Evidence map›Paper›PMID 42054668›Full record

ArticleJournal of medical Internet research2026

Open-Source Large Language Models and AI Health Equity: A Health Service Triangle Model Perspective.

Shaolong Wu, Wenxin Zou, Jiong Tu, Chunxiao Wang, Cheng Jin, Jing Liao, Kwok Cho Tang, Ning Liu, Chun Hao

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

9 authors.

Shaolong Wu *Department of Public Administration, School of Government, Sun Yat-sen University, Guangzhou, China.ORCID https://orcid.org/0000-0003-2800-4209
Wenxin Zou *Department of Public Administration, School of Government, Sun Yat-sen University, Guangzhou, China.ORCID https://orcid.org/0009-0006-3884-7292
Jiong TuSchool of Sociology and Anthropology, Sun Yat-sen University, Guangzhou, China.ORCID https://orcid.org/0000-0002-9662-6117
Chunxiao WangSchool of Health Management, Guangzhou Medical University, Guangzhou, China.ORCID https://orcid.org/0009-0009-1859-0962
Cheng JinParty Committee, Huizhou Municipal Central People's Hospital, Huizhou, China.ORCID https://orcid.org/0009-0003-3493-5736
Jing LiaoSun Yat-sen Global Health Institute, Institute of State Governance, Sun Yat-sen University, Guangzhou, Guangdong, China.ORCID https://orcid.org/0000-0001-6370-3430
Kwok Cho TangSun Yat-sen Global Health Institute, Institute of State Governance, Sun Yat-sen University, Guangzhou, Guangdong, China.ORCID https://orcid.org/0009-0004-9133-3712
Ning Liu *School of Management, Lanzhou University, Lanzhou, China.ORCID https://orcid.org/0000-0001-8399-8985
Chun Hao *Sun Yat-sen Global Health Institute, Institute of State Governance, Sun Yat-sen University, Guangzhou, Guangdong, China.ORCID https://orcid.org/0000-0002-9881-6504

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study explores the role of open-source large language models (LLMs) in promoting artificial intelligence (AI) health equity from the perspective of the health service triangle model. First, it defines AI health, categorizes AI-supported decision-making patterns, and assesses the status quo of AI health inequalities. Second, by comparing open-source and closed-source LLMs in terms of patient privacy, data security, accessibility, and use, it demonstrates the distinct advantages of open-source LLMs for AI-enabled health services. Finally, based on the health service triangle model, this study demonstrates how open-source LLMs drive the democratization of AI-enabled health services-particularly benefiting low-resource regions-by expanding service types, improving accessibility, enhancing quality, and reducing costs. This study concludes that, while open-source LLMs must address challenges such as hallucination risks and ethical responsibilities, they ultimately enable AI health equity through technological sharing.

Indexed as

Artificial IntelligenceHealth EquityHumansLarge Language ModelsAIAI healthartificial intelligenceartificial intelligence healthdemocratizationhealth equityhealth service triangle modelopen-source large language modelsopen-source LLMs

Identifiers

PMID42054668
PMCPMC13173066

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.