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

Peige Song; Denan Jiang; Jiali Zhou; Yajie Zhu; Rosliza Abdul Manaf; Danladi Adamu Bojude; Marie Laurette Agbre-Yace; Sajjad Ali; Omolade Allen; Anayochukwu Edward Anyasodor; Zeus Aranda; Awsan Bahattab; Adams Bodomo; Florencia Borrescio-Higa; Marie Buchtova; Nataša Buljan; Vaishali Deshmukh; Lina Díaz-Castro; Sohaila Cheema; Winifred Ekezie; Kurubaran Ganasegeran; Balasankar Ganesan; Anton Glasnović; Christopher J. Graham; Mila Nu Nu Htay; Chinonso Igwesi-Chidobe; Per Ole Iversen; Mohammad Mainul Islam; Abdulkarim Jafar Karim; Brane Kalpič; Oluchi Kanma-Okafor; Giuseppe Lanza; Saturnino Luz; Wiriya Mahikul; Dunja Mladenić; Anthony Muchai Manyara; Bala Munipalli; Nellie Myburgh; Zhi Xiang Ng; Georgios Nikolopoulos; Chulwoo Park; Jay J. Park; Prince Peprah; Klara Rudan; Syed Ahmar Shah; Ting Shi; Gregor tiglic; Rosnah Sutan; Athanasios Tsanas; Holly Tibble; Abdul Tawab Khpalwak; Mark Tomlinson; Sandro Vento; Josipa Vlasac Glasnović; Liang Wang; Jingyi Xu; Jianrong Zhang; Yanfeng Zhang; Eamon Sheikh; Obianuju B. Ozoh; Apostolos Tsiachristas; Davies Adeloye; Steven Kerr; Mili Sanwalka; Stjepan Orešković; Aziz Sheikh; Igor Rudan

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
, Denan Jiang
, Jiali Zhou
, Yajie Zhu
, Rosliza Abdul Manaf
, Danladi Adamu Bojude
, Marie Laurette Agbre-Yace
, Sajjad Ali
, Omolade Allen
, Anayochukwu Edward Anyasodor
, Zeus Aranda
, Awsan Bahattab
, Adams Bodomo
, Florencia Borrescio-Higa
, Marie Buchtova
, Nataša Buljan
, Vaishali Deshmukh
, Lina Díaz-Castro
, Sohaila Cheema
, Winifred Ekezie

Kurubaran Ganasegeran, Balasankar Ganesan, Anton Glasnović, Christopher J. Graham, Mila Nu Nu Htay, Chinonso Igwesi-Chidobe, Per Ole Iversen, Mohammad Mainul Islam, Abdulkarim Jafar Karim, Brane Kalpič, Oluchi Kanma-Okafor, Giuseppe Lanza, Saturnino Luz, Wiriya Mahikul, Dunja Mladenić, Anthony Muchai Manyara, Bala Munipalli, Nellie Myburgh, Zhi Xiang Ng, Georgios Nikolopoulos, Chulwoo Park, Jay J. Park, Prince Peprah, Klara Rudan, Syed Ahmar Shah, Ting Shi, Gregor tiglic, Rosnah Sutan, Athanasios Tsanas, Holly Tibble, Abdul Tawab Khpalwak, Mark Tomlinson, Sandro Vento, Josipa Vlasac Glasnović, Liang Wang, Jingyi Xu, Jianrong Zhang, Yanfeng Zhang, Eamon Sheikh, Obianuju B. Ozoh, Apostolos Tsiachristas, Davies Adeloye, Steven Kerr, Mili Sanwalka, Stjepan Orešković, Aziz Sheikh, Igor Rudan

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3 Citas (Scopus)

Resumen

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.

