Early discussions of generative language models in medical education #mededu emphasized their promise for simulation, digital patients, individualized feedback, learner assessment, health information dissemination, research support, and translation, while also warning about bias, privacy, academic integrity, misinformation, legal ambiguity, and unequal access. Since that first wave, generative AI has moved from novelty to routine exposure for learners, educators, researchers, and institutions. medical education #mededu therefore needs a more mature framework than a catalog of opportunities and risks. This viewpoint argues that the next phase should be organized around educational entrustment: determining which functions can be delegated to AI systems, under what conditions, with what human supervision, and with what evidence of benefit. Building on recent proposals to apply entrustment to AI in health professions education, we operationalize the concept into a graduated, function-level model that specifies which educational functions may be delegated; at what stakes; and with what oversight, assessment, and governance. We classify use cases by educational stakes and AI autonomy and outline implications for assessment redesign, curriculum development, faculty capability, cognitive autonomy, equity, and institutional governance. The central challenge is whether medical schools can integrate these tools in ways that preserve clinical reasoning, professional identity, accountability, and fairness. The next generation of research should move beyond model performance on examinations and evaluate how AI changes learning, judgment, behavior, and patient care.

Continuing medical education #mededu (CME) and continuing professional development (CPD) systems have traditionally relied on time-based credit allocation, using participation duration as a proxy for professional learning. Although administratively simple and scalable, this model does not reliably demonstrate whether physicians have engaged in meaningful learning, improved clinical reasoning, or critically appraised evidence. The emergence of generative AI creates an opportunity to rethink how physician learning is documented, assessed, and credited. This viewpoint proposes the Case-based Learning Intelligence Credit System (CLICS), a conceptual framework for translating AI-mediated clinical learning interactions into auditable evidence of reasoning-related engagement that could support CME/CPD credit. CLICS introduces the professional learning episode (PLE) as the basic unit of creditable learning: a coherent AI-mediated interaction demonstrating a clinically meaningful problem, reasoning development through iterative inquiry, contextual or evidentiary integration, and reflective synthesis. PLEs are evaluated using the proposed Practice Intelligence Score-7 (PIS-7) rubric, subject to human calibration and oversight; the rubric assesses observable reasoning behavior within the episode rather than the AI’s answer, and qualifying PLEs may be translated into CME/CPD credit through threshold-based, human-auditable conversion rules. CLICS is not intended to replace traditional CME but to extend it as an optional, evidence-generating pathway for personalized, practice-embedded professional development. Its implementation requires iterative validation, stratified human audit, privacy-by-design architecture, antigaming controls, bias monitoring, and professional oversight. If validated, CLICS may enable identification of domain-specific areas for improvement and support personalized, adaptive learning pathways.

Background: Virtual reality (VR) technologies have demonstrated therapeutic usefulness across a variety of healthcare settings. However, graduate medical education #mededu (GME) trainee perspectives on VR acceptability and usability are limited. The…

Date Submitted: Apr 21, 2025. Open Peer Review Period: Apr 22, 2025 - Jun 17, 2025.

Background: As an emerging delivery mode of education, online continuing medical education #mededu (CME) increases the accessibility of high-quality medical training for professionals and students in China. Guoyuan (meaning “nationwide” in Chinese) is an online CME platform delivered via a mobile app and operated by the National Telemedicine Center of China since 2018, serving as an illustrative case of mobile online CME implementation. Objective: We identified trends in the adoption and usage of the Guoyuan mobile online CME platform from 2018 to 2023 and provided evidence for the application and optimization of online CME. Methods: We analyzed yearly usage data of the Guoyuan mobile app (The First Affiliated Hospital of Zhengzhou University) in 2018-2023 and collected surveys on the satisfaction and recognition of competency enhancement in online CME in each connected hospital in 2023. Using the IBM SPSS, the nonparametric Kruskal-Wallis test was used to compare attendance across different disciplines, followed by post hoc pairwise comparisons for course types with significant differences and ordinal logistic regression analysis to examine factors influencing satisfaction with the online CME system and perceived competency enhancement among invited doctors. Results: From 2018 to 2023, Guoyuan had 94,537 registered trainees, 1672 published course videos, and 1,878,437 attendances. Attendance was higher for courses in ophthalmology, otolaryngology, and pathology than in other disciplines (median attendance 610, IQR 105-2055 vs 283, IQR 106-690 participants). Based on a sample size of 245 participants, ordinal regression analysis showed that discipline category, professional title, and working years significantly influenced satisfaction. General practitioners showed lower overall satisfaction than internal medicine doctors (odds ratio [OR] 0.323, 95% CI 0.110-0.948; OR 0.251, 95% CI 0.087-0.729; and OR 0.196, 95% CI 0.066-0.585; =.04; =.01=.003). Junior titles reported higher audio-visual clarity (OR 3.151, 95% CI 1.178-8.427; .02) and process satisfaction (OR 4.939, 95% CI 1.674-14.576; =.004). More experienced doctors had higher system usability (OR 1.102, 95% CI 1.012-1.200; =.03) and process satisfaction (OR 1.141, 95% CI 1.044-1.247; =.003). Recognition of online CME’s benefits was influenced by multiple factors. Greater clinical experience positively predicted recognition of clinical use (OR 1.106, 95% CI 1.004-1.218; =.04), while an inverse association was observed with age (OR 0.894, 95% CI 0.802-0.996; =.04). For research-related benefits, positive predictors included discipline category in obstetrics and gynecology compared to internal medicine (OR 6.217, 95% CI 1.236-31.258; =.03) and junior professional title (OR 3.791, 95% CI 1.231-11.673; =.02), whereas intensive care unit was a negative predictor compared to internal medicine (OR 0.111, 95% CI 0.014-0.893; =.04). Conclusions: Online mobile CME platforms have gained widespread adoption among medical professionals in China, particularly after the #covid19 outbreak. However, substantial disciplinary disparities in course availability and user experience persist, indicating the need for further optimization of course design and software interaction.

Date Submitted: Dec 15, 2024. Open Peer Review Period: Dec 16, 2024 - Feb 10, 2025.