Maternal, Newborn, and Adolescent Health

Climate change effects on maternal and neonatal health caused by heat stress, and air pollution can be detrimental in the immediate and long term. Heat exposure has been associated with the risk of low birth weight, preterm, and stillbirths. Dehydration in pregnant women can trigger the early onset of, as well as prolonged labour. Heat stress can lead to reduced blood flow and oxygen to the placenta, affecting fetal growth, it can increase blood pressure and pre-eclampsia in pregnancy, and heighten the risk of postpartum depression and posttraumatic disorder.

Robert Korom of Penda Health Limited in Kenya will integrate ChatGPT-4 into their established patient communication system to increase consultation efficiency and the speed of delivering accurate health information in Kenya. Their existing chat-based digital health solution relies on a dedicated team of clinicians and call center agents to serve low-income Kenyans; however, increasing needs are leading to longer response times.

Minh Do of Fulbright University Vietnam in Vietnam will create a chatbot "NướcGPT" (Nước means water in Vietnamese) that combines cutting-edge AI tools with a user-friendly interface in the local language to support the management of salinity intrusion in the Mekong Delta. The Mekong Delta, home to 21.5 million Vietnamese, is suffering from increased saltwater intrusion caused by multiple factors including climate change.

Tonee Ndungu of Kytabu Company Ltd. in Kenya will develop a comprehensive AI-powered mobile application, SOMANASI (derived from the Swahili words meaning "learn together") to provide personalized education to every student in Kenya. Kenya suffers from widespread educational inequities with many students failing to receive individualized attention. The application will harness ChatGPT-4 and act as an intelligent virtual tutor that delivers tailored content, adaptive learning experiences, and interactive guidance.

Imad Elhajj of the Humanitarian Engineering Initiative of the American University of Beirut in Lebanon will use Large Language Models (LLMs) to develop an interactive community health promotion platform with a chatbot that provides accurate health messages and real-time responses to queries on platforms like WhatsApp to vulnerable populations in Lebanon and Jordan. They will process texts from trusted websites, documents, and other text repositories, such as UNICEF and the WHO, into smaller text segments.

Suhani Jalota of the Myna Mahila Foundation in India will build a chatbot, Myna Bolo, by incorporating Large Language Models (LLMs) into their health application to provide tailored sexual and reproductive health services through smartphones, via text or audio, in local languages to women in India. In India, 71% of girls report not knowing about menstruation before their first period. This is because of limited access to unbiased information due to stigma, discrimination, and lack of resources.

Shashi Jain of the Indian Institute of Science in India in collaboration with Uma Urs from Oxford Brookes University in the United Kingdom along with colleagues from Akaike and Kotak Mahindra Bank also in India, will build a GPT-enabled AI bot called SATHI, which stands for Scheme, Access, Training, Help, and Inclusion, to deliver information on the latest government financial schemes that support sectors, like micro-enterprises and farms, to potential customers and providers in rural and suburban India.

Amrita Mahale of ARMMAN in India, in collaboration with colleagues at ARTPARK also in India, will integrate an LLM-powered co-pilot into an existing learning and support application to improve the training of auxiliary nurses and midwives in India so they can better manage high-risk pregnancies. One woman dies in childbirth every twenty minutes in India. Many maternal and infant deaths could be prevented by improving access to critical care information and ensuring that health workers can detect risk factors and treat complications early on.

Augustino Hellar of Prime Health Initiative Tanzania will develop a machine learning algorithm for the early detection of high-risk pregnancies and integrate it into an existing mobile health application to help reduce maternal mortality in Tanzania. The existing application is being used by health care workers across 23,000 households in Tanzania’s Geita Region to track health during pregnancy and provide health education via SMS.

Ifeoluwa Olokode of Helium Health in Nigeria will develop a digital antenatal risk stratification tool to determine the risk of maternal mortality for pregnant women in Nigeria and link them to appropriate care services to reduce maternal death rates. Nigeria has one of the highest burdens of maternal mortality, with the biggest driver being a delay in the decision to seek health care.