Abstract

Continuing Medical Education (CME) is fundamental to sustaining clinical competency and elevating overall healthcare quality. Conventional CME programmes are hampered by standardized, one‑size‑fits‑all curricula, low learning efficiency and limited alignment with real‑world clinical demands. Artificial Intelligence (AI) offers personalised, adaptive and scalable solutions to modernize existing CME frameworks. This narrative review adheres to the SANRA (Scale for the Assessment of Narrative Review Articles) reporting guidelines for narrative reviews. Literature searches were performed in PubMed, Web of Science and Scopus, using search terms: “artificial intelligence”, “continuing medical education”, “continuing professional development”, “practising clinician training”, “CME competency assessment”. The search covered publications from 2021‑2026. Initial database searching retrieved approximately 428 unique records. After title‑abstract screening and full‑text eligibility assessment against pre‑defined criteria, 14 relevant articles were finally included for qualitative synthesis. Strict inclusion criteria were enforced: only studies focusing explicitly on licensed practising healthcare professionals participating in formal CME / continuing professional development were retained; papers investigating medical students were excluded.

This narrative review summarises core applications of AI within CME, including intelligent personalised learning pathway recommendation, AI‑assisted clinical simulation, automated competency evaluation, and data‑informed training demand analysis. We further outline prevailing barriers, such as data privacy risks, limited model interpretability, ethical dilemmas and difficulties in clinical system integration. Where high‑quality evidence specific to licensed practising CME participants is lacking, findings derived from broader medical‑education research are noted explicitly. Lastly, future research directions are proposed to facilitate safe, high‑performance AI‑enabled CME for frontline healthcare practitioners, with clear articulation of critical evidence gaps, especially the requirement to test whether AI‑driven CME learning gains transfer to modified clinical practice and improved patient‑level outcomes. This work provides evidence‑based references for the digital transformation of CME and lifelong professional development among practising clinicians.

Keywords:Artificial intelligence; Continuing medical education; Continuing professional development; Clinical training; Personalised learning; Competency assessment; Practising clinicians