AI-Mediated Assessment of Continuing Medical Education: The Case-based Learning Intelligence Credit System (CLICS)...

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.