Healthcare has traditionally lagged behind other industries in digital adoption. With AI, however, the tide may be changing. The healthcare industry is deploying AI solutions in the clinical setting at a pace twice that of the broader U.S. economy making healthcare a market leader.1 Unlike the healthcare industry at large, though, med schools have been slower to adopt AI, with faculty wondering in recent years whether the technology would help or hinder medical education.
Med schools are catching up, though, and it’s no surprise. Thirty percent of high-achieving digital-native Gen Z students in a national survey choosing medicine or healthcare as a major and 32% identifying medicine or healthcare as preferred employers. According to Alison Whelan, MD, Chief Academic Officer of AAMC:2
“[med schools are] just beginning to see how AI will fundamentally upend some of the ways that we educate and train our nation’s future doctors. The cautious experimentation and implementation of AI in med school offers great promise for faculty and students, but it has to be carried out with great care and clear-eyed assessments.”
Some recent AI efforts that are improving the efficiency and effectiveness of educational processes include:2
- Generating questions and answers for test prep in Ohio
- Creating evaluations for students applying for residency in Florida
- Conducting clinical visits with AI-simulated patients in Texas and Minnesota
Additional AI applications were identified in a narrative review that synthesized insights from 14 studies exploring AI integrations in medical education programs. The researchers found a wide range of applications including diagnostic assistance, curriculum redesign, enhanced assessment methods, and streamlined administrative tasks. Using AI components such as machine learning, natural language processing (NLP), neural networks, and large language models (LLMs), schools are creating an array of AI learning tools. Virtual patient simulations, automated assessments, and personalized feedback are, according to the study’s author, “transforming the way medical students, residents, and clinicians engage with information while developing their clinical skills.”
Criticisms of AI in Medical Education
Despite these promising efforts, there are criticisms. Speaking at the Association of Health Care Journalists Fall Summit in October 2026, investigative journalist and fellow in neonatal intensive care at Stanford University Eli Cahan, MD, MSc, notes one criticism that argues that students using AI in school might not build clinical reasoning skills or might lose those they have.3 He turns that criticism around, though, asking whether we actually need doctors to be good at clinical reasoning. He compares it to navigation skills. For Columbus, navigation skills were critical but now we have GPS, making navigation skills less important.3
Other participants at the Summit pushed back on that idea, including Tina Hernandez-Boussard3, PhD, a professor of biomedical informatics at Stanford, who takes a more cautious approach, warning about the risk of losing clinical skills. She shared an experience of medical residents traveling to Uganda to assist at a hospital’s cardiac ward. The hospital lost electricity so there was no AI to read EKGs. According to Dr. Hernandez-Boussard, “The residents were not able to synthesize the information in a way they were confident to make decisions on the patients.” Another Summit participant, Graciela Gonzalez-Hernandez, PhD, vice chair for research and education in computational biomedicine at Cedars-Sinai Medical Center in Los Angeles shared the same concern, saying, “We have to have the capacity of training our physicians better by removing technology. We have to make sure there are scenarios that are run with paper and pencil.”3 As with Dr. Hernandez-Boussard, Dr. Gonzalez-Hernandez does not object to clinical AI completely. She suggests AI can aid students by “adding a layer of objectivity” and balance. She further suggests that students and clinicians “should question AI when [they] get answers from a chatbot” and that AI can also be made to push back, suggesting alternatives or questions the student may not have considered.3
Conclusion
While current efforts are reshaping medical education and healthcare delivery, there are still challenges that hinder broader implementation. According to the narrative review cited above, a lack of validated curricula, limited empirical research, and infrastructure constraints are some of the hurdles to be overcome. To promote sustainable and equitable adoption, a phased, learner-centered approach tailored to the evolving demands of medical education is needed. With a third of the generation that came of age with AI planning healthcare-related careers, that might be the push that medical education needs to accelerate AI application development.
References
1. Sachin H. Jain, “AI Adoption In Healthcare Is Surging: What A New Report Reveals,” Forbes, October 21, 2025.
2. Patrick Boyle, “AI in Medical Education: 5 Ways Schools Are Employing New Tools,” AAMCNews, February 27, 2025.
3. Joyce Frieden, "Are Med Students Cheating if They Use AI?" MedPage Today, October 15, 2026.
