aceArtificial 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…
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 source says clinical translation of orthopaedic AI remains constrained, and that adoption requires more than high discrimination, including external and prospective validation; the benefits described are possibilities rather than demonstrated outcomes.
Evidence
- Peer-reviewedKnee Surgery, Sports Traumatology, Arthroscopy2026-10-15
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Truvace Impact Record TRV-2026-1619, v1: “Artificial intelligence across the continuum of care for the aging musculoskeletal patient: From longitudinal phenotyping to personalized surgery and recovery monitoring.” Truvace, 2026-10-16. /record/TRV-2026-1619 (accessed at citation time). sha256 06f67911aa94ed93…
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