JMIR Medical Education

Continuing medical education (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.

The integration of AI into health care is increasingly shaping clinical practice and decision-making. Beyond technical performance, AI has implications for professional roles, clinical reasoning, and responsibility. Understanding how medical students and clinicians perceive AI, how these perceptions relate to professional identity concerns, and how clinicians evaluate AI-supported decisions in different clinical contexts is therefore important for both implementation and medical education.

Early discussions of generative language models in medical education 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 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.

The continuous development of AI presents unprecedented opportunities for public health. This development prompts educators and researchers to consider how AI can be applied to and integrated into a rapidly evolving global landscape in which AI is reshaping health systems. In turn, this generates an urgent need for structured proposals that bridge theory and practice. In this viewpoint, we present perspectives and proposals on AI applications in public health. We operationalize AI literacy as a core public health competency, defined through knowledge, skills, and attitudes and scaled across professional roles. For education, we describe how AI can support learners, teachers, and organizations through personalized learning experiences, dynamic scenarios, immersive simulations, content preparation, and communication strategies. For research, we analyze how AI can be embedded as a transversal enabler supporting the World Health Organization’s essential public health functions. We emphasize that AI should enhance, rather than replace, human capabilities and propose that AI literacy should be recognized as a core public health competency. We suggest that harnessing AI for public health education and research is, therefore, less a technological challenge than a collective responsibility shared by educators, researchers, and institutions.

Our research group previously developed a virtual reality (VR) training module for radiopharmaceutical administration, focusing on procedural skills and patient interactions. The module’s educational effectiveness was validated with objective measures, such as electroencephalography and mood assessments.

Extended reality technologies, including virtual reality (VR), augmented reality, and mixed reality, are increasingly used in medical education to create immersive and interactive learning environments. As these modalities expand, validated instruments are needed to measure learners’ experiences accurately. The Immersive Technology Evaluation Measure (ITEM) is a multidomain questionnaire assessing immersion, motivation, cognitive load, usability, and debriefing. Although cognitive interviewing informed its original development, less is known about how ITEM questions function when used in a different linguistic and educational context.

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AI is being increasingly integrated into health care and medical education. Although AI literacy is considered an essential competency for future physicians and educators, limited evidence exists regarding how perceived benefits, perceived risks, and AI literacy jointly influence AI use intention among different learner groups.

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Large language model (LLM)–powered virtual standardized patients (VSPs) enable scalable clinical skills practice, but the validity of AI-generated scores relative to faculty ratings remains unclear.

Case-based learning (CBL) promotes transfer of knowledge to practice, yet occupational health CBL must also develop exposure assessment and epidemiologic thinking. Static, single-session cases that disclose all information upfront can truncate iterative reasoning and encourage premature diagnostic closure. Generative AI (GenAI) can support the efficient development of high-fidelity, progressively disclosed cases, but hallucination risks require strict quality control.

Most large language models (LLMs) have achieved passing scores on medical licensing examinations. However, most evaluations focus on single-question accuracy, overlooking performance on multistep patient management scenarios, such as making a diagnosis followed by a treatment plan. It is unclear if LLMs can maintain high performance on complete clinical cases.