Data science and artificial intelligence for maternal, newborn and child health: scoping review and thematic analysis
The limited application of data science and artificial intelligence in MNCH at the national level highlights a significant gap in Africa. Our review found that 28 studies (53.8%) were research projects compared to operational implementations integrated into routine systems to inform policy. Main barriers include inadequate infrastructure, limited data stewardship capacity, and insufficient government commitment. Opportunities exist through Africa's youthful demographics, expanding mobile technology, and latecomer advantage in digital innovation.
Authors
- Agbessi Amouzou (ORCID: https://orcid.org/0000-0002-6262-3866)
- Phillip Wanduru (ORCID: https://orcid.org/0000-0001-5934-2505)
- Grieven P. Otieno
- Eric O. Ohuma (ORCID: https://orcid.org/0000-0002-3116-2593)
- Samson Yohannes Amare (ORCID: https://orcid.org/0000-0002-5425-1126)
- Rornald Muhumuza Kananura (ORCID: https://orcid.org/0000-0002-9915-1989)
- Abiy Seifu Estifanos (ORCID: https://orcid.org/0000-0002-6833-9823)
- Fati Kirakoya‐Samadoulougou (ORCID: https://orcid.org/0000-0002-9584-6329)
- Bancy Ngatia (ORCID: https://orcid.org/0000-0001-6111-2418)
Institutions
- Université Libre de Bruxelles (BE)
- Johns Hopkins University (US)
- Karolinska Institutet (SE)
- London School of Hygiene & Tropical Medicine (GB)
- African Population and Health Research Center (KE)
- Directorate-General Joint Research Centre (BE)
- Addis Ababa University (ET)
- Makerere University (UG)
- Mekelle University (ET)
Publication Details
- Journal
- BMC Public Health
- Published
- 2025-11-28
- DOI
- https://doi.org/10.1186/s12889-025-25430-0
- Citations
- 1
- Primary Topic
- Mobile Health and mHealth Applications
- Type
- article
- Field-Weighted Citation Impact
- 2.82