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Related Experiment Videos

Continuing Professional Development in the Artificial Intelligence Era.

Anita Samuel1, Ronald M Cervero

  • 1Dr. Samuel: Associate Professor, Department of Health Professions Education, Uniformed Services University of Health Sciences, Bethesda, MD. Cervero: Professor, Department of Health Professions Education, Uniformed Services University of Health Sciences, Bethesda, MD.

The Journal of Continuing Education in the Health Professions
|July 10, 2026
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) challenges traditional continuing professional development (CPD) by distributing agency between clinicians and AI. A new Post-AI CPD model (Augmentation, Integration, Co-Agency) is proposed to prepare clinicians for AI-embedded practice.

Keywords:
CPD frameworksartificial intelligenceco-agencygenerative artificial intelligence

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Area of Science:

  • Medical Education
  • Artificial Intelligence in Healthcare
  • Clinical Practice

Background:

  • Artificial Intelligence (AI) is transforming clinical workflows, impacting traditional assumptions in continuing professional development (CPD).
  • Existing CPD frameworks, which center on clinician agency and knowledge generation, are disrupted by AI systems acting as active contributors to clinical judgment.
  • The distribution of agency across human and machine actors challenges established links between CPD, competence, performance, and outcomes.

Purpose of the Study:

  • To introduce a novel Post-AI CPD model that addresses the evolving nature of clinical practice in AI-mediated environments.
  • To describe how clinicians learn and adapt within AI-integrated workflows.
  • To propose a shift in CPD focus from tool usage to stewardship of AI-embedded practice.

Main Methods:

  • Conceptual analysis of existing CPD frameworks and AI's impact on clinical agency.
  • Development of a Post-AI CPD model with three configurations: Augmentation, Integration, and Co-Agency.
  • Exploration of the implications for clinician learning and professional development.

Main Results:

  • The Post-AI CPD model describes three configurations: Augmentation (AI enhances efficiency), Integration (negotiating shared judgment with AI), and Co-Agency (joint production of clinical action).
  • AI systems are shifting from passive tools to active partners in clinical reasoning and decision-making.
  • Traditional CPD models risk misalignment with AI-saturated clinical environments.

Conclusions:

  • CPD must evolve to prepare clinicians for AI-embedded practice, focusing on interpretation, supervision, and stewardship.
  • The proposed Post-AI CPD model offers a framework for understanding and navigating clinician learning in AI-mediated healthcare.
  • Failure to reconfigure CPD risks its relevance in the future of AI-driven clinical care.