Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 Apr 29:26:e54934.
doi: 10.2196/54934.

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

Ana González-Castro  1 , Raquel Leirós-Rodríguez #  2 , Camino Prada-García  3 , José Alberto Benítez-Andrades  4
Affiliations

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

Ana González-Castro et al. J Med Internet Res. .

Abstract

Background: Falls and their consequences are a serious public health problem worldwide. Each year, 37.3 million falls requiring medical attention occur. Therefore, the analysis of fall risk is of great importance for prevention. Artificial intelligence (AI) represents an innovative tool for creating predictive statistical models of fall risk through data analysis.

Objective: The aim of this review was to analyze the available evidence on the applications of AI in the analysis of data related to postural control and fall risk.

Methods: A literature search was conducted in 6 databases with the following inclusion criteria: the articles had to be published within the last 5 years (from 2018 to 2024), they had to apply some method of AI, AI analyses had to be applied to data from samples consisting of humans, and the analyzed sample had to consist of individuals with independent walking with or without the assistance of external orthopedic devices.

Results: We obtained a total of 3858 articles, of which 22 were finally selected. Data extraction for subsequent analysis varied in the different studies: 82% (18/22) of them extracted data through tests or functional assessments, and the remaining 18% (4/22) of them extracted through existing medical records. Different AI techniques were used throughout the articles. All the research included in the review obtained accuracy values of >70% in the predictive models obtained through AI.

Conclusions: The use of AI proves to be a valuable tool for creating predictive models of fall risk. The use of this tool could have a significant socioeconomic impact as it enables the development of low-cost predictive models with a high level of accuracy.

Trial registration: PROSPERO CRD42023443277; https://tinyurl.com/4sb72ssv.

Keywords: AI; accidental falls; artificial intelligence; fall risk; machine learning; patient care; public health.

PubMed Disclaimer

Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1
Figure 1
Identification of studies via databases and registers.
Figure 2
Figure 2
Infographic about the characteristics of the analyzed research.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Step safely: strategies for preventing and managing falls across the life-course. World Health Organization. 2021. [2024-04-02]. https://www.who.int/publications/i/item/978924002191-4 .
    1. Keall MD, Pierse N, Howden-Chapman P, Guria J, Cunningham CW, Baker MG. Cost-benefit analysis of fall injuries prevented by a programme of home modifications: a cluster randomised controlled trial. Inj Prev. 2017 Feb;23(1):22–6. doi: 10.1136/injuryprev-2015-041947.injuryprev-2015-041947 - DOI - PubMed
    1. Almada M, Brochado P, Portela D, Midão L, Costa E. Prevalence of falls and associated factors among community-dwelling older adults: a cross-sectional study. J Frailty Aging. 2021;10(1):10–6. doi: 10.14283/jfa.2020.44. - DOI - PubMed
    1. Menéndez-González L, Izaguirre-Riesgo A, Tranche-Iparraguirre S, Montero-Rodríguez Á, Orts-Cortés MI. [Prevalence and associated factors of frailty in adults over 70 years in the community] Aten Primaria. 2021 Dec;53(10):102128. doi: 10.1016/j.aprim.2021.102128. https://linkinghub.elsevier.com/retrieve/pii/S0212-6567(21)00162-1 S0212-6567(21)00162-1 - DOI - PMC - PubMed
    1. Guirguis-Blake JM, Michael YL, Perdue LA, Coppola EL, Beil TL. Interventions to prevent falls in older adults: updated evidence report and systematic review for the US preventive services task force. JAMA. 2018 Apr 24;319(16):1705–16. doi: 10.1001/jama.2017.21962.2678103 - DOI - PubMed

Publication types