Artificial intelligence in an ageing population

Abstract Artificial intelligence is increasingly being applied to the assessment, monitoring and care of older adults, with potential benefits across both population health and individual clinical practice. This narrative review examines current and emerging applications of AI in common conditions and care needs associated with ageing, including hypertension, stroke, myocardial infarction, Parkinson’s disease, osteoporosis, sarcopenia and supportive care robotics. Across these domains, AI-enabled analysis of electronic health records, electrocardiograms, imaging, video, wearable-sensor data and home-monitoring measurements may support earlier disease detection, more objective functional assessment, personalised risk prediction and treatment, and greater independence. Computer-vision approaches may provide detailed measures of movement following stroke and in Parkinson’s disease, while opportunistic analysis of existing imaging may improve recognition of low bone mineral density, vertebral fractures and reduced muscle health. Care robots may additionally assist with companionship, medication management and falls detection. However, much of the evidence remains based on selected datasets, proof-of-concept studies or controlled settings. Algorithmic bias, limited representation of frail and multimorbid older adults, privacy, governance, interpretability, interoperability, user burden and workforce consequences remain important barriers. AI should therefore complement rather than replace clinical judgement. Equitable implementation will require representative datasets, rigorous external and prospective validation, transparent models, user-centred design and evaluation of clinically meaningful outcomes.