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

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 limit…

By the Truvace desk2 min read1 sourcePeer-reviewedTRV-2026-1619· cite & verify
Artificial intelligence across the continuum of care for the aging musculoskeletal patient: From longitudinal phenotyping to personalized surgery and recovery monitoring
Physical Therapy - The Noun Project by undefined. CC0 · http://creativecommons.org/publicdomain/zero/1.0/deed.en

In brief

This review of orthopaedic AI argues that the field should move from narrow, task-specific models toward systems that follow older musculoskeletal patients across their whole care pathway. It describes upstream tools such as radiomics, opportunistic CT assessment and AI-augmented DXA modelling, then intraoperative planning, perioperative frailty and sarcopenia phenotyping, and post-discharge monitoring of mobility, sleep, pain and rehabilitation.

The framing matters because older patients face frailty, multimorbidity and a risk of losing independence after surgery, and isolated diagnostic or imaging models do not capture those trajectories. The paper presents these applications as possibilities rather than demonstrated outcomes, and says adoption depends on calibration, uncertainty quantification, external and prospective validation, workflow integration, monitoring and accountability.

Main points

  1. The review says many orthopaedic AI models address isolated diagnostic, imaging or short-term prediction tasks rather than longitudinal care trajectories.
  2. Upstream tools named include radiomics, longitudinal osteoarthritis phenotyping, opportunistic CT skeletal assessment and AI-augmented DXA modelling.
  3. For total knee arthroplasty, AI planning may shift from generic alignment targets to phenotype-specific strategies that account for factors such as bone morphology, ligament envelope and frailty.
  4. After discharge, continuous monitoring of mobility, sleep, pain, wound symptoms and rehabilitation adherence is proposed for the 90-day postoperative episode.
  5. Explainable AI is framed as one part of a broader trust framework that includes decision-curve analysis and post-deployment monitoring.

The gain

AI applied across the care pathway, from CT and DXA-based skeletal assessment to phenotype-specific knee arthroplasty planning and post-discharge monitoring, may improve prediction of progression, fracture risk and surgical timing and help close the 90-day postoperative surveillance gap.

The rundown

The paper starts from the demographic context of orthopaedic care: population aging, multimorbidity, frailty, osteoporosis, sarcopenia, polypharmacy and a growing vulnerability to loss of independence after surgery. It states that the central limitation is whether AI systems are valid across the continuum of geriatric orthopaedic care, not just whether older adults appear in datasets.

Across the pathway, it describes AI use at four stages: predicting disease progression and surgical timing, personalizing arthroplasty, supporting perioperative risk prediction with multimodal temporal models that combine imaging, health records, labs, medications, patient-reported outcomes and wearable signals, and monitoring recovery after discharge.

It rates its evidence as Level IV and concludes that the future of orthopaedic AI lies in longitudinal, multimodal support aimed at preserving mobility and independence rather than just better diagnosis or more precise surgery.

Sources

  1. Peer-reviewedKnee Surgery, Sports Traumatology, Arthroscopy2026-10-15

The debate