Editorial
Author
1
Clinical Professor, Faculty of Medicine, The University of British Columbia, Vancouver, Canada
2
Clinical Professor, Mashhad University of Medical Sciences, Mashhad, Iran
3
Editor-in-Chief, Future of Medical Education Journal
Abstract
Medical education has evolved through successive accelerants—from writing and the printing press to the internet—each expanding the speed, reach, and fidelity of learning. This editorial argues that artificial intelligence (AI) is the next inflection point in that evolutionary arc. We synthesize recent reviews across health professions and propose an educator-led governance roadmap. We also conduct a rapid bibliometric scan (titles containing “AI” and “education”) in PubMed and OpenAlex (2023–2025) to illustrate early diffusion dynamics.
Across the literature, near-term opportunities cluster around three domains: 1) personalization at scale via adaptive practice, formative feedback, and multilingual access; 2) workflow augmentation, including generation of learning materials, items, and rubrics; and 3) high-fidelity practice using simulation and conversational agents for clinical reasoning and communication. Evidence for short-term knowledge and skills gains is promising but fragile, as most studies are small, single-site, and of brief duration, and rarely assess downstream competence, equity, or safety. Salient risks include hallucinations and bias, privacy and confidentiality concerns, threats to assessment integrity, and potential erosion of learner agency without careful scaffolding.
Our bibliometric indicator retrieved 419 PubMed records and 1,745 OpenAlex author-affiliation entries, with contributions led by high-income countries yet rapidly emerging across middle- and low-income settings—suggesting a shortening lag from innovation to global uptake. We recommend human-in-the-loop use, transparent disclosure, data safeguards, redesign of assessment processes, faculty capacity-building, and rigorous evaluation with equity and safety endpoints. If aligned with learning science and ethical governance, AI can compress discovery-to-practice cycles and narrow disparities—enhancing, rather than replacing, the human relationships at the heart of medical training.
Keywords
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