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Med."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Successfully adapting to life in the highest altitudes (\u201cRoof of the world\u201d) 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.<\/jats:p>","DOI":"10.1038\/s41746-026-02701-7","type":"journal-article","created":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T12:33:30Z","timestamp":1780662810000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Agentic AI in global health equity for high altitude populations"],"prefix":"10.1038","volume":"9","author":[{"given":"Peilun","family":"Shi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianmin","family":"Ren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Judy Wawira","family":"Gichoya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tien Yin","family":"Wong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xunming","family":"Ji","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ningli","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wu","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,5]]},"reference":[{"key":"2701_CR1","doi-asserted-by":"publisher","first-page":"e2102463118","DOI":"10.1073\/pnas.2102463118","volume":"118","author":"JC Tremblay","year":"2021","unstructured":"Tremblay, J. 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