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.
T. Murphy, Rob E. Carpenter· International Journal on Int...· 1 citation
Artificial intelligence (AI), treated in this study as an umbrella term for AI-enabled clinical and educational technologies rather than as a single platform, is reshaping medical education, including how diagnostic skills, treatment planning, and patient care are taught. This study examines AI integration in medical education through the perceptions and readiness of clinical educators. Guided by the Unified Theory of Acceptance and Use of Technology, the study explores factors influencing AI adoption in medical training, including performance expectancy, effort expectancy, social influence, and facilitating conditions. In this exploratory study, semi-structured interviews were conducted with 15 clinical educators in the south-central United States who supervise third-year medical students. Findings suggested six recurring themes: the technological learning curve, the need for hands-on learning, institutional support, mentorship, preservation of human elements, and generational differences in comfort with AI. While some AI-enabled applications may support adaptive and personalized learning, educators expressed concerns about maintaining empathy, patient interaction, and human-centered care. The findings suggest that effective AI integration may require strategic institutional support, ongoing training, and pedagogical change. This study provides insight into developing AI-ready medical education models that balance technical competence with humanistic values.
T. Murphy, Ginger Vaughn, Rob E. Carpenter et al.· International Medical Educat...· 0 citations