Foundation first: a health-system readiness framework for responsible AI adoption in Somalia.
- 1School of Public Health and Nutrition, College of Health Sciences, Amoud University, Borama, Somaliland, Somalia.
- 2Research and Innovation Center, Amoud University, Borama, Somaliland, Somalia.
Abstract
Artificial intelligence (AI) is increasingly positioned as a mechanism for expanding access, strengthening surveillance, improving clinical decisions, and increasing health-system efficiency. Yet AI implementation is path-dependent: its value depends on the health-system, digital, institutional, and social foundations into which it is introduced. Somalia provides a critical test of this proposition because a dynamic telecommunications ecosystem coexists with uneven electricity, broadband use, primary care capacity, routine data quality, digital skills, and governance, particularly across rural, pastoralist, nomadic, displaced, and drought-affected populations. This Perspective develops a foundation-first AI implementation agenda for Somalia. It integrates the World Health Organization health-system building blocks, the Nonadoption, Abandonment, Scale-up, Spread and Sustainability framework, and an equity lens, while aligning proposed actions with the Sustainable Development Goals, Somalia's National Transformation Plan 2025-2029, Health Sector Strategic Plan III, and African Union Agenda 2063. The central argument is that AI should be treated as a capstone of digital transformation rather than its starting point. Reliable energy, inclusive connectivity, strong primary and community care, interoperable information systems, workforce capability, sustainable financing, and accountable governance should precede or accompany AI deployment. This sequencing can convert AI from a potentially exclusionary technology into a context-fit multiplier of health-system capacity.
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BibTeX
@article{dahir2026foundation,
title = {{Foundation first: a health-system readiness framework for responsible AI adoption in Somalia.}},
author = {Hamze G Dahir and Abdisalam Hassan Muse},
journal = {Frontiers in digital health},
year = {2026},
month = oct,
doi = {10.3389/fdgth.2026.1959721},
url = {https://europepmc.org/article/MED/42909091},
note = {CC BY},
}
RIS
TY - JOUR
TI - Foundation first: a health-system readiness framework for responsible AI adoption in Somalia.
AU - Hamze G Dahir
AU - Abdisalam Hassan Muse
PY - 2026
DA - 2026/10/07
JO - Frontiers in digital health
DO - 10.3389/fdgth.2026.1959721
UR - https://europepmc.org/article/MED/42909091
AB - Artificial intelligence (AI) is increasingly positioned as a mechanism for expanding access, strengthening surveillance, improving clinical decisions, and increasing health-system efficiency. Yet AI implementation is path-dependent: its value depends on the health-system, digital, institutional, and social foundations into which it is introduced. Somalia provides a critical test of this proposition because a dynamic telecommunications ecosystem coexists with uneven electricity, broadband use, primary care capacity, routine data quality, digital skills, and governance, particularly across rural, pastoralist, nomadic, displaced, and drought-affected populations. This Perspective develops a foundation-first AI implementation agenda for Somalia. It integrates the World Health Organization health-system building blocks, the Nonadoption, Abandonment, Scale-up, Spread and Sustainability framework, and an equity lens, while aligning proposed actions with the Sustainable Development Goals, Somalia's National Transformation Plan 2025-2029, Health Sector Strategic Plan III, and African Union Agenda 2063. The central argument is that AI should be treated as a capstone of digital transformation rather than its starting point. Reliable energy, inclusive connectivity, strong primary and community care, interoperable information systems, workforce capability, sustainable financing, and accountable governance should precede or accompany AI deployment. This sequencing can convert AI from a potentially exclusionary technology into a context-fit multiplier of health-system capacity.
SN - 2673-253X
ER -