AI literacy as a core competency for health professionals: A consolidated framework for implementation research (CFIR)-Informed perspective

Artificial intelligence (AI) is rapidly transforming health practice through applications in clinical decision support, diagnostics, population health, and health system management. Yet, most health professionals lack formal training and understanding of AI concepts, as well as the critical appraisal of algorithmic outputs. This gap between technological adoption and workforce preparedness raises concerns about patient safety, ethical decision-making, and the quality of care, particularly due to overreliance on automated recommendations, limited recognition of bias, and inadequate understanding of model uncertainty. AI literacy has therefore become a critical competency for safe and effective participation in AI-enabled health systems. However, translating this need into sustained educational and professional practice requires attention to the multi-level determinants of implementation. This perspective article applies the Consolidated Framework for Implementation Research (CFIR) to conceptualize AI literacy as an educational intervention and maps barriers and facilitators across intervention characteristics, inner and outer settings, individual attributes, and implementation processes. Building on this analysis, the article proposes strategies for integrating AI literacy through competency-based curriculum design, experiential and interprofessional learning, faculty development, performance-based assessment, and alignment with accreditation and continuing professional development structures. Positioning AI literacy as a core professional competency is essential to maintaining human oversight, supporting ethical AI use, and safeguarding patient-centered care in increasingly technology-mediated health environments.