Digitalization of health care in low- and middle-income countries
The rising incidence of noncommunicable diseases, combined with the costs of mitigating climate change, sovereign debt and regional conflicts, is undermining global health security and threatening progress towards achieving the sustainable development goals of the United Nations. The negative impact of these polycrises is disproportionately borne by low- and middle-income countries, which have the highest disease burden and lowest health-care spending. Health digitalization is emerging as a promising countermeasure, accelerated by artificial intelligence (AI) software and quantum computing hardware. We provide a multisector critical analysis of the three key enablers - governance, infrastructure and security - of the responsible AI-enabled digitalization for safe, affordable, equitable and sustainable health-care systems in low- and middle-income countries. We consider leading use cases in public-private partnerships, democratized sovereign AI and embedded human security. Our analysis proposes that these use cases demonstrate how digital AI-accelerated global health may be advanced as human-centred managed strategic competition. We conducted our analysis through an inclusive range of theoretical perspectives and practical experience spanning academia, industry and practice across the world. We provide recommendations for the responsible management of the key enablers to accelerate global health for all. We anticipate that this paper will be useful for public health decision-makers, both in low- and middle-income countries leading local health digitalization, and in high-income countries supporting this transaction through their technologies, funding and knowledge exchange.
Authors
- Patience Oduor (ORCID: https://orcid.org/0000-0001-9877-4466)
- María Elizabeth de los Rios Uriarte (ORCID: https://orcid.org/0000-0001-9600-445X)
- Timothy R. Aksamit (ORCID: https://orcid.org/0000-0002-5243-5394)
- Colleen M. Gallagher (ORCID: https://orcid.org/0000-0003-1100-726X)
- Maria Ines Girault
- Oleg Sinyavskiy
- Donald Kokonya (ORCID: https://orcid.org/0000-0002-4206-9933)
- Joyce Rayel (ORCID: https://orcid.org/0009-0005-1015-7612)
- Sagar B. Dugani (ORCID: https://orcid.org/0000-0001-7858-1317)
- Dominique Monlezun (ORCID: https://orcid.org/0000-0001-7671-1886)
- Claudia Sotomayor (ORCID: https://orcid.org/0000-0001-7408-5180)
- Alberto García Gómez (ORCID: https://orcid.org/0000-0001-9090-0966)
- Lillian Omutoko
Institutions
- University of Nairobi (KE)
- Mayo Clinic (US)
- Kenyatta University (KE)
- Georgetown University (US)
- Kazakh National Medical University (KZ)
- Universidad Anáhuac México Sur (MX)
- Georgetown University Medical Center (US)
- Pontifical Athenaeum Regina Apostolorum (IT)
- Bicol University (PH)
- Universidad Anáhuac (MX)
- Mayo Clinic in Arizona (US)
- University of Global Health Equity (RW)
- Mayo Clinic in Florida (US)
- Masinde Muliro University of Science and Technology (KE)
Publication Details
- Journal
- Bulletin of the World Health Organization
- Published
- 2025-02-01
- DOI
- https://doi.org/10.2471/blt.24.291643
- Citations
- 25
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 16.36