Continuing Medical Education as a Workforce Learning System for Artificial Intelligence in Clinical Practice
TL;DR
It is argued that AI-focused CME should be designed as a longitudinal organizational learning system rather than a discrete instructional event, and implications are offered for technology integration, faculty development, and organizational learning in clinical education.
Abstract
Artificial intelligence (AI) is increasingly embedded in clinical decision support, documentation, imaging, predictive analytics, and workflow management. However, the educational systems responsible for preparing practicing physicians to use AI have not fully addressed the interpretive, ethical, and organizational demands of AI-supported care. This qualitative case study examined how continuing medical education (CME) shapes physicians’ readiness to integrate AI into clinical practice. Guided by the Unified Theory of Acceptance and Use of Technology (UTAUT) and human resource development (HRD), the study analyzed semi-structured interviews with 14 physicians from diverse specialties within a large academic health system. Organizational artifacts, including CME announcements, internal communications, and evaluation materials, were also examined to support triangulation. Findings indicate that CME was most influential when it framed AI as support for clinical attention rather than as decision authority, developed interpretive judgment, reinforced physician accountability, connected formal instruction to informal workplace learning, aligned with clinical workflow, and supported trust calibration over time. The study argues that AI-focused CME should be designed as a longitudinal organizational learning system rather than a discrete instructional event. Implications are offered for technology integration, faculty development, and organizational learning in clinical education.