{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T23:07:01Z","timestamp":1793488021960,"version":"4.5.0"},"reference-count":69,"publisher":"Oxford University Press (OUP)","issue":"12","license":[{"start":{"date-parts":[[2023,12,20]],"date-time":"2023-12-20T00:00:00Z","timestamp":1703030400000},"content-version":"vor","delay-in-days":19,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100012338","name":"Alan Turing Institute","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100012338","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Institute for Health Research Applied Research Collaboration Yorkshire & Humber"},{"DOI":"10.13039\/501100018955","name":"NIHR Leeds Biomedical Research Centre","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100018955","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100023699","name":"Health Data Research UK","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100023699","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000276","name":"Department of Health and Social Care","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000276","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Artificial intelligence (AI) in healthcare describes algorithm-based computational techniques which manage and analyse large datasets to make inferences and predictions. There are many potential applications of AI in the care of older people, from clinical decision support systems that can support identification of delirium from clinical records to wearable devices that can predict the risk of a fall. We held four meetings of older people, clinicians and AI researchers. Three priority areas were identified for AI application in the care of older people. These included: monitoring and early diagnosis of disease, stratified care and care coordination between healthcare providers. However, the meetings also highlighted concerns that AI may exacerbate health inequity for older people through bias within AI models, lack of external validation amongst older people, infringements on privacy and autonomy, insufficient transparency of AI models and lack of safeguarding for errors. Creating effective interventions for older people requires a person-centred approach to account for the needs of older people, as well as sufficient clinical and technological governance to meet standards of generalisability, transparency and effectiveness. Education of clinicians and patients is also needed to ensure appropriate use of AI technologies, with investment in technological infrastructure required to ensure equity of access.<\/jats:p>","DOI":"10.1093\/ageing\/afad219","type":"journal-article","created":{"date-parts":[[2023,11,7]],"date-time":"2023-11-07T01:04:09Z","timestamp":1699319049000},"source":"Crossref","is-referenced-by-count":90,"title":["New Horizons in artificial intelligence in the healthcare of older people"],"prefix":"10.1093","volume":"52","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1809-7077","authenticated-orcid":false,"given":"Taha","family":"Shiwani","sequence":"first","affiliation":[{"name":"Academic Unit for Ageing & Stroke Research , Bradford Institute for Health Research, , Duckworth Lane, Bradford, West Yorkshire BD9 6RJ , UK"},{"name":"Bradford Teaching Hospitals NHS Foundation Trust , Bradford Institute for Health Research, , Duckworth Lane, Bradford, West Yorkshire BD9 6RJ , 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