ReviewClinical medicine (London, England)2026
Artificial intelligence for detecting fall events, assessing fall risks, and preventing fall occurrences in the elderly: A narrative review.
Review in Clinical medicine (London, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Falls in the elderly represent a critical public health crisis, necessitating a shift from reactive to proactive management. This review evaluates artificial intelligence (AI) across four primary modalities: wearable sensors, vision-based systems, ambient devices, and natural language processing and large language models applied to electronic health records. Machine learning and deep learning algorithms enable real-time detection, near-fall identification, and predictive risk stratification. Hybrid edge-cloud frameworks and multimodal fusion enhance accuracy and scalability. However, challenges include the 'simulation gap' in training data, 'black box' interpretability, and privacy concerns, 'alarm fatigue' among nursing staff, and 'class imbalance.' Future directions emphasise hybrid systems, integrating multiple sensor streams with edge-cloud computing, to enhance ecological validity and responsiveness. Ultimately, seamless integration of interpretable AI into clinical workflows offers a transformative path toward personalised prevention, reducing fall-related morbidity and empowering elderly independence.
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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 OpenQuestion graph.