5161 12 archive 3 disk0/00/00/51/61 2026-04-22 10:52:23 2026-04-22 10:52:23 2026-04-22 10:52:23 data_collection show Waiswa Joseph Akuze 0000-0001-8221-902X TRUE 7c1830f2525aef53c74381d7ad9a4ba8 Otieno Grieven P. FALSE Amare Samson Yohannes FALSE Ngatia Bancy 0000-0001-6111-2418 TRUE Wanduru Phillip 0000-0001-5934-2505 FALSE Kirakoya-Samadoulougou Fati FALSE Kananura Rornald Muhumuza FALSE Amouzou Agbessi FALSE Estifanos Abiy Seifu FALSE Ohuma Eric O. 0000-0002-3116-2593 TRUE c5508e47ee8ebe50a3bf3a07ebcd0ac5 London School of Hygiene & Tropical Medicine, London, United Kingdom Additional file 3 of Data science and artificial intelligence for maternal, newborn and child health: scoping review and thematic analysis EPIH Data science Artificial intelligence Maternal health Newborn health Child health Africa Scoping review Data collated as part of a scoping review and thematic analysis on the topic of data science and artificial intelligence for maternal, newborn and child health. 2026-01-27 published Figshare Dataset Study authors XMCH CompilationSynthesis Aggregation none Fogarty International Center National Institutes of Health Bill and Melinda Gates Foundation Dataset Quantitative en Figshare https://doi.org/10.6084/m9.figshare.31153952.v1 dataresource Data science and artificial intelligence for maternal, newborn and child health: scoping review and thematic analysis. https://researchonline.lshtm.ac.uk/id/eprint/4678282 Fogarty International Center https://doi.org/10.13039/100000061 National Institutes of Health https://doi.org/10.13039/100000002 Bill and Melinda Gates Foundation https://doi.org/10.13039/100000865 2026-01-27