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Generative artificial intelligence in medicine: a mixed-methods survey of UK general practitioners
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Women's and Children's Health, Participatory eHealth and Health Data Research Group. Centre for Primary Care and Health Services Research, The University of Manchester, Manchester, UK.ORCID iD: 0000-0003-0908-6173
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2025 (English)In: BMJ Digital Health & AI, E-ISSN 3049-575X, Vol. 1, no 1, article id e000051Article in journal (Refereed) Published
Abstract [en]

Objective To explore the opinions of general practitioners (GPs) in the UK about the use of generative artificial intelligence (AI) tools in primary care.

Methods and analysis At the beginning of 2024, using a convenience sample, we administered an online mixed-methods survey to registered GPs currently working in the UK.

Results A total of 1006 GPs responded, with 53% being male and 54% over 46 years old. One-fifth of GPs reported having used AI for clinical practice, with male doctors and those in bigger cities being more likely to have used it. 80% of respondents expressed a need for more training in understanding these tools. GPs at least somewhat agreed AI would improve documentation (59%) and patient information gathering (56%). 55% felt AI could increase inequities and 54% saw potential for patient harm, but 47% believed it could enhance healthcare efficiency. GPs who used these tools were significantly more optimistic about the scope for generative AI in improving clinical tasks. One-third of GPs left comments that were classified into four major themes: (1) lack of familiarity and understanding with AI, (2) role of AI in clinical practice, (3) concerns about AI and (4) AI and the future of healthcare.

Conclusions This study highlights UK GPs’ developing perspectives on generative AI in clinical practice, emphasising the need for more training. Many GPs reported a lack of knowledge and experience with this technology, although a portion already used non-medical grade technology for clinical tasks, with the risks that this entails.

Place, publisher, year, edition, pages
BMJ Publishing Group Ltd, 2025. Vol. 1, no 1, article id e000051
National Category
Public Health, Global Health and Social Medicine
Research subject
Machine learning
Identifiers
URN: urn:nbn:se:uu:diva-564433DOI: 10.1136/bmjdhai-2025-000051OAI: oai:DiVA.org:uu-564433DiVA, id: diva2:1986763
Part of project
Beyond Implementation of eHealth, Forte, Swedish Research Council for Health, Working Life and WelfareAvailable from: 2025-08-03 Created: 2025-08-03 Last updated: 2025-11-20Bibliographically approved

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Kharko, AnnaHägglund, MariaBlease, Charlotte

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CiteExportLink to record
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Citation style
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