Research priorities for data science and artificial intelligence in global health: an international consensus exercise

Authors

• Peige Song, Zhejiang University School of Public Health
• Denan Jiang, Zhejiang University School of Public Health
• Jiali Zhou, Zhejiang University School of Public Health
• Yajie Zhu, Hangzhou Normal University
• Rosliza Abdul Manaf, Faculty of Medicine and Health Sciences
• Danladi Adamu Bojude, Gombe State University
• Marie Laurette Agbre-Yace, Institut National de Santé Publique
• Sajjad Ali, Ziauddin University
• Omolade Allen, The University of Manchester
• Anayochukwu Edward Anyasodor, Charles Sturt University
• Zeus Aranda, Tecnológico de Monterrey
• Awsan Bahattab, Università degli Studi del Piemonte Orientale “Amedeo Avogadro”
• Adams Bodomo, Universität Wien
• Florencia Borrescio-Higa, Universidad Adolfo Ibáñez
• Marie Buchtova, Univerzita Hradec Králové
• Nataša Buljan, Specialist Family Medicine Practice
• Vaishali Deshmukh, INCLEN Trust International
• Lina Díaz-Castro, Instituto Nacional de Psiquiatría Ramon de la Fuente

Document Type

Review

Publication Date

3-1-2026

Abstract

Summary Applications of data science and artificial intelligence (AI) in global health are expanding, yet research remains fragmented and often misaligned with the needs of low-income and middle-income countries (LMICs). To address this misalignment, we conducted a global research priority-setting exercise using the Child Health and Nutrition Research Initiative (CHNRI) method. 155 research ideas were scored by 51 experts based on feasibility, potential impact on disease burden, paradigm shift potential, implementation potential, and equity. Top-ranked priorities focused on epidemic preparedness, including AI-based outbreak prediction, improved diagnostics for infectious diseases, and early-warning systems. Other highly ranked topics included AI-assisted resource allocation, telemedicine, culturally adapted mobile health services, and chronic disease management tools. Experts from LMICs prioritised infectious disease control and diagnostic equity, whereas experts from high-income countries emphasised infrastructure and climate-related analytics. The resulting agenda provides a roadmap for aligning AI and data science research with global health priorities, particularly in LMICs.

Publication Title

Lancet Global Health

ISSN

2572116X

DOI

10.1016/S2214-109X(25)00473-5

Recommended Citation

Song, Peige; Jiang, Denan; Zhou, Jiali; Zhu, Yajie; Manaf, Rosliza Abdul; Bojude, Danladi Adamu; Agbre-Yace, Marie Laurette; Ali, Sajjad; Allen, Omolade; Anyasodor, Anayochukwu Edward; Aranda, Zeus; Bahattab, Awsan; Bodomo, Adams; Borrescio-Higa, Florencia; Buchtova, Marie; Buljan, Nataša; Deshmukh, Vaishali; and Díaz-Castro, Lina, "Research priorities for data science and artificial intelligence in global health: an international consensus exercise" (2026). Research Publications (2026 to 2030). 199.
https://knova.um.edu.my/research_publications_2026_2030/199

Volume

14

Issue

3

First Page

e455