this context. We believe that the insights gleaned from this analysis will serve as a foundation for future recommendations and best practices in the field, fostering the responsible and effective use of AI technologies in medical education.
Subfields: Health Informatics, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/5be9a64df5f8d7e5a33fcc2c7bdfcde1fbbd085a
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In Health Informatics
In Radiology, Nuclear Medicine and Imaging
Citations in the corpus
- The effects of over-reliance on AI dialogue systems on students' cognitive abilities: a systematic review (2024, background): “Ethical concerns have been raised regarding the potential for AI hallucinations (Gao et al., 2022), algorithmic bias (Alrazaq et al., 2023), plagiarism (De Angelis et al., 2023), privacy issues (Dempere et al., 2023), transparency concerns (Dergaa et al., 2023), and over-reliance (Koos & Wachsmann,…”
- Opportunities, Challenges, and Future Directions of Generative Artificial Intelligence in Medical Education: Scoping Review (2023, background): “[22] Cheating on examinations “Student dependency on the language model may also propagate academic dishonesty or ‘cheating.”
- The ethics of ChatGPT in medicine and healthcare: a systematic review on Large Language Models (LLMs) (2024, background): “While we recognize that these issues are affected by the use of LLMs in significant ways 6,35,36 these, challenges are not specific to health-related applications.”
- Enhancing patient care with AI agents: integrating advanced chatbot technologies for improved healthcare delivery (2026, unknown): “Leverages a broader range of data sources, including user-generated content, reviews, and real-time patient feedback, which allows for more dynamic, context-aware responses based on the latest information Abd-Alrazaq et al. [50], Macdonald et al. [51].”
- Testing for LLM response differences: the case of a composite null consisting of semantically irrelevant query perturbations (2025, unknown): “…et al., 2024; Team et al., 2023) and similar improvements in specialized domains such as medicine (Thirunavukarasu et al., 2023; Nori et al., 2023; Abd-Alrazaq et al., 2023), radiology (D’Antonoli et al., 2024; Kim et al., 2024), law (Sun, 2023; Siino et al., 2025), etc. (Lo, 2023; Rahman et…”