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

Peige Song; Winifred Ekezie; et al. (over 50 authors)

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

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

Peige Song^*
, Winifred Ekezie
, et al. (over 50 authors)

^*Corresponding author for this work

Research output: Contribution to journal › Review article › peer-review

Abstract

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.

Original language	English
Pages (from-to)	E455-E465
Number of pages	11
Journal	The Lancet Global Health
Volume	14
Issue number	3
Early online date	Feb 2026
DOIs	https://doi.org/10.1016/S2214-109X(25)00473-5
Publication status	Published - 1 Mar 2026

Bibliographical note

Copyright © 2026 The Author(s). Published by Elsevier Ltd. This is an
Open Access article under the CC BY 4.0 license.

Funding

The authors thank Ms Natalia Meyer-Gomez for her support to National Institute for Health and Care Research (NIHR)'s EQUI-RESP-AFRICA project. This study was supported by the International Society of Global Health (ISoGH) and NIHR UK EQUI-RESP-AFRICA grant number 156234 (Improving Equity in Respiratory Disease Outcomes in Africa using Data-Driven Tools), which uses UK international development funding from the UK Government to fund and support global health research. EQUI-RESP-AFRICA was commissioned by the NIHR using Official Development Assistance funding. The views expressed in this publication are those of the author(s) and not necessarily those of the NIHR or the ISoGH.

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Research priorities for data science and artificial intelligence in global health- an international consensus exerciseFinal published version, 697 KBLicence: CC BY 4.0

Cite this

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

abstract = "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.",

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N2 - 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.

AB - 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.

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

Abstract

Bibliographical note

Funding

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