Artificial intelligence across the continuum of care for the aging musculoskeletal patient: From longitudinal phenotyping to personalized surgery and recovery monitoring.

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Abstract

Musculoskeletal care is being reshaped by population aging, multimorbidity, frailty, osteoporosis, sarcopenia, polypharmacy and increasing vulnerability to postoperative loss of independence. Artificial intelligence (AI) has entered orthopaedic research, but its clinical translation remains constrained. Many models address isolated diagnostic, imaging or short-term prediction tasks rather than the longitudinal care trajectories that determine outcomes in older musculoskeletal patients. The central limitation is therefore not simply whether older adults are represented in datasets, but whether AI systems are valid across the continuum of geriatric orthopaedic care. This review argues for a transition from task-specific orthopaedic AI toward continuum-of-care AI for the aging musculoskeletal patient. Upstream, radiomics, longitudinal osteoarthritis phenotyping, opportunistic computed tomography (CT)-based skeletal assessment and AI-augmented dual-energy X-ray absorptiometry (DXA) modelling may improve prediction of disease progression, fracture risk, treatment failure and surgical timing. At the point of intervention, AI-assisted arthroplasty planning may help move total knee arthroplasty from generic alignment targets toward phenotype-specific reconstruction strategies that account for constitutional alignment, bone morphology, ligament envelope, neuromuscular control, frailty and functional reserve. Perioperatively, automated frailty and sarcopenia phenotyping, wearable gait analysis and multimodal temporal models integrating imaging, electronic health records, laboratory data, medications, patient-reported outcomes and wearable signals may support dynamic risk prediction. After discharge, continuous monitoring of mobility, sleep, pain trajectories, wound-related symptoms and rehabilitation adherence may help close the high-risk surveillance gap during the 90-day postoperative episode. However, clinical adoption requires more than high discrimination. Explainable AI should be understood as one component of a broader trust framework that also includes calibration, uncertainty quantification, decision-curve analysis, external and prospective validation, workflow integration, post-deployment monitoring and accountability. The future of orthopaedic AI is not simply better diagnosis or more precise surgery, but longitudinal, multimodal support across the full care continuum: predicting progression, selecting treatment, personalizing surgery, optimizing perioperative care, monitoring recovery and preserving mobility and independence. LEVEL OF EVIDENCE: Level IV.

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