SynthesisJournal of medical Internet research2024
The Applications of Artificial Intelligence for Assessing Fall Risk: Systematic Review.
Synthesis in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
33 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Machine Learning and Deep Learning Models for Predicting Future Falls in Community-Dwelling Older Adults: Systematic Review and Meta-Analysis of Longitudinal Evidence.Journal of medical Internet research · 2026Pooled it
- A Systematic Review of Multivariate Models for Predicting Fall-Related Injuries in Older Adults.Journal of nursing management · 2026Pooled it
- Opportunities and challenges of artificial intelligence in public health: a systematic review on technological efficacy, ethical dilemmas, and governance pathways.Frontiers in public health · 2025Pooled it
- AI-Based Measurement Tools for Intrinsic Capacity in Older Adults: Scoping Review.Journal of medical Internet research · 2026Article
- Artificial Intelligence Empowers Physical Therapy for Older Adults: A Perspective.Health science reports · 2026Article
- Artificial Intelligence in Physical Therapy: A Narrative Review of Artificial Intelligence Applications Across the Patient and Client Management Model.Health science reports · 2026Article
- Older adults' perspectives on big data use in hip fracture research: a qualitative study.BMC geriatrics · 2026Article
- Balance Assessment Using Gamified Digital Technology in Community-Dwelling Older Adults: Mixed Methods Validation Study and Randomized Controlled Trial.JMIR serious games · 2026Article
- Evaluation of a Frustrated Total Internal Reflection (FTIR) based balance sensor for objective fall risk assessment in older adults: a study protocol.BMC geriatrics · 2026Article
- The potential of artificial intelligence in clinical trials.European journal of clinical investigation · 2026Review
- Are We Missing the Environmental Factors in AI-Based Fall Risk Models?: A Systematic Review.Research square · 2026Article
- Development and Validation of Machine Learning Models for Predicting Falls Among Hospitalized Older Adults: Retrospective Cross-Sectional Study.JMIR aging · 2026Article
- The representational bottleneck in rehabilitation AI: from human-cognitive proxies to pathway-based clinical representations.Frontiers in digital health · 2026Article
- Development of an Artificial Intelligence-Based System for Predicting Fall Risk in Neurological Patients.Sovremennye tekhnologii v meditsine · 2026Article
- Artificial intelligence-driven risk prediction of polypharmacy in older adults: current advances, clinical applications, and future perspectives.Frontiers in public health · 2026Review
- Detection of balance in the elderly under the influence of stress (DEPIE): A cross-sectional study protocol.PloS one · 2026Observational
- AI-Assisted Dynamic Postural Control Screening to Improve Functional Mobility in Older Adult Populations: Quasi-Experimental Study.JMIR aging · 2025Article
- AI-based patient monitoring for fall prevention in stroke patients: a pilot study at a Malaysian acute stroke unit.Journal of neuroengineering and rehabilitation · 2025Article
- 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 · 2025Article
- Reference standard for the prevention and management of hospital falls: a multidisciplinary Delphi consensus study.BMJ open · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundFalls 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.
objectiveThe 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.
methodsA 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.
resultsWe 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.
conclusionsThe 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 registrationPROSPERO CRD42023443277; https://tinyurl.com/4sb72ssv.
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Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.