ORCID

Abstract

Background Artificial intelligence (AI) in healthcare often requires large, confidential clinical datasets. However, a recent UK government survey revealed that 20–40% of the public remain sceptical of its use in health research due to concerns about data security, patient–practitioner communication and commercialisation of data. A greater understanding of public attitudes is therefore needed, particularly in the context of stroke research.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 18 736 patients.Aims 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.Methods 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’s 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.Results 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.Conclusion 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.

Publication Date

2025-12-15

Publication Title

BMJ Open

ISSN

2044-6055

Acceptance Date

2025-11-17

Deposit Date

2026-02-05

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