Alain Ndayishimiye of the Center for AI Policy and Innovation Ltd. in Rwanda will integrate a translation model with GPT-4 to produce a health service support tool in the national language, bypassing the need to build language-specific LLMs from scratch. LLMs have broad and powerful applications for improving public services such as education and healthcare by bridging information gaps across different cohorts.
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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.
Martin Mwangi of Intellisoft Consulting Ltd. in Kenya will build an application-supported LLM to improve knowledge, attitudes, and practices surrounding the risk factors for non-communicable diseases (NCD) for young people in Kenya. NCDs constitute the leading cause of mortality globally, accounting for three-quarters of deaths worldwide. Many Kenyans lack information on NCDs and their major risk factors, which include unhealthy diet, physical inactivity, and harmful alcohol use.
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).
Bishesh Khanal of the Nepal Applied Mathematics and Informatics Institute for Research in Nepal will assess LLMs for their ability to provide accurate information on sexual, reproductive, and maternal health (SRMH) topics in Nepali to the general public and female community health volunteers. In Nepal, limited access to SRMH resources due to language barriers and social stigmas has led to increased numbers of unsafe pregnancies and sexually transmitted diseases. While LLMs could be helpful, they have many limitations, particularly in low-resource, non-Western settings.
Tamlyn Roman of Quantium Health in South Africa will use generative AI and Large Language Models (LLMs) to develop an automated analyst that integrates disparate health datasets and automates data analytics to support evidence-based decision-making in public health. Although there is a relative abundance of health-related data in South Africa, it is difficult to use effectively because the datasets are not standardized and analytics capacity to support policy- and decision-making is limited.
Enrica Duncan of Mapa Do Acolhimento in Brazil will use AI to improve the influx of volunteer psychologists and lawyers to their support network, which provides mental health and legal support to women at risk of gender-based violence. In 2022, one woman died every six hours from gender-based violence in Brazil. They have built a network of 10,000 volunteers who have supported over 5,000 women.
Christophe Bocquet of Dalberg Global Development Advisors (K) Ltd. in Kenya will develop VIDA PLUS, a chatbot accessible via WhatsApp that delivers public health information by live interaction to health officials, particularly in rural areas, to support their decision-making. Accessing relevant public health information is often challenging for health workers in rural areas who have limited access to technology and data literacy.
Daudi Jjingo of the Infectious Diseases Institute in Uganda will leverage generative AI to develop an interactive conversation-based platform to communicate the national guidelines for pandemic preparedness in a native African language to health workers to improve pandemic management. The national guidelines, currently available as a lengthy PDF, will be translated into a local Bantu language, Luganda, to improve accessibility to non-English speaking users, and converted into a data format for Large Language Models (LLMs) such as GPT-4.
Cally Ardington of the University of Cape Town in South Africa will develop an AI-powered voice-recognition model that performs Early Grade Reading Assessments (EGRA) in low- and middle-income countries (LMICs). Seventy percent of children in LMICs do not learn to read in any language, which severely affects their overall education and future prospects. Reading assessments, such as EGRA, test children on letter-sound knowledge, word reading, reading connected text, and answering questions on that text.