Flipping the script: co-creation and local innovation in AI for global health paradigms
4 affiliations
- 1Department of Molecular Biosciences, The University of Texas at Austin, Austin, Texas, USA
- 2Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
- 3ICanServe Foundation, Manila, Philippines
- 4Department of Radiation Oncology, University of Santo Tomas, Manila, Philippines
Abstract
Artificial intelligence (AI) in global health is often framed through a narrative of technological salvation, in which models and systems developed in high-income countries (HICs) are exported to low and middle-income countries (LMICs) as turnkey solutions. This top-down paradigm overlooks local expertise and frequently produces context-mismatched, unsustainable tools. We argue for a shift towards cocreation and reverse knowledge flow, in which LMIC innovators lead AI initiatives across the entire pipeline, from problem definition and data stewardship to model development, validation, deployment and governance. Rather than ‘contextualising’ HIC-built systems, cocreated AI is rooted in local health system realities, leveraging local providers and community health workers, cost-conscious innovation and decentralised care models.
Early examples from LMICs suggest that locally led AI decision-support platforms can improve access, quality and continuity of care. Building on emerging South–South collaborations, we describe how networks of LMIC-based AI researchers can generate solutions validated across diverse populations and inform new global standards.
To enable this, international funding, evaluation and governance architectures must be restructured to require LMIC coleadership, data sovereignty, equitable intellectual property arrangements and long-term capacity building. Grounding global AI norms in ethical frameworks originating in LMICs, such as African discourses on data sovereignty and Ubuntu ethics, can prevent the universalisation of HIC-centric values. Repositioning LMICs as engines of innovation is a practical necessity to realise equitable, effective AI for global health.
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BibTeX
@article{patel2026flipping,
title = {{Flipping the script: co-creation and local innovation in AI for global health paradigms}},
author = {Milit S Patel and Maria Jocelyn Kara Magsanoc-Alikpala and Luisa E Jacomina and Luke R G Pike and Puneeth Iyengar and Nancy Y Lee and Edward Christopher Dee},
journal = {BMJ Health \& Care Informatics},
year = {2026},
month = oct,
doi = {10.1136/bmjhci-2026-102172},
url = {https://doi.org/10.1136/bmjhci-2026-102172},
note = {CC BY-NC 4.0},
}
RIS
TY - JOUR
TI - Flipping the script: co-creation and local innovation in AI for global health paradigms
AU - Milit S Patel
AU - Maria Jocelyn Kara Magsanoc-Alikpala
AU - Luisa E Jacomina
AU - Luke R G Pike
AU - Puneeth Iyengar
AU - Nancy Y Lee
AU - Edward Christopher Dee
PY - 2026
DA - 2026/10/28
JO - BMJ Health & Care Informatics
DO - 10.1136/bmjhci-2026-102172
UR - https://doi.org/10.1136/bmjhci-2026-102172
AB - Artificial intelligence (AI) in global health is often framed through a narrative of technological salvation, in which models and systems developed in high-income countries (HICs) are exported to low and middle-income countries (LMICs) as turnkey solutions. This top-down paradigm overlooks local expertise and frequently produces context-mismatched, unsustainable tools. We argue for a shift towards cocreation and reverse knowledge flow, in which LMIC innovators lead AI initiatives across the entire pipeline, from problem definition and data stewardship to model development, validation, deployment and governance. Rather than ‘contextualising’ HIC-built systems, cocreated AI is rooted in local health system realities, leveraging local providers and community health workers, cost-conscious innovation and decentralised care models. Early examples from LMICs suggest that locally led AI decision-support platforms can improve access, quality and continuity of care. Building on emerging South–South collaborations, we describe how networks of LMIC-based AI researchers can generate solutions validated across diverse populations and inform new global standards. To enable this, international funding, evaluation and governance architectures must be restructured to require LMIC coleadership, data sovereignty, equitable intellectual property arrangements and long-term capacity building. Grounding global AI norms in ethical frameworks originating in LMICs, such as African discourses on data sovereignty and Ubuntu ethics, can prevent the universalisation of HIC-centric values. Repositioning LMICs as engines of innovation is a practical necessity to realise equitable, effective AI for global health.
SN - 2632-1009
ER -