Africa adopts AI technology in healthcare to boost diagnosis

Operating room with modern monitors and equipment. [Courtesy] African countries are gradually embracing Artificial Intelligence (AI) to improve health outcomes by enhancing diagnostics, treatment, and overall healthcare services. Dr Sabin Nsanzimana, Rwanda's Minister of Health, said AI and digital health solutions are key to building resilient, high-quality, and accessible healthcare systems. Speaking at the Africa Health Agenda International Conference (AHAIC 2025), in Kigali, Rwanda, held on March 2 and 5, 2025, Dr Nsanzimana highlighted the role of technology in addressing the health workers shortage and reducing healthcare costs. "We need to apply on the continent the latest technology and AI system. These equipment and machines should be brought into the system and help young people discover solutions in health," said Dr Nsanzimana. He added, "AI can be used. Because of technology, we do not need much trained doctors but by training in the system, this can happen." Rwanda has an AI policy to support the use of AI while also managing potential risks. The discussion comes at a time when donor funding is reducing, an issue Dr Nsanzimana warned could strain healthcare systems, especially following the signing of a stop order by US President Donald Trump. AI-driven innovations, such as automated diagnostics, predictive analytics, and telemedicine, are making healthcare more accessible, especially in remote areas. The adoption of AI in healthcare is transforming maternal health services across Africa, bringing innovative solutions to underserved communities. One such breakthrough, highlighted during AHAIC 2025, is BabyChecker, an AI-powered portable ultrasound device developed by Delft Imaging, a Netherlands-based social enterprise. BabyChecker represents a significant milestone in maternal healthcare, providing a cost-effective and efficient solution for early pregnancy risk detection. Designed specifically for low- and middle-income countries, this portable device comprises a smartphone, an ultrasound probe, a mobile application, and a user instructions card. Unlike conventional ultrasound machines, which require trained professionals, BabyChecker leverages AI to analyse ultrasound images. The solution helps community health workers (CHWs) with minimal training to conduct ultrasound screenings easily, with a focus on communities with limited access to healthcare due to high maternal and child deaths. Akshay Rajagopal, BabyChecker Project Manager at Delft Imaging, explains that the technology analyses obstetric ultrasound scans to identify gestational age and high-risk pregnancies, such as foetal malposition, placenta previa, and multiple pregnancies. The device consists of a smartphone, ultrasound probe, mobile application and user instructions card. The app guides healthcare workers to perform a six-sweep protocol involving three vertical and three horizontal sweeps across the pregnant belly. Once the six sweeps are completed, AI processes the ultrasound images in just five seconds, providing critical insights. The results are displayed on the smartphone as traffic lights, namely green, yellow, and red. The green signal indicates that pregnancy is progressing well and that the woman can continue her routine antenatal care, whereas yellow indicates potential complications, but they are not high-risk, while red indicates a risky pregnancy that requires immediate referral to save the lives of a mother and child. "BabyChecker is designed for community health workers. No internet, no electricity, this is for rural settings," he explained. He added: "By equipping CHWs with AI-driven screening tools, we can reduce maternal mortality significantly." Data reveals that every day, about 800 women die from preventable causes related to pregnancy and childbirth, globally. Akshay urged governments and funders to invest in AI-driven solutions to support maternal healthcare in the hardest-to-reach areas, amid increasing healthcare demand with shortage of specialised workers.According to the World Health Organisation (WHO), Africa will have a shortage of 6.1 million health workers by 2030, necessitating an increased need for skilled health workers in the region. However, with a projected 6.1 million health workers' shortage by the same year 2030, the need to rapidly build and upskill new and existing health worker capacity across Africa gets increasingly dire. The cost of in-service training is high and oftentimes, health workers are unable to leave their health posts to go and upgrade their skills at a centralized locality. Jibu, which is Amref Health Africa's premier digital learning platform, is designed to deliver capacity at scale to build a fit-for-purpose health workforce for the African continent. Offering online and hybrid training courses with rich interactivity, Jibu meets the health sector needs by imparting much needed skills and knowledge to Africa's young and experienced health workforce alike, using the tool in their hands. Jibu is accessible through the web, and mobile app (Jibu on Android and iPhone) enabling a seamless, online and offline learning experience with learner support on the go. With over 98,000 users onboard, Jibu's reach cuts across 37 countries in the Sub-Saharan Africa region and continues to grow.The platform has multilingual capabilities learning and offers over 35 courses across diverse health themes, including mental health, maternal and newborn health, communicable and non-communicable diseases, youth-focused courses, as well as leadership, management, and governance for health systems strengthening. These courses are developed in collaboration with Ministries of Health, subject-matter experts, as well as training institutions, and are guided by policy and research in human resources for health.The platform also offers micro-courses, which are short, 5-10-minute-long courses to raise awareness on emerging health issues such as basics of mpox - useful during the outbreak, understanding AI in health, phishing, youth advocacy and depression. "Jibu is now integrating AI into its learning delivery, a move that will significantly enhance the learning experience by personalizing content based on user preferences and learning behaviours. AI in Jibu will also improve learning support through chatbots and virtual tutors, ensuring 24/7 interactive learning. Additionally, AI-driven insights will allow for better content design and data-informed decision-making by analysing learner-platform engagement patterns," said Raphael Muia, Digital Learning Technology Officer at Amref Health Africa. In addition, Miscrosof, Ministry of Health Kenya, University of South California and Amref Health have developed an AI-powered tool to predict malnutrition, particularly among children at risk.Samuel Mburu, Head of Digital Transformation Excellence at Amref Health, said the technology predicts where people, particularly children who are at risk of malnutrition, by creating an algorithm using data collected in dispensaries and healthcare facilities. The data allows for an in-depth examination of the intersection between climate change and health, particularly malnutrition. Mburu explained that the tool develops a digital dashboard that visualizes malnutrition data on a map. Planetary data is derived from satellite images, provides real-time information on vegetation cover, farming activities, and temperature fluctuations. "By integrating AI and planetary data, Amref Health Africa aims to provide data-driven solutions to improve health outcomes in these vulnerable communities,' noted Mburu. Apart from analysing malnutrition, the AI can be adopted for resource mobilisation and supply chain management within the public health sector. "We are looking at how AI can predict future needs to enhance the supply chain and ensure timely interventions. While nutrition is our primary focus, we are also exploring other segments within the health space using the AI tool," he said. The technology developed in Kinango, Kwale country is set to be rolled out across the African to revolutionize how health challenges are addressed through data-driven decision-making.

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