This paper is in the following e-collection/theme issue:

Digital Health Reviews (1724) Machine Learning (2076) Decision Support for Health Professionals (1539) Artificial Intelligence (2129) mHealth for Screening (296) Falls Prevention in the Elderly (101) Development and Evaluation of Research Methods, Instruments and Tools (744) Methodological Innovations (37)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/54934, first published .
The Applications of Artificial Intelligence for Assessing Fall Risk: Systematic Review

The Applications of Artificial Intelligence for Assessing Fall Risk: Systematic Review

The Applications of Artificial Intelligence for Assessing Fall Risk: Systematic Review

Authors of this article:

Ana González-Castro1 Author Orcid Image ;   Raquel Leirós-Rodríguez2 Author Orcid Image ;   Camino Prada-García3 Author Orcid Image ;   José Alberto Benítez-Andrades4 Author Orcid Image
Article Authors Cited by (25) Tweetations (3) Metrics

Journals

  1. Alsaleh H, Alsaad S, Alabdulwahab S, Vennu V, Bindawas S. Fall Prevention in Older Adults: Insights from Saudi Arabian Physical Therapists on the Otago Exercise Program. Risk Management and Healthcare Policy 2024;Volume 17:2689 View
  2. Le N, Sonka M, Skeete D, Romanowski K, Galet C. Predicting admission for fall‐related injuries in older adults using artificial intelligence: A proof‐of‐concept study. Geriatrics & Gerontology International 2025;25(2):232 View
  3. Yu X, Cai Y, Yang R, Ma F, Kim W. Revisiting sensor-based intelligent fall risk assessment for older people: A systematic review. Engineering Applications of Artificial Intelligence 2025;144:110176 View
  4. Turja T, Castaño-Rosa R. A scoping review: domestic fall risks in older adults stemming from unsafe behaviors and their association with physical environmental factors. Journal of Public Health 2025 View
  5. Bincalar A, Freeman C, schraefel m. Optimal Algorithms for Improving Pressure-Sensitive Mat Centre of Pressure Measurements. Sensors 2025;25(5):1283 View
  6. Gervasi C, Perego E, Galli F, Torri V, Castoldi M, Bombardieri E. Prevention of falls in hospitalized patients—evaluation of the effectiveness of a monitoring system (Verso Vision) developed with artificial intelligence. Frontiers in Digital Health 2025;7 View
  7. Gao Y, Zhang X, Zhao G, Shi Y, Zhang Y. BPSSL: Balanced pseudo-label based semi-supervised learning for medical image classification. Biomedical Signal Processing and Control 2025;109:108044 View
  8. Hao J. Artificial intelligence empowers physical therapy for neurological conditions. Neurological Sciences 2025;46(10):5573 View
  9. Delaforce A, Li J, Niven P, Maddock E, Grujovski M, Fahy M, Good N, Jayasena R. Using a Co-Designed Implementation Enhancement Plan to Increase the Adoption of a Digital Fall Prevention Platform: A Non-Randomized Pre-Post Interventional Study. Journal of Multidisciplinary Healthcare 2025;Volume 18:3507 View
  10. Ferrara P, Monti C, Rozza D, Fornari C, Antonazzo I, Ferrara M, Bellelli G, Brandi M, Mantovani L, Mazzaglia G. Incidence and risk factors for falls among nursing home residents in Italy: a retrospective cohort study. Aging Clinical and Experimental Research 2025;37(1) View
  11. Kim J, Lee S. Evaluation of Activities of Daily Living: Current Insights and Future Horizons. Annals of Geriatric Medicine and Research 2025;29(2):143 View
  12. Jiang S, Zhao F, Liang Y, Wang S, Xu Q, Wang R, Wu T, Yang H. Development and Validation of a Dynamic Online Nomogram for Predicting Inpatient Fall Risk: A Cohort Study. Journal of Multidisciplinary Healthcare 2025;Volume 18:4819 View
  13. González-Castro A, Leirós-Rodríguez R, Nistal-Martínez M, Bodero-Vidal E, Benítez-Andrades J, Hernandez-Lucas P. Effect of COVID-19 on Falls in a Residential Care Facility for the Elderly: Longitudinal Observational Study. Journal of Clinical Medicine 2025;14(17):6229 View
  14. Rahmadani F, Alshamsi F, Almazrouei B, Hanaya Alsuwaidi A, Alhammadi M, Simsekler M. Streamlining Patient Fall Prevention and Management Through Human-Centered AI-Based Decision Support Systems. Risk Management and Healthcare Policy 2025;Volume 18:3051 View
  15. Morris M, Said C, Haines T, Heng H, Batchelor F, Hutchinson A, McKercher J, Semciw A, Hill A, Peterson S, Kane R, Fowler-Davis S, Campbell S, Sherrington C, Gilmartin-Thomas J, Phan U, Thwaites C. Reference standard for the prevention and management of hospital falls: a multidisciplinary Delphi consensus study. BMJ Open 2025;15(10):e105950 View
  16. Zhang H, An Y, Song M, Meng Y. Dynamic fall risk prediction in hospitalized cancer patients: development and validation of a machine learning model using multidimensional clinical data to overcome over-sensitivity in traditional scales. BMC Medical Informatics and Decision Making 2025;25(1) View
  17. Danial M, Chow C, Lim M, Ayop N, Looi I, Ch’ng A. AI-based patient monitoring for fall prevention in stroke patients: a pilot study at a Malaysian acute stroke unit. Journal of NeuroEngineering and Rehabilitation 2025;22(1) View
  18. Sharma J, Sidiq M. Ripple effect of human–AI interface in patient-reported outcome measures: shaping the future of motor relearning programs for fall prevention – letter to editor. European Journal of Physiotherapy 2025:1 View
  19. Neri L, Zhang H, Usvyat L. Artificial intelligence in kidney disease and dialysis: from data mining to clinical impact. Current Opinion in Nephrology & Hypertension 2026;35(1):30 View
  20. Prieto Y, Rossel P, Martínez-Carrasco C. Assessing the risk of falling in community-dwelling older adults through cognitive domains and machine learning techniques. PeerJ Computer Science 2025;11:e3367 View
  21. Gattani A, Dixit S, Patil M, Gupta M, Navghane A, Hule O, Srinivasan K. Artificial intelligence for fall detection in older adults: A comprehensive survey of machine learning, deep learning approaches, and future directions. Ageing Research Reviews 2026;113:102948 View
  22. N V, K R, Nallu Vivekanandan Y. Bio-inspired spiking neural network for modeling and optimizing adaptive vertigo therapy. Cognitive Neurodynamics 2026;20(1) View

Books/Policy Documents

  1. ANANDH S, GUDUR A, VAIRALKAR M. AI‐driven Innovations in Physiotherapy and Oncology 1. View

Conference Proceedings

  1. Zorriassatine F, Burton E, Boyd J, Naser A, Lotfi A. Proceedings of the 17th International Conference on PErvasive Technologies Related to Assistive Environments. Cross-Species Insights: Drawing Lessons Between Bird Lameness Detection and Human Gait Anomaly Detection View
  2. Khera P, Kour G, Kumar A. 2025 2nd International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS). Deep Learning Model for Prediction of Fall-Risk Using Gait Biomarkers in Geriatric Population View