Evidence map›Paper›PMID 42249081›Full record

ReviewNPJ digital medicine2026

Agentic AI in global health equity for high altitude populations.

Peilun Shi, Tianmin Ren, Yuan Xie, Judy Wawira Gichoya, Tien Yin Wong, Xunming Ji, Ningli Wang, Wu Yuan

Abstract readReview
In one paragraph

Review in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Peilun Shi *Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China.
Tianmin Ren *Department of Ophthalmology, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
Yuan XieBeijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Beijing Key Laboratory of Intelligent Diagnosis Technology and Equipment for Optic Nerve-Related Eye Diseases, Capital Medical University, Beijing, China.
Judy Wawira GichoyaDepartment of Radiology, Emory University, Atlanta, GA, USA.
Tien Yin WongTsinghua Medicine, Tsinghua University, Beijing, China.
Xunming JiBeijing Institute of Brain Disorders, Capital Medical University, Beijing, China.
Ningli WangBeijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Beijing Key Laboratory of Intelligent Diagnosis Technology and Equipment for Optic Nerve-Related Eye Diseases, Capital Medical University, Beijing, China. wningli@vip.163.com.
Wu YuanDepartment of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China. wyuan@cuhk.edu.hk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Successfully adapting to life in the highest altitudes ("Roof of the world") is a heritage of evolutionary adaptation for humans. With rising interest in adventure travel and expanding transport networks that facilitate mobility from low to high altitudes, provision of healthcare for populations living in high-altitude regions has re-emerged as an area of interest and research. These populations have several unique characteristics that limit the simple generalization of medical knowledge. First, these populations are naturally segregated into distinct ethnic groups, representing a unique marginal demographic. Second, the harsh natural environment, underdeveloped healthcare infrastructure, and limited research and understanding of healthcare needs, issues and challenges experienced by highland communities pose significant barriers to equitable healthcare access. The use of medical artificial intelligence and digital technology provides an opportunity to provide innovative solutions for these populations. However, these technologies would not facilitate health equity in their current state today as most are narrow in their application, are not trained on data representative of these regions, and ignore the multifactorial nature of being healthy that combines biological and physiological factors, in addition to environmental and socio factors. The success of generalist models for tasks such as scientific discovery provides a mechanism to leapfrog existing challenges and provide equitable care in these regions. In this paper, we discuss the opportunity of intelligent medical agents developed on generalist foundation models to meet the unique needs of high-altitude populations.

Identifiers

PMID42249081
PMCPMC13558769

What OpenQuestion holds

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Registered trials

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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.