Evidence map›Paper›PMID 41312147›Full record

ArticleDigital health

General practitioners' adoption of generative artificial intelligence in clinical practice in the UK: An updated online survey.

Charlotte Blease, Josefin Hagström, Carolina Garcia Sanchez, Anna Kharko, Brian McMillan, Jens Gaab, Emma Brulin, Cosima Locher, Maria Hägglund, Sara Riggare and 1 more

Abstract read
In one paragraph

Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. GenAI and clinical decision making in general practice.The British journal of general practice : the journal of the Royal College of General Practitioners · 2026
    Article
  5. Article
  6. Article
  7. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Charlotte BleaseParticipatory eHealth and Health Data Research Group, Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden.ORCID https://orcid.org/0000-0002-0205-1165
Josefin HagströmParticipatory eHealth and Health Data Research Group, Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden.
Carolina Garcia SanchezParticipatory eHealth and Health Data Research Group, Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden.ORCID https://orcid.org/0000-0003-0137-3434
Anna KharkoParticipatory eHealth and Health Data Research Group, Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden.ORCID https://orcid.org/0000-0003-0908-6173
Brian McMillanCentre for Primary Care and Health Services Research, University of Manchester, Manchester, UK.ORCID https://orcid.org/0000-0002-0683-3877
Jens GaabClinical Psychology and Psychotherapy, Faculty of Psychology, University of Basel, Basel, Switzerland.
Emma BrulinUnit of Occupational Medicine, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.ORCID https://orcid.org/0000-0002-3374-268X
Cosima LocherClinical Psychology and Psychotherapy, Faculty of Psychology, University of Basel, Basel, Switzerland.
Maria HägglundParticipatory eHealth and Health Data Research Group, Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden.
Sara RiggareParticipatory eHealth and Health Data Research Group, Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden.
Kenneth D MandlBoston Children's Hospital, Harvard Medical School, Boston, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Following the launch of ChatGPT in November 2022, interest in large language model-powered chatbots has soared with increasing focus on the clinical potential of these tools. Building on a previous survey conducted in 2024, we sought to gauge general practitioners' (GPs) adoption of this new generation of chatbots to assist with any aspect of clinical practice in the UK. Methods: An online survey was disseminated in January 2025 to a stratified convenience sample of GPs registered with the clinician marketing platform Doctors.net.uk. The research was conducted as part of a scheduled monthly 'omnibus survey,' designed to achieve a fixed sample size of 1000 participants. Results: Of the 1005 respondents, 50% respondents were men, 54% were 46 years or older. 25% reported using generative artificial intelligence (GenAI) tools in clinical practice; of these, 35% reported using these tools to generate documentation after patient appointments, 27% to suggest a differential diagnosis, 24% for treatment options, and 24% for referrals. Of the 249 GPs who used generative AI for clinical tasks, 71% said that, in general, these tools reduced work burdens. In the last 12 months, 85% reported that their employer had not encouraged them to use GenAI tools, but only 3% said their employer had prohibited them from using GenAI tools in their work; 95% reported they had no professional training in using GenAI tools in their work. Conclusions: This survey suggests that doctors' use of GenAI in clinical practice may be growing in the UK. Findings suggest that UK GPs may benefit from these tools, especially for administrative tasks and clinical reasoning support, and after adopting them, most users reported a decrease in work burdens. Continued absence of reported training remains a concern.

Indexed as

educationgeneral practiceGenerative AIlarge language modelsprimary caresurveytraining

Identifiers

PMID41312147
PMCPMC12647557

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.