Artificial Intelligence in Orthopaedic Education Across the Career Continuum: Moving from Benchmark Performance to Continuing Professional Development.

Artificial intelligence (AI) is entering orthopaedic education through image interpretation, simulation, technical skill assessment, examination support and content generation. Most evidence, however, comes from undergraduate and postgraduate settings, whereas practising surgeons require continuing medical education and continuing professional development (CME/CPD) that supports safe adoption, maintenance of competence and practice improvement. This matters because AI may improve task performance while it is present without producing durable learning or safer independent practice. This critical narrative review asks: what learner benefit does the current orthopaedic education literature demonstrate, and how should that evidence be translated into AI-enabled CME/CPD for practising orthopaedic clinicians? Orthopaedic-specific reviews and primary studies were synthesised and supplemented by a targeted PubMed and Google Scholar update through 4 August 2026. Across modalities, a consistent pattern emerges: AI is strongest in tightly bounded tasks with objective outputs, whereas evidence for retention, transfer, workplace performance and patient benefit is sparse. Diagnostic support can improve accuracy, speed or confidence while available; machine-learning models can distinguish expertise levels in selected simulated procedures; and large language models perform strongly on some text-based examinations but remain vulnerable to explanation errors, hallucinated reasoning, model and version dependence, and image-rich tasks. One recent historical cohort study suggests that structured AI-assisted peer teaching may improve knowledge, clinical reasoning and three-month retention. We therefore propose an AI-specific outcomes framework that separates model capability and assisted performance from learning, transfer, workplace performance and patient or system outcomes, alongside four design principles for CPD. For CME/CPD, the priority is not simply to use AI more often, but to ensure that AI-enabled education produces demonstrable learning, safe transfer and improved practice.