Idioma original	Inglés
Páginas (desde-hasta)	e455-e465
Publicación	The Lancet Global Health
Volumen	14
N.º	3
DOI	https://doi.org/10.1016/S2214-109X(25)00473-5
Estado	Publicada - mar 2026

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Song, P., Jiang, D., Zhou, J., Zhu, Y., Manaf, R. A., Bojude, D. A., Agbre-Yace, M. L., Ali, S., Allen, O., Anyasodor, A. E., Aranda, Z., Bahattab, A., Bodomo, A., Borrescio-Higa, F., Buchtova, M., Buljan, N., Deshmukh, V., Díaz-Castro, L., Cheema, S., ... Rudan, I. (2026). Research priorities for data science and artificial intelligence in global health: an international consensus exercise. The Lancet Global Health, 14(3), e455-e465. https://doi.org/10.1016/S2214-109X(25)00473-5

@article{7598ce2409354d64948ac951b3cd8868,

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

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.",

author = "Peige Song and Denan Jiang and Jiali Zhou and Yajie Zhu and Manaf, \{Rosliza Abdul\} and Bojude, \{Danladi Adamu\} and Agbre-Yace, \{Marie Laurette\} and Sajjad Ali and Omolade Allen and Anyasodor, \{Anayochukwu Edward\} and Zeus Aranda and Awsan Bahattab and Adams Bodomo and Florencia Borrescio-Higa and Marie Buchtova and Nata{\v s}a Buljan and Vaishali Deshmukh and Lina D{\'i}az-Castro and Sohaila Cheema and Winifred Ekezie and Kurubaran Ganasegeran and Balasankar Ganesan and Anton Glasnovi{\'c} and Graham, \{Christopher J.\} and Htay, \{Mila Nu Nu\} and Chinonso Igwesi-Chidobe and Iversen, \{Per Ole\} and Islam, \{Mohammad Mainul\} and Karim, \{Abdulkarim Jafar\} and Brane Kalpi{\v c} and Oluchi Kanma-Okafor and Giuseppe Lanza and Saturnino Luz and Wiriya Mahikul and Dunja Mladeni{\'c} and Manyara, \{Anthony Muchai\} and Bala Munipalli and Nellie Myburgh and Ng, \{Zhi Xiang\} and Georgios Nikolopoulos and Chulwoo Park and Park, \{Jay J.\} and Prince Peprah and Klara Rudan and Shah, \{Syed Ahmar\} and Ting Shi and Gregor tiglic and Rosnah Sutan and Athanasios Tsanas and Holly Tibble and Khpalwak, \{Abdul Tawab\} and Mark Tomlinson and Sandro Vento and Glasnovi{\'c}, \{Josipa Vlasac\} and Liang Wang and Jingyi Xu and Jianrong Zhang and Yanfeng Zhang and Eamon Sheikh and Ozoh, \{Obianuju B.\} and Apostolos Tsiachristas and Davies Adeloye and Steven Kerr and Mili Sanwalka and Stjepan Ore{\v s}kovi{\'c} and Aziz Sheikh and Igor Rudan",

note = "Publisher Copyright: {\textcopyright} 2025 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license. http://creativecommons.org/licenses/by/4.0/",

