Bridging the Gap Between Competency-Based Medical Education and Artificial Intelligence Integration in Pharmacology: A Narrative Review

Background: Competency-based medical education (CBME) and artificial intelligence (AI) are reshaping medical training and clinical practice. Pharmacology is central to this transition because prescribing, pharmacovigilance, drug information retrieval, and individualized therapy are increasingly supported by digital and AI-enabled systems. The objective is to review implementation gaps in CBME-based pharmacology education, identify deficiencies in AI readiness, and propose a practical integration framework for undergraduate pharmacology training. Material and Methods: A narrative review was conducted using literature from PubMed, Scopus, and Google Scholar. Publications addressing CBME, AI in medical education, clinical decision support, pharmacovigilance, rational prescribing, and pharmacology teaching were reviewed. Evidence was synthesized thematically to identify curricular, faculty, assessment, technology, and governance-related gaps. Results: Important gaps were identified in curricular time allocation, faculty preparedness, assessment alignment, self-directed learning, and institutional infrastructure. AI-related deficiencies included lack of explicit competency standards, limited exposure to clinical decision support systems, insufficient training in AI-assisted pharmacovigilance, weak appraisal of generative AI outputs, and inadequate ethical preparation. Conclusion: AI integration in CBME pharmacology should be competency-based rather than tool-based. A five-component model involving curriculum redesign, faculty development, technology integration, assessment reform, and continuous evaluation may help institutions prepare graduates for safe, ethical, and evidence-informed prescribing in AI-augmented healthcare environments.

Keywords: Artificial Intelligence; Clinical Decision Support Systems; Competency-Based Education; Pharmacology; Pharmacovigilance.