ReviewDigital health
Artificial intelligence in primary health care: A bibliometric analysis of publications from 2015 to 2024.
Review in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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.
Who cites it
2 citing papers in PubMed.
- Evaluation of AI tool assisting primary healthcare physicians to diagnostic and treatment tasks.Family medicine and community health · 2026Article
- A lightweight hybrid deep learning framework for multi-pill detection, multi-attribute recognition, OCR-based imprint analysis, and metadata retrieval.Frontiers in artificial intelligence · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Primary health care (PHC) is a key approach to achieving the goal of "Health for All," and artificial intelligence (AI) can empower primary health care in various contexts, including screening, diagnosis, and treatment. Since 2019, the application of AI in primary health care has attracted increasing academic attention. This is a bibliometric study aiming to identify publication trend, distributions, frontiers, and hotspots of extant research about AI in PHC. Methods: We retrieved papers from the Web of Science Core Collection. Using VOSviewer, CiteSpace, and Bibliometrix, we conducted a bibliometric analysis to examine the publication trend, citation trend, country distribution, institution distribution, journal distribution, author distribution, reference distribution, keyword co-occurrence, and keyword burst. Results: We totally obtained 653 English-language papers published in Web of Science Core Collection from 2015 to 2024. These papers were authored by 4304 researchers in 1551 institutions from 70 countries. Both publication volume and citation frequency have increased rapidly since 2019. The USA and the United Kingdom have the most publications, citations, and collaborations in this field. The journals that publish papers on this topic are mainly from the medicine field, spanning a broader range of disciplines, including molecular biology, genetics, health, nursing, medicine, psychology, education, and the social sciences. Conclusions: This research area has attracted growing academic attention since 2019, and this trend is ongoing. Key research topics include diagnosis, family medicine, mental health, medical image analysis, and early screening of diseases.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.