Can AI teach medicine?

Dear Editor

In my work as an educational fellow engaging with students from multiple universities, it has become increasingly evident to me that it is not a matter of whether Artificial Intelligence (AI) can be used in the undergraduate setting, but how curricula are going to adapt to its benefits and risks.

Artificial intelligence is a broad and poorly defined term (1), and many training clinicians are likely already utilising AI in the form of question banks with adaptive learning systems (2), possibly without even realising it. Early studies have even indicated that a Large Language Model AI is capable of passing the United States Medical Licensing Exam without specialised training.

Is it surprising then, that our medical students are increasingly utilising AI technology to revise for exams, write essays and even practice for OSCEs?

Data from the Digital Education Council suggests that 86% of all students use AI in their studies. However, comparable research on medical students reports a lower adoption rate, ranging between 11.3% and 20.4% (3)(4). This discrepancy raises important questions: Is this due to outdated data in a fast-evolving field, or does it stem from medical students’ concerns about being accused of 'cheating'?

This is a theme constantly raised by my own students, who feel that there is not clear guidance on the ethics of using AI in their learning.

Is using a large language model (LLM) such as ChatGPT to refine an essay fundamentally different from asking a friend or colleague to review it? Is generating reflective prompts via AI akin to receiving guidance from a supervisor?

Although some uses seem clear cut (adaptive learning systems are widely accepted whereas utilising ChatGPT to write a whole reflective essay would almost certainly be frowned upon), there are a myriad of uses that fall into a grey area.

Keeping pace with this rapidly evolving landscape may be challenging, but principles such as those outlined by D'Souza et al. (5)—which emphasize transparency, bias mitigation, and content validation—can serve as a foundation for establishing clear guidance and supporting students in their responsible use of AI.

(1) Artificial intelligence in medicine. Hamet P, Tremblay J. Metabolism. 2017;69S:0–40. doi: 10.1016/j.metabol.2017.01.011
(2) Efficacy of adaptive e-learning for health professionals and students: a systematic review and meta-analysis. Fontaine G, Cossette S, Maheu-Cadotte MA, et al. BMJ Open. 2019;9:0. doi: 10.1136/bmjopen-2018-02525
(3) Sallam M, Salim NA, Barakat M, Al-Mahzoum K, Al-Tammemi AB, Malaeb D, Hallit R, Hallit S. Assessing Health Students' Attitudes and Usage of ChatGPT in Jordan: Validation Study. JMIR Med Educ. 2023 Sep 5;9:e48254. doi: 10.2196/48254. PMID: 37578934; PMCID: PMC10509747.
(4) Alkhaaldi, Saif M I et al. “Medical Student Experiences and Perceptions of ChatGPT and Artificial Intelligence: Cross-Sectional Study.” JMIR medical education vol. 9 e51302. 22 Dec. 2023, doi:10.2196/51302
(5) Franco D'Souza R, Mathew M, Mishra V, Surapaneni KM. Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education. Med Educ Online. 2024 Dec 31;29(1):2330250. doi: 10.1080/10872981.2024.2330250. Epub 2024 Apr 3. PMID: 38566608; PMCID: PMC10993743.

Dear Editor

I found this article fascinating in its discussion of AI as a tool during clinical trianing. I was recently fortunate to attend a seminar hosted by Imperial College on the role of AI in teaching. It brought together educators from a range of disciplines; mathematics, chemistry, engineering, and, of course, the medical sciences and clinical medicine. A key takeaway was that this rapidly evolving field is as much about understanding how students use AI as it is about how we teach with it.

What struck me most was just how inventive students have become. Far beyond using AI to summarise articles or explain concepts, they’re creating podcasts, generating realistic clinical scenarios, and scripting role-play exercises to develop their communication skills. They’re not just passive users, they're co-creators in their own learning journeys.

This raises urgent questions for us as medical educators. How do we keep up? If students are already experimenting with AI in ways we’re only beginning to understand, how can we guide their learning responsibly? More importantly, how do we set boundaries—when should AI be welcomed as a learning tool, and when might it short-circuit essential clinical reasoning?

As the BMJ article rightly notes, AI lacks the nuanced perception of a tutor who notices confusion in a student’s facial expression rather than their words. And yet, it offers possibilities beyond the reach of any individual educator: instant feedback, scalable exposure to rare cases, and the ability to simulate complex clinical scenarios on demand. It has endless ways of explaining concepts and can always respond to that familiar student prompt: “Can you explain that a different way?”

So perhaps the more important question isn’t, “Can AI teach?” but “How should we teach with AI?” Used wisely, it could be a powerful educational ally. But left unchecked or unexamined, it risks replacing mentorship with automation. As one of the mathematicians at the seminar suggested, perhaps it’s time we ask students to submit the prompts they used with AI alongside their work, so we can better understand not just what they learned, but how they arrived there.

Ultimately, we must ensure students become not only competent users of AI but critically reflective ones. AI may support the transmission of knowledge, but the cultivation of judgment, empathy, and professionalism will always remain our responsibility.