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Updated: Jun 13, 2026
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Frailty Assessment in an Aging Mouse Model
Published on: September 23, 2025
Machine Learning-Based Frailty Prediction and Classification in Community-Dwelling Older Adults: A Systematic Review
Seungmi Kim1, Myung-Jun Shin2,3,4, Byung Kwan Choi2,5,6
1Department of Convergence Medicine, Pusan National University School of Medicine, Yangsan 50612, Republic of Korea.
Healthcare (Basel, Switzerland)
|June 12, 2026
Summary
Machine learning models for frailty prediction show varied accuracy and limited readiness for community implementation. Standardized definitions, external validation, and transparent reporting are crucial for advancing this field.
Area of Science:
- Gerontology and Geriatric Medicine
- Artificial Intelligence in Healthcare
- Computational Epidemiology
Background:
- Frailty is a key vulnerability in older adults, but current assessment methods are labor-intensive.
- Machine learning (ML) offers potential for frailty prediction but faces challenges in standardization and validation.
- Existing ML frailty models require systematic review regarding their rigor and readiness for real-world application.
Purpose of the Study:
- To systematically review machine learning-based studies on frailty prediction and classification in community-dwelling older adults.
- To assess the validation rigor, explainability, and implementation readiness of ML frailty models.
- To identify gaps and priorities for the development and deployment of ML frailty prediction tools.
Main Methods:
- Systematic review following PRISMA 2020 guidelines, registered in PROSPERO.
- Searches conducted in PubMed, Embase, Web of Science, Scopus, IEEE Xplore, and ACM Digital Library.
- Risk of bias (PROBAST), reporting quality (TRIPOD), and implementation readiness (RE-AIM, TRL) assessed for included studies.
Main Results:
- Fourteen studies included, with ML models showing heterogeneous performance in frailty classification (AUROC 0.70-0.98) and incident prediction (AUROC 0.58-0.85).
- Independent external validation was infrequent, and only 2 studies met low risk of bias and applicability criteria.
- Most studies are at Technology Readiness Level (TRL) 4-6, with limited reporting on implementation and maintenance (RE-AIM).
Conclusions:
- ML-based frailty models demonstrate variable predictive performance and are not yet ready for routine community implementation.
- Key priorities include standardized frailty definitions, robust external validation, transparent reporting of model performance and predictors.
- Prospective evaluation of implementation strategies is essential for clinical translation of ML frailty tools.