year = "2026",

month = mar,

doi = "10.1016/S2214-109X(25)00473-5",

language = "English",

volume = "14",

pages = "e455--e465",

journal = "The Lancet Global Health",

issn = "2214-109X",

publisher = "Elsevier Ltd.",

number = "3",

}

Song, P, Jiang, D, Zhou, J, Zhu, Y, Manaf, RA, Bojude, DA, Agbre-Yace, ML, Ali, S, Allen, O, Anyasodor, AE, Aranda, Z, Bahattab, A, Bodomo, A, Borrescio-Higa, F, Buchtova, M, Buljan, N, Deshmukh, V, Díaz-Castro, L, Cheema, S, Ekezie, W, Ganasegeran, K, Ganesan, B, Glasnović, A, Graham, CJ, Htay, MNN, Igwesi-Chidobe, C, Iversen, PO, Islam, MM, Karim, AJ, Kalpič, B, Kanma-Okafor, O, Lanza, G, Luz, S, Mahikul, W, Mladenić, D, Manyara, AM, Munipalli, B, Myburgh, N, Ng, ZX, Nikolopoulos, G, Park, C, Park, JJ, Peprah, P, Rudan, K, Shah, SA, Shi, T, tiglic, G, Sutan, R, Tsanas, A, Tibble, H, Khpalwak, AT, Tomlinson, M, Vento, S, Glasnović, JV, Wang, L, Xu, J, Zhang, J, Zhang, Y, Sheikh, E, Ozoh, OB, Tsiachristas, A, Adeloye, D, Kerr, S, Sanwalka, M, Orešković, S, Sheikh, A & Rudan, I 2026, 'Research priorities for data science and artificial intelligence in global health: an international consensus exercise', The Lancet Global Health, vol. 14, n.º 3, pp. e455-e465. https://doi.org/10.1016/S2214-109X(25)00473-5

TY - JOUR

T1 - Research priorities for data science and artificial intelligence in global health

T2 - an international consensus exercise

AU - Song, Peige

AU - Jiang, Denan

AU - Zhou, Jiali

AU - Zhu, Yajie

AU - Manaf, Rosliza Abdul

AU - Bojude, Danladi Adamu

AU - Agbre-Yace, Marie Laurette

AU - Ali, Sajjad

AU - Allen, Omolade

AU - Anyasodor, Anayochukwu Edward

AU - Aranda, Zeus

AU - Bahattab, Awsan

AU - Bodomo, Adams

AU - Borrescio-Higa, Florencia

AU - Buchtova, Marie

AU - Buljan, Nataša

AU - Deshmukh, Vaishali

AU - Díaz-Castro, Lina

AU - Cheema, Sohaila

AU - Ekezie, Winifred

AU - Ganasegeran, Kurubaran

AU - Ganesan, Balasankar

AU - Glasnović, Anton

AU - Graham, Christopher J.

AU - Htay, Mila Nu Nu

AU - Igwesi-Chidobe, Chinonso

AU - Iversen, Per Ole

AU - Islam, Mohammad Mainul

AU - Karim, Abdulkarim Jafar

AU - Kalpič, Brane

AU - Kanma-Okafor, Oluchi

AU - Lanza, Giuseppe

AU - Luz, Saturnino

AU - Mahikul, Wiriya

AU - Mladenić, Dunja

AU - Manyara, Anthony Muchai

AU - Munipalli, Bala

AU - Myburgh, Nellie

AU - Ng, Zhi Xiang

AU - Nikolopoulos, Georgios

AU - Park, Chulwoo

AU - Park, Jay J.

AU - Peprah, Prince

AU - Rudan, Klara

AU - Shah, Syed Ahmar

AU - Shi, Ting

AU - tiglic, Gregor

AU - Sutan, Rosnah

AU - Tsanas, Athanasios

AU - Tibble, Holly

AU - Khpalwak, Abdul Tawab

AU - Tomlinson, Mark

AU - Vento, Sandro

AU - Glasnović, Josipa Vlasac

AU - Wang, Liang

AU - Xu, Jingyi

AU - Zhang, Jianrong

AU - Zhang, Yanfeng

AU - Sheikh, Eamon

AU - Ozoh, Obianuju B.

AU - Tsiachristas, Apostolos

AU - Adeloye, Davies

AU - Kerr, Steven

AU - Sanwalka, Mili

AU - Orešković, Stjepan

AU - Sheikh, Aziz

AU - Rudan, Igor

PY - 2026/3

Y1 - 2026/3

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/105030633358

U2 - 10.1016/S2214-109X(25)00473-5

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

M3 - Review article

C2 - 41713447

AN - SCOPUS:105030633358

SN - 2214-109X

VL - 14

SP - e455-e465

JO - The Lancet Global Health

JF - The Lancet Global Health

IS - 3

ER -

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

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