Agentic AI in global health equity for high altitude populations
Агентный искусственный интеллект для обеспечения глобального равенства в здравоохранении среди высокогорных популяций
2026-06-05
SCID: 54.1/da2grh6h
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agentic AIgeneralist foundation modelsglobal health equityhigh-altitude populationsmedical artificial intelligence
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Abstract (AI)
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.
Key Findings
1
Achieving health equity requires models that integrate physiological and biological factors with environmental and socioeconomic conditions rather than treating health as narrowly biomedical.
2
Existing medical AI and digital technologies are too narrow, inadequately trained on representative regional data, and insufficiently attentive to biological, environmental, and social determinants of health.
3
Harsh environments, underdeveloped healthcare infrastructure, and limited research create substantial barriers to equitable healthcare access in high-altitude regions.
4
High-altitude populations comprise distinct ethnic groups whose unique biology and circumstances limit direct generalization of conventional medical knowledge.
5
Intelligent medical agents built on generalist foundation models are identified as a potential mechanism to address high-altitude populations’ distinctive healthcare needs and leapfrog current access barriers.
Research Object
High-altitude populations and their healthcare environments
Research Subject
The potential of intelligent medical agents built on generalist foundation models to address the distinctive healthcare needs and equity challenges of high-altitude populations
Publication Details
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2026-06-05
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