Innovation Hub on Artificial Intelligence for Sexual, Reproductive and Maternal Health in Africa
Innovation
Uganda
About
Maternal, sexual, and reproductive health indicators in lower-income countries often lag behind Sustainable Development Goals, with challenges like high maternal mortality and limited access to contraception persisting. Digital technologies, including artificial intelligence (AI), show promise for improving these health outcomes, but concerns about risks and ethical implications remain.
In the first phase of AI4D, this project established a research hub to advance maternal, sexual, and reproductive health and rights in sub-Saharan Africa through the responsible development and deployment of AI innovations. These innovations prioritize ethics, inclusivity, human rights, and sustainability while strengthening health systems.
In its second phase, the hub will expand its focus on scaling responsible AI innovations to further enhance health outcomes and system resilience across the continent.
Subprojects
This project aims to develop an AI enabled Chatbot that will provide personalised guidance to pregnant women and their partners on STIs.
This project aims to use mathematical modelling to identify key variables that predict sexually transmitted diseases among university students. The team will also develop a chatbot to disseminate information and help students get help freely.
This project will build a web application that will use a classification machine learning algorithm to predict the risk of miscarriage among women seeking antenatal care, while identifying the major factors that influence a pregnancy ending in a miscarriage.
This project will develop an ML model by comparing the prediction performance of nine classification models to identify teenage patients at risk of gestational hypertension.
Using existing datasets, this project will leverage ML and AI modelling to identify, quantify, analyse, and map high-risk populations that are eligible for PrEP and can pay for the services.
This team will use a previously developed tool for screening TB using chest X-Rays to identify the disease in people living with HIV.
This project seeks to utilise machine learning to break the barriers inhibiting adolescents with hearing, speech and visual disabilities from accessing SRH information and services. A mixed-methods research design will be adopted to collect data from in-school adolescents with hearing, speech and visual disabilities, as well as key stakeholders.
This project will build a Chatbot that will be used to increase HIV knowledge and HIV testing, while enhancing status awareness and status disclosure to sexual partners within the population group. It will also address discrimination and HIV-related stigma toward adolescents and young adults seeking testing and treatment.
This research project will follow up young girls and adolescent women aged 15-24 years using selected modern contraceptive methods and attending family planning clinics for a period of 12 months. The data collected will be used to develop an AI model that will predict likelihood of contraceptive side effects and contraceptive failure.
