Evidence map›Paper›PMID 40162893›Full record

ReviewJournal of primary care & community health

Opportunities and Challenges for Large Language Models in Primary Health Care.

Hongyang Qin, Yuling Tong

Abstract readReview
In one paragraph

Review in Journal of primary care & community health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing 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

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

  1. Pooled it
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  7. Diagnostic interpretation of pure tone audiograms by multimodal LLMs: A comparative study of ChatGPT-5.0 and Gemini 2.5.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026
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  13. Large Language Models and Primary Care: A Scoping Review.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026
    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

2 authors.

Hongyang QinThe Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.ORCID 0009-0004-6577-0070
Yuling TongThe Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Primary Health Care (PHC) is the cornerstone of the global health care system and the primary objective for achieving universal health coverage. China's PHC system faces several challenges, including uneven distribution of medical resources, a lack of qualified primary healthcare personnel, an ineffective implementation of the hierarchical medical treatment, and a serious situation regarding the prevention and control of chronic diseases. The rapid advancement of artificial intelligence (AI) technology, large language models (LLMs) demonstrate significant potential in the medical field with their powerful natural language processing and reasoning capabilities, especially in PHC. This review focuses on the various potential applications of LLMs in China's PHC, including health promotion and disease prevention, medical consultation and health management, diagnosis and triage, chronic disease management, and mental health support. Additionally, pragmatic obstacles were analyzed, such as transparency, outcomes misrepresentation, privacy concerns, and social biases. Future development should emphasize interdisciplinary collaboration and resource sharing, ongoing improvements in health equity, and innovative advancements in medical large models. There is a demand to establish a safe, effective, equitable, and flexible ethical and legal framework, along with a robust accountability mechanism, to support the achievement of universal health coverage.

Indexed as

Artificial IntelligenceNatural Language ProcessingPrimary Health CareChinaHealth PromotionHumansLarge Language Modelscommunity healthhealth promotionlarge language modelsprimary caretele-medicineuniversal health coverage

Identifiers

PMID40162893
PMCPMC11960148

What Socratic holds

Textmetadata
LicenceCC BY-NC
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 Socratic graph.