Evidence map›Paper›PMID 40560641›Full record

SynthesisJournal of medical Internet research2025

Perceptions of, Barriers to, and Facilitators of the Use of AI in Primary Care: Systematic Review of Qualitative Studies.

Héctor Martínez-Martínez, Julia Martínez-Alfonso, Belén Sánchez-Rojo-Huertas, Valeria Reynolds-Cortez, Andrea Turégano-Chumillas, Victoria A Meseguer-Ruiz, Shkelzen Cekrezi, Vicente Martínez-Vizcaíno

Abstract readSystematic Review
In one paragraph

Synthesis 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 17 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 2 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

17 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Atencion primaria · 2026
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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.

Héctor Martínez-MartínezHealth and Social Research Center, Universidad de Castilla-La Mancha, Cuenca, Spain.ORCID https://orcid.org/0009-0000-3991-3593
Julia Martínez-AlfonsoDaroca Primary Care University Center, Madrid Health Service, Madrid, Spain.ORCID https://orcid.org/0000-0001-8241-0537
Belén Sánchez-Rojo-HuertasHealth and Social Research Center, Universidad de Castilla-La Mancha, Cuenca, Spain.ORCID https://orcid.org/0009-0006-1713-4007
Valeria Reynolds-CortezHealth and Social Research Center, Universidad de Castilla-La Mancha, Cuenca, Spain.ORCID https://orcid.org/0000-0001-6140-0073
Andrea Turégano-ChumillasHealth and Social Research Center, Universidad de Castilla-La Mancha, Cuenca, Spain.ORCID https://orcid.org/0009-0000-3624-5163
Victoria A Meseguer-RuizIntegrated Healthcare Service. Primary Care Center Zona 7-Feria, Albacete, Spain.ORCID https://orcid.org/0009-0005-2527-6227
Shkelzen CekreziHealth and Social Research Center, Universidad de Castilla-La Mancha, Cuenca, Spain.ORCID https://orcid.org/0009-0003-5016-3529
Vicente Martínez-VizcaínoHealth and Social Research Center, Universidad de Castilla-La Mancha, Cuenca, Spain.ORCID https://orcid.org/0000-0001-6121-7893

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial intelligence (AI) has the potential to transform primary care by reducing the considerable bureaucratic burden. However, clinicians and patients share concerns regarding data privacy, security, and potential biases in AI algorithms.

objectiveThis study aimed to provide an in-depth understanding of primary care professionals' and patients' perceptions of, barriers to, and facilitators of the use of AI in primary care.

methodsWe conducted a systematic review of qualitative studies using MEDLINE (via PubMed), Web of Science, and Scopus databases from inception to June 9, 2024. We used the Sample, Phenomenon of Interest, Design, Evaluation, and Research Type (SPIDER) tool to design the systematic search strategy for qualitative studies. Eligible studies included qualitative analyses-based on interviews, focus groups, or similar methods-of perceptions of, barriers to, and facilitators of the use of AI in primary care, involving primary care professionals or patients. Exclusion criteria included studies on clinical decision support systems, reviews, commentaries, editorials, conference abstracts, and non-English or non-Spanish publications. Methodological quality was assessed using the Joanna Briggs Institute checklist. A thematic synthesis approach was used to structure the results, and the Grading of Recommendations Assessment, Development, and Evaluation-Confidence in the Evidence From Reviews of Qualitative Research (GRADE-CERQual) tool was used to assess the confidence in each finding.

resultsWe analyzed 316 participants, including primary care physicians, patients, and other health care professionals, from 13 studies across 6 countries selected from 942 screened records. We identified four analytical themes using thematic synthesis: (1) change in the physician-patient relationship, highlighting concerns about loss of empathy, connection, and trust; (2) AI as a partner for efficient time and information management, including its potential to improve workflow and decision-making, alongside skepticism about increased workload; (3) data as the cornerstone of AI development, reflecting concerns about data privacy, quality, bias, and corporate responsibility; and (4) barriers to and facilitators of AI in primary care, emphasizing equity, accessibility, and stakeholder co-design. The GRADE-CERQual assessment provided high confidence in all themes except theme 4, which was rated as moderate confidence.

conclusionsThis meta-synthesis includes the perspectives of primary care physicians and patients, but further research is needed on the perspectives of other professionals. Moreover, there was heterogeneity in methods and sampling strategies. This first systematic review synthesizing qualitative evidence on AI perceptions in primary care provides a comprehensive understanding of related barriers and facilitators. The key themes identified suggest that AI may help address workload, decision-making, and data management, improving health care efficiency while ensuring ethical, patient-centered care.

trial registrationPROSPERO CRD42024560048; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024560048.

Indexed as

Artificial IntelligencePrimary Health CareAttitude of Health PersonnelHumansQualitative Researchartificial intelligencefamily practicegeneral practicephysiciansprimary health carequalitative evidence synthesisqualitative researchsystematic review

Identifiers

PMID40560641
PMCPMC12242059

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.