Artificial intelligence in primary health care: a focused bibliometric mapping of research trends and changes in thematic attention (2015-2025).

AIM: To map publication growth, collaboration, knowledge structure, and thematic change in artificial intelligence (AI) research in primary health care (PHC) and general practice and assess whether attention has expanded from model development to validation, implementation evaluation, and governance. BACKGROUND: PHC AI research covers risk stratification, screening, diagnostic support, clinical decision support, and workflow optimization. However, publication growth and changing hotspots do not directly indicate clinical implementation or practical value. METHODS: Web of Science Core Collection was searched for AI- and PHC-related records published from 1 January 2015 to 19 October 2025. Of 1,481 records, 1,261 articles and review articles were screened using titles, abstracts, and author keywords, Python-developed term lexicons, weighted scoring, and manual adjudication. Records lacking a specific PHC application, focused on public or consumer health, or insufficiently relevant were excluded. The main sample included 1,123 papers for bibliometric, collaboration, thematic, citation, and bibliographic coupling analyses. A supplementary search identified 599 candidates to assess omissions due to terminological variation; 142 remained after 457 exclusions and were added to form a 1,265-paper sensitivity corpus used only for keyword-frequency, co-occurrence, and annual trend reruns. Annual output was assessed for 2015 to 2024; 2025 was retained as an incomplete-year observation but excluded from complete-year comparisons. FINDINGS: Annual output rose from 6 papers in 2015 to 220 in 2024 and increased continuously after 2018. Sensitivity analyses confirmed a stable thematic structure centred on AI, machine learning, PHC, electronic health records, screening and diagnosis, with overall increases in standardized AI- and PHC-term frequencies. Bibliographic coupling identified multiple application settings and model-evaluation topics. Model validation became more common; implementation/workflow remained visible, governance/regulation emerged after 2018, and fairness/bias and explainability were more prominent from 2022 to 2024. These findings indicate broader research attention, but do not demonstrate routine AI use or improved clinical outcomes.