Project intends to identify highly specific, and tandem repeats in the genomes of W. bancrofti, B. malayii, B. timori, and other human filarial agents, to develop a field-deployable, diagnostically robust molecular Point-of-Care test for Lymphatic Filariasis detection.
Infectious Disease
Project intends to discover high affinity and specific ligands called suprabodies as the biomarkers of Lymphatic Filariasis.
Project intends to capture the images of blood samples for diagnosis of Lymphatic Filariasis and applying customized machine learning algorithms for quantification and classification of Lymphatic Filariasis.
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.
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.
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.
Nneka Mobisson of mDoc Healthcare in Nigeria will integrate ChatGPT-4 into their chatbot, Kem, which provides virtual self-care coaching for low-income women of reproductive age in Nigeria, to improve its accuracy and capacity to respond to queries with evidence-based information. The burden of maternal deaths in Nigeria remains inequitably high with many risks encountered even before conception, highlighting the importance of supporting self-care.
Floris Sonnemans of Degas Ghana Limited in Ghana will apply AI technology to support African smallholder farmers to implement more climate-adaptive and regenerative agricultural (RA) techniques, such as crop diversification, and scale climate action across the continent. Africa only contributes 3.8% of global greenhouse gas emissions but experiences the harshest impacts, particularly on food production. However, protective RA techniques are relatively new and challenging to adopt, and there are not enough field agents to support farmers and respond to queries.
Essa Mohamedali and Kalebu Gwalugano of the Tanzania AI Community in Tanzania will use ChatGPT-4 to develop a chatbot and support tool to help healthcare workers adhere to the Integrated Management of Child Illness (IMCI) guidelines and access updates and alternative treatment options by linking them to the latest research via their mobile phones. Access to formal training on the IMCI guidelines is limited for healthcare workers, particularly in the private sector, and its duration makes it prohibitively expensive for companies.