Citation

González-Castro A, Leiros-Rodríguez R, Prada-García C, Benítez-Andrades JA. J. Med. Internet. Res. 2024; 26: e54934.

Copyright

(Copyright © 2024, Centre for Global eHealth Innovation)

DOI

10.2196/54934

PMID

38684088

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.

Language: en

Keywords

*Accidental Falls/prevention & control; *Artificial Intelligence; accidental falls; AI; artificial intelligence; fall risk; Humans; machine learning; patient care; Postural Balance; public health; Risk Assessment/methods