Women's Health

Anne Lee of Brigham and Women's Hospital in the U.S. and Yasir Shafiq of Aga Khan University in Pakistan will develop geospatial models to predict risks of undernutrition among adolescent girls and pregnant and lactating women in settings affected by conflict, climate and COVID-19 to help target interventions. Globally, around 30–40 million pregnant women and 50 million adolescent girls are underweight. Risks of undernutrition have recently been amplified by numerous armed conflicts, climatic shocks such as flooding and the COVID-19 pandemic.

Margaret Kasaro and Soumya Benhabbour of the University of North Carolina at Chapel Hill in the U.S. will evaluate 3D-printed intravaginal ring (IVR) prototypes in Zambia to identify the design most acceptable to women for long-term use against unplanned pregnancy and HIV infection. In Zambia, HIV prevalence remains particularly high among women, and 41% of pregnancies are unplanned. IVRs are an effective, well-tolerated, and women-controlled contraceptive and HIV-preventative; however, their performance has suffered in large-scale clinical trials because of poor adherence.

Faisal Sultan and Sara Khalid of Shaukat Khanum Memorial Cancer Hospital and Research Centre in Pakistan will leverage the power of open-source AI Large Language Models (LLMs) to extract insights more quickly and easily from large volumes of clinical data to support medical decision-making and minimize health disparities in South Asia. Healthcare systems in South Asia have limited resources and the critical information required for decision-making is often buried in patient notes (such as family history, drug adverse events, and social, behavioral, and environmental determinants).

Scott Mahoney of The Health Foundation of South Africa will create an application that combines human expertise with AI technology to produce clinical recommendations from published medical evidence to be used as a decision-support tool for healthcare professionals in low- and middle-income countries. Currently, producing guidelines and support tools relies on manual reading and synthesis by individual clinicians or editorial teams, which is time consuming and can lead to biased coverage.

Olubayo Adekanmbi of Data Science Nigeria in Nigeria will develop a multilingual, voice-based chatbot to demystify complex financial concepts and provide customized financial support to informal traders, women business owners, and smallholder farmers in Nigeria. These groups are often disadvantaged due to their low income and literacy and are historically underserved by conventional financial systems. They will create a chatbot capable of recording transactions from verbal inputs, such as "I bought four oranges at N50 naira," and answering financial questions.

Suzanne Staples of the THINK Tuberculosis and HIV Investigative Network (RF) NPC in South Africa and Kristina Wallengren of THINK International in Denmark will produce a toolkit that leverages ChatGPT for the analysis and interpretation of health program data in low- and middle-income countries (LMICs). Due to resource constraints, data analysis takes a back seat to diagnostics and treatments and is a scarce skill in LMICs, particularly in the public health sector. In addition, health data management is hindered by manual and fragmented electronic datasets.