ReviewThe Lancet regional health. Western Pacific2025
Large language models and global health equity: a roadmap for equitable adoption in LMICs.
Review in The Lancet regional health. Western Pacific, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled 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.
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
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of Large Language Models in Chronic Disease Care: Mixed Methods Systematic Review and Thematic Synthesis.Journal of medical Internet research · 2026Pooled it
- Harnessing Artificial Intelligence in Health Research in Low-Income and Middle-Income Countries: Potential and Caution.Mayo Clinic proceedings. Digital health · 2026Review
- Large language models approach clinician performance in ESC cardiovascular risk stratification: a vignette-based benchmark study.European heart journal. Digital health · 2026Article
- Large Language Models in Cardiovascular Prevention: A Narrative Review and Governance Framework.Diagnostics (Basel, Switzerland) · 2026Review
- Redefining lightweight vision models for healthcare AI.Frontiers in artificial intelligence · 2026Article
- Hotspot Evolution and Future Prospects of Large Language Models in Medical Education: A Bibliometric Analysis.Advances in medical education and practice · 2026Article
- A roadmap for medical large language models: a review of foundations, applications, and challenges.Military Medical Research · 2026Review
- How artificial intelligence is disrupting the field of medical knowledge and evaluation: a narrative review.BMJ digital health & AI · 2026Review
- Through the looking glass: ethical considerations regarding LLM-induced hallucinations to medical questions.Frontiers in digital health · 2026Article
- Rebalancing power in infectious disease modelling: Toward inclusive and contextual approaches.PLOS global public health · 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
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large language models (LLMs) have been proposed to address global health inequity by providing accessible and high-quality health care, particularly in low- and middle-income countries (LMICs). However, despite the early enthusiasm following the release of GPT, development and deployment of LLMs have remained heavily concentrated in high-income countries (HIC), raising concerns that such technology may worsen existing health disparities instead of alleviating them. The most recent LLMs, which include features such as lower cost, and open-source framework, show promise in rebalancing LLMs' benefits worldwide. In this viewpoint, we examine the current challenges and imbalance in LLM deployment across global regions, identify the key barriers to adoption in LMICs, assess current LLMs' advances and the new opportunities they bring to global health equity. We also propose a five-dimensional roadmap-focusing on people, products, platforms, processes, and policies-to advance LLMs' equitable adoption in LMIC and improve inclusive progress in global health. Funding: National Key R&D Program (Grant No: 2022YFC2502800); National Natural Science Fund of China (Grant No: 82388101); Beijing Natural Science Foundation (Grant No: IS23096).
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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.