CONTINUING MEDICAL EDUCATION AND ARTIFICIAL INTELLIGENCE INTEGRATION IN CLINICAL PRACTICE: A CASE STUDY OF PHYSICIANS’ EXPERIENCES

This study examined the role of continuing medical education (CME) in supporting physicians’ integration of artificial intelligence (AI) into clinical practice within a large academic health system. Guided by a human resource development (HRD) perspective, the study used a qualitative phenomenologically informed case design to explore physicians’ sensemaking of AI-related CME within clinical work. Data were collected through semi-structured interviews across specialties and supplemented with organizational artifacts, including CME materials and internal communications. Data were analyzed using an inductive coding process that identified seven interrelated themes. Findings indicated that CME functioned not as a discrete intervention but as part of a distributed learning system in which formal instruction, experiential learning, and professional dialogue supported capability development. Physicians described AI integration as an interpretive process requiring judgment, contextualization, and ongoing adaptation rather than compliance with technological outputs. Interpretive judgment emerged as the central mechanism through which AI-generated information was incorporated into clinical reasoning, with interpretive competence as the core outcome of professional learning in AI-enabled environments. Learning was shaped by organizational conditions, workflow alignment, and opportunities for experiential and social learning. Dynamic processes, including interpretive burden and trust calibration, highlighted the evolving nature of engagement with AI over time. Conceptually, the study extends HRD scholarship by demonstrating shaping and skilling through distributed learning systems. Implications emphasize designing learning systems that support interpretation, continuous development, and alignment with workplace conditions.