Evidence map›Paper›PMID 40769193›Full record

ArticleJournal of medical Internet research2025

Generative Artificial Intelligence in Primary Care: Qualitative Study of UK General Practitioners' Views.

Charlotte Blease, Carolina Garcia Sanchez, Cosima Locher, Brian McMillan, Jens Gaab, John Torous

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. Unmet needs in functional neurological disorder related to digital healthcare.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2026
    Review
  3. Article
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  5. Article
  6. Article
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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

6 authors.

Charlotte BleaseParticipatory eHealth & Health data Research Group, Department of Women's and Children's Health, Uppsala University, MTC-huset, Dag Hammarskjölds väg 14B, 1 tr, Uppsala, 752 37, Sweden, 46 184710000.ORCID http://orcid.org/0000-0002-0205-1165
Carolina Garcia SanchezParticipatory eHealth & Health data Research Group, Department of Women's and Children's Health, Uppsala University, MTC-huset, Dag Hammarskjölds väg 14B, 1 tr, Uppsala, 752 37, Sweden, 46 184710000.ORCID http://orcid.org/0000-0003-0137-3434
Cosima LocherClinical Psychology and Psychosomatics, Faculty of Psychology, University of Basel, Basel, Switzerland.ORCID http://orcid.org/0000-0002-9660-0590
Brian McMillanCentre for Primary Care and Health Services Research, University of Manchester, Manchester, United Kingdom.ORCID http://orcid.org/0000-0002-0683-3877
Jens GaabClinical Psychology and Psychosomatics, Faculty of Psychology, University of Basel, Basel, Switzerland.ORCID http://orcid.org/0000-0002-9194-8491
John TorousDepartment of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States.ORCID http://orcid.org/0000-0002-5362-7937

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The potential for generative artificial intelligence (GenAI) to assist with clinical tasks is the subject of ongoing debate within biomedical informatics and related fields. Objective: This study aimed to explore general practitioners' (GPs') opinions about GenAI on primary care. Methods: In January 2025, we conducted a web-based survey of 1005 UK GPs' experiences and opinions of GenAI in clinical practice. This study involved a qualitative inductive descriptive analysis of a written response ("comments") to an open-ended question in the survey. After analysis, the interpretation of themes was also informed by the technology acceptance model. Results: Out of 1005 respondents, 611 GPs (61%) provided written comments in response to the free text question, totaling 7990 words. Comments were classified into 3 major themes and 8 subthemes in relation to GenAI in clinical practice. The major themes were (1) unfamiliarity, (2) ambivalence and anxiety, and (3) role in clinical tasks. "Unfamiliarity" encompassed a lack of experience and knowledge, and the need for training on GenAI. "Ambivalence and anxiety" included mixed expectations among GPs in relation to these tools, beliefs about diminished human connection, and skepticism about AI accountability. Finally, commenting on the role of GenAI in clinical tasks, GPs believed it would help with documentation. However, respondents questioned AI's clinical judgment and raised concerns about operational uncertainty concerning these tools. Female GPs were more likely to leave comments than male GPs, with 53% (324/611) of female GPs providing feedback compared to 41.1% (162/394) who did not. Chi-square tests confirmed this difference ((χ²₂= 14.6, P=.001). In addition, doctors who left comments were significantly more likely to have used GenAI in clinical practice compared with those who did not. Among all respondents, 71.7% (438/611) had not used GenAI. However, noncommenters were even less likely to have used it, with 80.7% (318/394) reporting no use. A chi-square test confirmed this difference (χ²₁=10.0, P=.002). Conclusions: This study provides timely insights into UK GPs' perspectives on the role, impact, and limitations of GenAI in primary care. However, the study has limitations. The qualitative data analyzed originates from a self-selected subset of respondents who chose to provide free-text comments, and these participants were more likely to have used GenAI tools in clinical practice. However, the substantial number of comments offers valuable insights into the diverse views held by GPs regarding GenAI. Furthermore, the majority of our respondents reported limited experience and training with these tools; however, many GPs perceived potential benefits of GenAI and ambient AI for documentation. Notably, 2 years after the widespread introduction of GenAI, GPs' persistent lack of understanding and training remains a critical concern. More extensive qualitative work would provide a more in-depth understanding of GPs' views.

Indexed as

Attitude of Health PersonnelGeneral PractitionersGenerative Artificial IntelligencePrimary Health CareAdultFemaleHumansMaleMiddle AgedQualitative ResearchSurveys and QuestionnairesUnited Kingdomartificial intelligenceeducationgeneral practicegenerative AIlarge language modelsonline survey questionnaireprimary carequalitative research.training

Identifiers

PMID40769193
PMCPMC12327960

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.