Evidence map›Paper›PMID 40688576›Full record

ArticleDigital health

General practitioners' opinions of generative artificial intelligence in the UK: An online survey.

Anna Kharko, Carolina Garcia Sanchez, Josefin Hagström, Jens Gaab, Cosima Locher, Brian McMillan, David Sundemo, Charlotte Blease

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 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
–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

14 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Atencion primaria · 2026
    Pooled it
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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

8 authors.

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
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
Josefin HagströmParticipatory eHealth and Health Data Research Group, Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden.ORCID https://orcid.org/0000-0003-2835-0259
Jens GaabClinical Psychology and Psychotherapy, Faculty of Psychology, University of Basel, Basel, Switzerland.ORCID https://orcid.org/0000-0002-9194-8491
Cosima LocherClinical Psychology and Psychosomatics, Faculty of Psychology, University of Basel, Basel, Switzerland.ORCID https://orcid.org/0000-0002-9660-0590
Brian McMillanCentre for Primary Care and Health Services Research, University of Manchester, Manchester, UK.ORCID https://orcid.org/0000-0002-0683-3877
David SundemoGeneral Practice/Family Medicine, School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.ORCID https://orcid.org/0000-0002-5871-1636
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

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The rapid rise of large language model-based generative artificial intelligence (AI) tools, such as chatbots and AI scribes, has prompted interest in their clinical applicability. This study examined the perspectives of Methods: In January 2025, a national online survey of 1005 GPs, stratified by region, was administered via the UK's largest clinical marketing platform. The survey gathered data on attitudes towards generative AI, its perceived effects on clinical tasks, care delivery and respondent demographics. Group differences and correlations between opinions and respondent characteristics were analysed. Results: Among the 1005 respondents, 50% were male, 54% aged 46 or older, and 83% practised in England. Only 11% were encouraged by their employer to use generative AI tools, and 5% had received training. Most agreed that AI could improve documentation (69%) and patient information gathering (57%), and 59% expected increased efficiency in care. A similar proportion (59%) anticipated that patients may rely on AI instead of seeking medical care. The majority (79%) agreed that training is needed, while 71% disagreed that AI could enhance empathic communication. Male GPs were more likely to view AI as beneficial across tasks, while female GPs more often emphasised training needs. Older GPs expressed concern about potential inequities stemming from AI use. Conclusions: While GPs were cautiously optimistic about generative AI - particularly for documentation and data collection - scepticism persisted. In particular, around empathy and equity, highlighting the need for guidance on AI integration into primary care.

Indexed as

AIArtificial intelligencedoctoreducationgeneral practicegeneral practitionergenerative AIGPlarge language modelsLLMprimary caresurvey opinionssurvey researchtraining

Identifiers

PMID40688576
PMCPMC12276478

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.