ArticleNature health2026
Large language models for frontline healthcare support in low-resource settings.
Article in Nature health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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Who cites it
5 citing papers in PubMed.
- Generative AI-enabled clinical decision support system in primary care: a pragmatic, cluster-randomized trial.Nature medicine · 2026Trial
- Development of an Equity-Centered Sociotechnical Architecture for Generative AI Integration in Public Health Promotion: Conceptual Framework.Journal of medical Internet research · 2026Article
- Harnessing Artificial Intelligence in Health Research in Low-Income and Middle-Income Countries: Potential and Caution.Mayo Clinic proceedings. Digital health · 2026Review
- Benchmarking large language models and clinicians using locally generated primary healthcare vignettes in Kenya.BMJ digital health & AI · 2026Article
- Fed-XLM-R: a privacy-preserving federated framework with adapter-scoped differential privacy for mental health triage-level intent classification on resource-constrained edge devices.Frontiers in digital health · 2026Article
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large language models (LLMs) have demonstrated strong performance in medical contexts; however, existing benchmarks often fail to reflect the real-world complexity of low-resource health systems accurately. Here we develop a dataset of 5,609 clinical questions contributed by 101 community health workers across 4 Rwandan districts and compared responses generated by 5 LLMs (Gemini-2, GPT-4o, o3-mini, Deepseek R1 and Meditron-70B) with those from local clinicians. A subset of 524 question-answer pairs was evaluated using a rubric of 11 expert-rated metrics, scored on a 5-point Likert scale. Gemini-2 and GPT-4o were the best performers (achieving mean scores of 4.49 and 4.48 out of 5, respectively, across all 11 metrics). All LLMs significantly outperformed local clinicians (
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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.