{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T15:39:44Z","timestamp":1787758784705,"version":"build-2784847793"},"reference-count":31,"publisher":"BMJ","issue":"12","license":[{"start":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T00:00:00Z","timestamp":1765756800000},"content-version":"unspecified","delay-in-days":14,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000272","name":"National Institute for Health Research","doi-asserted-by":"crossref","award":["NIHR Academic Clinical Lectureship (SM) NIHR Acade"],"award-info":[{"award-number":["NIHR Academic Clinical Lectureship (SM) NIHR Acade"]}],"id":[{"id":"10.13039\/501100000272","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/T518153\/1 (ESPRC)"],"award-info":[{"award-number":["EP\/T518153\/1 (ESPRC)"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000265","name":"Medical Research Council","doi-asserted-by":"publisher","award":["MR\/Y503472\/1"],"award-info":[{"award-number":["MR\/Y503472\/1"]}],"id":[{"id":"10.13039\/501100000265","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["bmj.com"],"crossmark-restriction":true},"short-container-title":["BMJ Open"],"accepted":{"date-parts":[[2025,11,17]]},"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Artificial intelligence (AI) in healthcare often requires large, confidential clinical datasets. However, a recent UK government survey revealed that 20\u201340% of the public remain sceptical of its use in health research due to concerns about data security, patient\u2013practitioner communication and commercialisation of data. A greater understanding of public attitudes is therefore needed, particularly in the context of stroke research.<\/jats:p>\n                    <jats:p>In this article, we describe the patient and public involvement work undertaken for the AI-Based-Stroke-Risk-fActor-Classification-and-Treatment (ABSTRACT) project, which aims to train AI models to predict future stroke risk from the electronic health records of 1\u200918\u2009736\u2009patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Aims<\/jats:title>\n                    <jats:p>We aimed to evaluate the opinions of stroke\/transient ischaemic attack (TIA) patients, caregivers and members of the public on the following themes: (1) the acceptability of using AI to predict stroke from electronic health records, (2) obtaining these data using an opt-out model of consent and (3) allowing access to this dataset from members both within and outside of the routine clinical care team.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A total of 83 participants were recruited via the National Health Service social media and by approaching hospital inpatients. Participants were first provided with background information on stroke, AI in medical research and ABSTRACT\u2019s proposed data handling protocol. A mixed methods approach was then used to explore each of the above themes using online survey, semistructured focus groups and one-to-one interviews.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Nearly all participants felt that it was appropriate to use patient data to train AI models to predict stroke risk and that it was acceptable to obtain these data via an opt-out model of consent. Almost all participants also agreed that data could be shared within and outside of the routine clinical care team, provided it was General Data Protection Regulation compliant and used for medical research only.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The public and those with lived stroke\/TIA experience appeared to support using deidentified medical datasets for AI-driven stroke risk prediction under an opt-out consent model. However, this is provided that the research conducted is transparent, for a clear medical purpose and adheres to strict data security measures.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1136\/bmjopen-2025-108671","type":"journal-article","created":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T05:30:27Z","timestamp":1765863027000},"page":"e108671","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":2,"title":["Opinions of the UK general public and stroke survivors in using artificial intelligence and \u2018opt-out\u2019 models of consent in medical research: a qualitative study"],"prefix":"10.1136","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3119-9983","authenticated-orcid":false,"given":"William","family":"Heseltine-Carp","sequence":"first","affiliation":[{"name":"University of Plymouth, Plymouth, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mark","family":"Thurtson","sequence":"additional","affiliation":[{"name":"University of Plymouth, Plymouth, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8746-9957","authenticated-orcid":false,"given":"Michael","family":"Allen","sequence":"additional","affiliation":[{"name":"Medical School, University of Exeter, Exeter, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Browning","sequence":"additional","affiliation":[{"name":"University of Plymouth, Plymouth, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Megan","family":"Courtman","sequence":"additional","affiliation":[{"name":"University of Plymouth, Plymouth, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aishwarya","family":"Kasabe","sequence":"additional","affiliation":[{"name":"University of Plymouth, Plymouth, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emmanuel","family":"Ifeachor","sequence":"additional","affiliation":[{"name":"University of Plymouth, Plymouth, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stephen","family":"Mullin","sequence":"additional","affiliation":[{"name":"University of Plymouth, Plymouth, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"239","published-online":{"date-parts":[[2025,12,15]]},"reference":[{"key":"2026010703470232000_15.12.e108671.1","doi-asserted-by":"crossref","first-page":"1077","DOI":"10.1007\/s11136-014-0909-z","article-title":"Beyond lip service and box ticking: how effective patient engagement is integral to the development and delivery of patient-reported outcomes","volume":"24","author":"Absolom","year":"2015","journal-title":"Qual Life Res"},{"key":"2026010703470232000_15.12.e108671.2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1369-7625.2012.00795.x"},{"key":"2026010703470232000_15.12.e108671.3","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1177\/19418744241288887","article-title":"The Ghost in the Machine: Artificial Intelligence in Neurocardiology Will Advance Stroke Care","volume":"15","author":"Saini","year":"2025","journal-title":"Neurohospitalist"},{"key":"2026010703470232000_15.12.e108671.4","doi-asserted-by":"publisher","DOI":"10.3174\/ajnr.A7716"},{"key":"2026010703470232000_15.12.e108671.5","doi-asserted-by":"crossref","first-page":"e599","DOI":"10.1016\/S2589-7500(21)00132-1","article-title":"Patient and general public attitudes towards clinical artificial intelligence: a mixed methods systematic review","volume":"3","author":"Young","year":"2021","journal-title":"Lancet Digit Health"},{"key":"2026010703470232000_15.12.e108671.6","unstructured":"Public attitudes to data and ai: tracker survey (wave 3)\u2019, gov.uk. 2024. 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