Canonical text (the exact bytes fingerprinted)

TRUVACE RECORD VERSION
record: TRV-2026-1619
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-16T22:02:19.118603Z
status: published
lens: g_space
sector: health
headline: Artificial intelligence across the continuum of care for the aging musculoskeletal patient: From longitudinal phenotyping to personalized surgery and recovery monitoring
dek: 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…
gain_title: 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.
problem_title: (none)
trace_subject: (none)
gain_reading: 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.
gain_evidence: may improve prediction of disease progression, fracture risk, treatment failure and surgical timing | may help close the high‐risk surveillance gap during the 90‐day postoperative episode
problem_reading: (none)
problem_evidence: (none)
quick_read: 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.
limitation: 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.
tag: Evidence-backed gain
key_points: The review says many orthopaedic AI models address isolated diagnostic, imaging or short-term prediction tasks rather than longitudinal care trajectories. | Upstream tools named include radiomics, longitudinal osteoarthritis phenotyping, opportunistic CT skeletal assessment and AI-augmented DXA modelling. | 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. | After discharge, continuous monitoring of mobility, sleep, pain, wound symptoms and rehabilitation adherence is proposed for the 90-day postoperative episode. | Explainable AI is framed as one part of a broader trust framework that includes decision-curve analysis and post-deployment monitoring.
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:
- peer_reviewed | Knee Surgery, Sports Traumatology, Arthroscopy | https://doi.org/10.1002/ksa.70647 | 2026-10-15
prev: 0000000000000000000000000000000000000000000000000000000000000000

sha256

06f67911aa94ed93381d43873876e0c32eed11cfca2b185011abacf2323b3281

previous

0000000000000000000000000000000000000000000000000000000000000000