From patient notes to prognostication: The revolutionary potential of event‐based foundation models in orthopaedics

The integration of artificial intelligence (AI)-equipped tools into electronic health record (EHR) platforms may drive the evolution of orthopaedic diagnosis and decision-making, leveraging big data to generate precise and context-aware insights. With the advent of event-based foundation models, a critical step forward in predictive modelling is imminent, with upcoming implementations promising prospective generation of patient timelines, anticipated future events and real-time estimates of risk, even when trained on heterogeneous, multimodal healthcare data. Namely, Epic's Cosmos Medical Event Transformer (CoMET) platforms, a newly introduced family of event-based models trained on billions of clinical encounters, are capable of learning and accurately predicting temporospatial patterns in medical occurrences at scale, thus enabling a new approach to patient-specific decision-making and prognostication. Accordingly, the current manuscript aims to provide a description of these models, including their unique capabilities, potential limitations and future applications to orthopaedic practices. LEVEL OF EVIDENCE: Level V.