JMIR Publications

ABSTRACT

Continuing medical education (CME) and continuing professional development (CPD) systems have traditionally relied on time-based credit allocation, in which participation duration is used as a proxy for professional learning. Although this model is administratively simple and globally scalable, it does not reliably demonstrate whether physicians have engaged in meaningful learning, improved clinical reasoning, critically appraised evidence, or translated new knowledge into practice. The rapid emergence of generative artificial intelligence (AI), particularly conversational large language models, creates an opportunity to rethink how physician learning is documented, assessed, and credited. This viewpoint proposes the Clinical Learning Intelligence Credit System (CLICS), a conceptual framework for transforming AI-mediated clinical learning interactions into auditable evidence of cognitive engagement and CME/CPD credit. CLICS is grounded in the premise that clinically meaningful learning increasingly occurs through real-time, problem-centered interactions with AI systems, where physicians iteratively refine questions, evaluate evidence, and construct context-aware reasoning. The framework introduces the Clinical Learning Episode (CLE) as the basic unit of creditable learning. A CLE is defined as a coherent AI-mediated interaction demonstrating problem framing, reasoning development, contextual integration, critical appraisal, iterative inquiry, and reflective synthesis. CLICS further proposes a calibrated AI-based Physician Intelligence Evaluator, using a seven-domain rubric known as PIS-7, to assess the physician’s reasoning trace rather than the AI’s answer. The resulting score can 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 by enabling recognition of practice-embedded cognitive learning. Rather, it is proposed as an optional, evidence-generating pathway for personalized, practice-embedded professional development. Its implementation requires iterative validation, percentile-based human audit, privacy-by-design architecture, GDPR-aligned data governance, anti-gaming controls, bias monitoring, and clear professional oversight. If validated, CLICS may support a next-generation CME infrastructure in which credit is awarded not only for time spent learning, but for demonstrable clinical reasoning and reflective professional intelligence. In addition, CLICS may enable identification of domain-specific competency gaps and support personalized, adaptive learning pathways based on observed clinical reasoning patterns.