{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T20:48:11Z","timestamp":1786654091625,"version":"build-2736575974"},"reference-count":26,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J Med Internet Res"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n AI has the potential to transform health care in low- and middle-income countries, where access to quality care remains limited. Maternal, sexual, and reproductive health (MSRH) outcomes are especially poor due to resource shortages, financial barriers, and geographic inequities. With thoughtful implementation, AI could help address these gaps through innovations in diagnostics, health education chatbots, and telemedicine. However, responsible use is essential to ensure AI reduces, rather than exacerbates, health disparities between high- and low-income regions.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n Our study examines the perceptions, uses, benefits, and challenges of AI in MSRH among medical professionals, community members, and AI experts, guided by the diffusion of innovations theory.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n We conducted an exploratory qualitative study involving a round table discussion, key informant interviews, focus group discussions, and stakeholder meetings, to examine the perceptions of health workers, policymakers, AI researchers and implementers, as well as Community Advisory Board members. We explored the opportunities, risks, limitations, and best practices for responsible AI in MSRH in sub-Saharan Africa. Framework analysis was used to analyze the collected data, and 3 member-check sessions were conducted to verify the accuracy of the findings. Finally, the themes derived from the data were mapped onto the diffusion of innovations theory to guide reporting of the study findings.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n The study recruited 59 participants (35 male and 24 female), across the different data collection methods: round table discussion (16 participants), key informant interviews (10 participants), focus group discussions (7 participants), and stakeholder meetings (26 participants). We found a widespread lack of understanding and awareness of AI among both health workers and the general community. Among participants who shared their perspectives, 2 overarching themes emerged around the current and potential uses of AI innovations: filling gaps when skilled and experienced health personnel are not consistently available and targeting high-priority health activities or patients. Participants also emphasized several critical considerations. Building trust among health care providers, patients, and the broader community was seen as essential, alongside addressing language and cultural diversity, both of which require deliberate capacity strengthening. Ensuring equity and sustainability through cocreation strategies was equally stressed. Key concerns raised included ethics, cost, health literacy, and data biases.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n Our findings will inform the development of a continent-wide AI hub for MSRH, highlighting barriers and opportunities for improving health care access. We aim to support policymakers, researchers, and implementers in using AI to promote equitable maternal, sexual, and reproductive health care delivery across Africa.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/84378","type":"journal-article","created":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T20:55:09Z","timestamp":1780606509000},"page":"e84378-e84378","source":"Crossref","is-referenced-by-count":0,"title":["Exploring Perceptions of Leveraging AI to Improve Outcomes in Maternal, Sexual, and Reproductive Health in Sub-Saharan Africa: Exploratory Qualitative Study"],"prefix":"10.2196","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0085-3498","authenticated-orcid":false,"given":"Rachel","family":"King","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0049-3787","authenticated-orcid":false,"given":"Elizabeth","family":"Oseku","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1157-9797","authenticated-orcid":false,"given":"Cecilia","family":"Akatukwasa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-8492-6479","authenticated-orcid":false,"given":"Moreen","family":"Nanyonjo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9798-2191","authenticated-orcid":false,"given":"Joshua","family":"Beinomugisha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1802-434X","authenticated-orcid":false,"given":"Joan","family":"Akullo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7457-5747","authenticated-orcid":false,"given":"Jackie","family":"Ssemata","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9297-1311","authenticated-orcid":false,"given":"Rosalind","family":"Parkes-Ratanshi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,8,13]]},"reference":[{"key":"R1","unstructured":"World Health Organization global adult estimates. 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