ArticleBMC geriatrics2026
Artificial intelligence in geriatric healthcare: a scoping review.
Article in BMC geriatrics, 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.
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0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundDeep demographic ageing is triggering a twin crisis in healthcare: a rising prevalence of multimorbidity among older adults and a severe global shortage of healthcare professionals, particularly nursing staff, which together create life-threatening gaps in long-term care. Although artificial intelligence shows significant potential to alleviate these pressures by improving the efficiency and accessibility of geriatric healthcare, its integration faces critical challenges spanning systemic, user-level, and societal dimensions.
objectiveThis review aims to systematically analyze the current applications of artificial intelligence in geriatric healthcare, identify key barriers hindering its effective integration, and propose stakeholder-specific roadmaps.
methodsThis scoping review follows the PRISMA-ScR guidelines. This review was conducted using a two-step search strategy from five databases: PubMed, MEDLINE, the Cochrane Library, EMBASE, and Web of Science. First, a comprehensive search was performed across five electronic databases for literature published between January 1, 2015, and September 30, 2025. Second, the reference lists of identified studies and relevant reviews were manually screened. The study selection followed the PCC framework, focusing on evidence of artificial intelligence applications, implementation challenges, and proposed solutions in geriatric healthcare.
resultsArtificial intelligence is extensively applied across five key domains in geriatrics: health monitoring and disease management, safety supervision and risk prevention, cognitive and mental health support, social interaction and emotional companionship, and daily living assistance. Despite this potential, three major barrier categories were identified: (1) Systemic fractures; (2) User-level resistance, and (3) A widening social divide. In response, the study proposes concrete roadmaps, such as mandating Fast Healthcare Interoperability Resources standards for data interoperability, establishing ethical artificial intelligence certification, deploying culturally adaptive designs, and initiating workforce upskilling programs.
conclusionsArtificial intelligence holds significant promise for mitigating the global geriatric healthcare crisis exacerbated by demographic aging and nursing shortages. However, realizing its full potential requires a coordinated, multi-stakeholder approach to overcome the entrenched systemic, human, and social obstacles. The proposed roadmaps provide an actionable framework that may facilitate the development of artificial intelligence systems that are more efficient, equitable, and human-centered, pending empirical validation in real-world settings. REGISTRATION: The protocol has been registered on OSF.
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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.