Evidence-Grounded AI for Musculoskeletal Care

2026-07-14

AI Summary

This study addresses the challenge of fragmented patient data and the lack of individualized, end-to-end management in musculoskeletal disease care by proposing OrthoPilot—the first longitudinal clinical AI system that spans the entire care pathway. Built upon a large language model, OrthoPilot integrates real-time, multimodal heterogeneous data from within the hospital with external authoritative medical knowledge to deliver evidence-based, intelligent decision support from diagnosis through rehabilitation. Moving beyond conventional AI systems that merely predict isolated clinical events, OrthoPilot demonstrates significant clinical impact: in prospective trials, it improved end-to-end care success rates by 10.6%, increased bed utilization by 9.7% during randomized deployment, and substantially enhanced patients’ access to health-related information.

Abstract

Musculoskeletal diseases are among the leading causes of disability worldwide and create the greatest global need for rehabilitation. Because recovery, remodelling and degeneration often unfold over months to years, musculoskeletal care requires longitudinal management that repeatedly integrates evolving patient evidence, external medical knowledge and stage-specific functional goals. In routine practice, this evidence is fragmented across visits, departments and hospital systems, limiting individualized, evidence-based care. Here we report OrthoPilot, a clinical artificial intelligence system powered by a large language model that integrates hospital data streams with authoritative external knowledge for continuous musculoskeletal management. OrthoPilot autonomously retrieves real-time imaging, laboratory, pathology and order data and converts evolving patient states into evidence-based decisions from admission diagnosis to rehabilitation planning. We established a specialist-validated benchmark from real-world electronic health records spanning 1,000 disease codes. In a reader study across the complete care pathway, OrthoPilot was compared with 81 orthopaedic physicians and surpassed experts with 25 years of experience in diagnostic reasoning, clinical decision-making and management planning. It also outperformed all evaluated intelligent systems across 60 external clinical centres. In a prospective study of 1,870 complex cases, OrthoPilot increased full-chain management success by 10.6%. During an 8-month randomised deployment involving 8,240 inpatients, it increased cumulative cases per bed by 9.7% and improved patient-reported access to health information. These results move clinical AI from predicting isolated events toward executing longitudinal management across complete musculoskeletal care pathways.

Problem

• musculoskeletal care
• longitudinal management
• evidence integration
• fragmented clinical data
• individualized care

Innovation

• longitudinal AI
• evidence-grounded reasoning
• multimodal clinical integration
• musculoskeletal care pathway
• large language model in healthcare