Review: AI across the care continuum for older musculoskeletal patients
This abstract-level review argues that orthopaedic AI should move from isolated diagnostic or short-term prediction tasks toward longitudinal, multimodal support across the whole care continuum for older musculoskeletal patients, while noting that clinical adoption needs trust, validation and workflow integration, not just accuracy. [1]
Automatic summary · not editorially reviewed
The key findings
The review argues that many orthopaedic AI models address isolated diagnostic, imaging or short-term prediction tasks rather than the longitudinal care trajectories that determine outcomes in older musculoskeletal patients. [1]
The review proposes continuum-of-care AI spanning prediction before intervention, AI-assisted arthroplasty planning, perioperative risk prediction and post-discharge monitoring during the 90-day postoperative episode. [1]
The review states that clinical adoption requires more than high discrimination and places explainable AI within a broader trust framework including validation, workflow integration and post-deployment monitoring. [1]
The source at a glance
- Study type / format
- Review (abstract only); level of evidence IV as stated in the abstract.
- Population / context
- Older musculoskeletal patients, including geriatric orthopaedic patients along the care continuum; no sample size is reported in the abstract.
- Summary based on
- Study abstract
- Original publication
- October 15, 2026
Why task-specific orthopaedic AI may fall short in aging care
Musculoskeletal care is being reshaped by population aging, multimorbidity, frailty, osteoporosis, sarcopenia, polypharmacy and growing vulnerability to losing independence after surgery. AI has entered orthopaedic research, but its clinical translation remains constrained. According to the review, 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 authors therefore argue that the central limitation is not simply whether older adults appear in datasets, but whether AI systems are valid across the continuum of geriatric orthopaedic care.
Supporting source [1]
What the review proposes along the care pathway
The review argues for a transition from task-specific orthopaedic AI toward continuum-of-care AI. Upstream, it names radiomics, longitudinal osteoarthritis phenotyping, opportunistic computed tomography (CT)-based skeletal assessment and AI-augmented dual-energy X-ray absorptiometry (DXA) modelling as possible ways to 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 reconstruction strategies specific to a patient's phenotype. Perioperatively, automated frailty and sarcopenia phenotyping, wearable gait analysis and multimodal temporal models may support dynamic risk prediction. After discharge, continuous monitoring of mobility, sleep, pain trajectories, wound-related symptoms and rehabilitation adherence may help close a surveillance gap during the 90-day postoperative episode.
Supporting source [1]
Adoption needs trust and validation, not only accuracy
The review states that 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 authors describe the future of orthopaedic AI not simply as better diagnosis or more precise surgery, but as longitudinal, multimodal support across the full care continuum: predicting progression, selecting treatment, personalizing surgery, optimizing perioperative care, monitoring recovery and preserving mobility and independence. The abstract reports a level of evidence of Level IV.
Supporting source [1]
How to interpret this
Abstract only; the full text was not reviewed. This is a review arguing a position, not a new study with its own sample, follow-up or measured outcomes. The abstract names potential uses of AI but reports no effect sizes, accuracy figures or validation results. Level of evidence IV is stated in the abstract.
Based on the study abstract; the full paper, tables and supplementary data were not reviewed.
Source & supporting passages
Oettl FC, Zsidai B, Pruneski JA, Oeding JF, Longo UG, Hirschmann MT, Du Hyun R, Samuelsson K, ESSKA Artificial Intelligence Committee. · europepmc.org · October 15, 2026