Wearable- and Vision-Based Gait Models for Predicting Future Falls in Older Adults: A Systematic Review of Performance, Validation, and Clinical Readiness

Background/Objectives: We reviewed the performance, validation and clinical readiness of wearable- and vision-based models predicting future falls. Methods: Nine databases and supplementary sources were searched for reports published in 2000–20 July 2026 (retrospective PROSPERO registration: CRD420261502037). Eligible populations had a minimum, mean or median age ≥60 years or separable older-adult results. Screening, extraction and appraisal used single-reviewer assessment with second-reviewer verification, rather than independent duplication. Ten of 47 reports (21.28%) lacked complete-article access. Cohort families were synthesized without meta-analysis; PROBAST+AI appraisal was restricted to complete articles. Results: The 47 reports represented 37 cohort families (15–1705 participants; horizons 7 days–5 years). Representative AUCs ranged from 0.51 to 0.99 across 26 families; 24 estimates were apparent, internally validated or validation-unverified. The only independent-cohort evaluation of published models yielded AUCs of 0.49–0.59. Retained domain-level appraisals classified all 37 complete-article evaluations as high risk of bias. Calibration intercept and slope appeared in one report (2.13%); none reported decision curve analysis, net benefit or clinical impact. Conclusions: Interpretability is limited by high risk of bias, sparse events, limited independent validation and missing calibration. The evidence does not support routine deployment; this concerns evidentiary readiness rather than technical potential.