ReviewClinical medicine (London, England)2026
Using data and artificial intelligence to improve care pathways of older people experiencing falls and frailty: Opportunities, challenges and practical considerations for clinicians.
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. Cited by 2 papers.
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
2 citing papers in PubMed.
- Letter to the Editor: 'Using data and artificial intelligence to improve care pathways of older people experiencing falls and frailty: Opportunities, challenges and practical considerations for clinicians'.Clinical medicine (London, England) · 2026Article
- Frailty, fragility and falls in older people: What's new?Clinical medicine (London, England) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Older people living with falls and frailty are common in emergency attendances, admissions and functional decline. Artificial intelligence (AI) and machine learning (ML) are increasingly incorporated in risk prediction, service streamlining and re-engineering, yet their roles in healthcare practice remain unclear. This CME article provides a practical overview for clinicians of acute care and internal medicine with a special interest in older people's care. We summarise emerging applications of AI and AI-assisted tools across the falls and frailty care pathway, from community support through the emergency department, orthogeriatrics and post-acute rehabilitation. We highlight potential benefits: enhanced risk stratification, facilitation of comprehensive geriatric assessment (CGA), rehabilitation and delivery of care transition programmes. We then discuss challenges and ethical concerns, for instance, 'digital ageism', automation bias and weak evidence for impact. Finally, we outline pragmatic questions and steps that clinicians can adopt when using AI-enabled tools in clinical settings.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.