Awards

Francis Kombe of EthiXPERT NPC in South Africa will develop an AI-based platform to support African research ethics committees and clinical trial decision-making. It will build on their cloud-based, online review system RHInnO Ethics. This system is currently used to manage the entire ethics review cycle, including protocol submission and review, approval, and follow-up, with the goal of shortening the review timeline, enhancing review quality, and speeding the discovery of life-saving public health interventions. They will consult with relevant stakeholders to identify elements of ethics review that could benefit from AI. They will then identify the required structured and unstructured data, use this data to train a model based on GPT-4, and integrate the model into their existing review system. They will evaluate the new platform, comparing it with and without the AI element and assessing results from current users, including decision quality and timeliness.

Paul Macharia of the University of Nairobi in Kenya will develop an LLM-based platform to give healthcare providers real-time access to comprehensive, up-to-date, adolescent HIV information for enhanced decision-making and better patient health outcomes. To guide the project, they will establish a community advisory board, including HIV-positive adolescents, healthcare providers, and community leaders. They will interview providers to identify their current sources of this information and their unmet needs. They will then create a dataset relevant for adolescent HIV care, including medical literature, clinical guidelines, and research findings; use it to train an LLM; and develop a natural language interface for healthcare providers to interact with the LLM. They will pilot test the platform in different healthcare settings, collecting data on its impact on provider knowledge and practice.

Philippe Manyacka Ma Nyemb of Gaston Berger University in Senegal will develop AI-based approaches to better monitor and manage cardiovascular diseases and understand their risk factors. They will perform a household-level study in the Saint-Louis Region of Senegal with monthly data collection, including medical examinations, behavioral surveys, and physical environment assessments. The monthly monitoring data will be analyzed by AI-based approaches, yielding cardiovascular disease risk scores for household members. They will use the study data and risk scores to train a Large Language Model with a chatbot interface available to healthcare professionals and the public. The chatbot and data collection process will serve as an integrated platform to reduce the burden of cardiovascular diseases. It will increase awareness of the disease and its risk factors for the public, and it will help increase adoption and effective use of digital tools more broadly to improve health.

Jules Brice Tchatchueng Mbougua of Centre Pasteur du Cameroun in Cameroon will develop a chatbot to provide health information in the Fulfulde language, which is commonly spoken in West Africa, to increase equitable access to healthcare. To overcome language barriers as well as variable levels of literacy, the chatbot will interact with users by speech or text and with bidirectional translation between Fulfulde, French, and English. To enable this, they will compile an extensive Fulfulde dataset covering health-related expressions and terminology. They will develop health information content that is culturally relevant by co-creating it with the communities the tool is meant to serve, and it will be focused on primary healthcare. The chatbot will include a way for users to provide feedback to ensure it is delivering information most relevant to the evolving needs of Fulfulde-speaking communities.

Mandicou Ba of Université Cheikh Anta Diop in Senegal will develop an AI-based tool for automatic, cost-effective, and accessible early diagnosis of the eye disorder strabismus and for guiding surgical correction. Strabismus is eye misalignment, the two eyes pointing in different directions, and the associated impaired vision can become permanent at a young age if uncorrected. They will collect a clinical dataset of facial images of strabismus patients in Senegal, annotated by experts. After identifying a suitable AI-based method, they will apply it to the dataset to create an AI model for diagnosis and accurate calculation of angular deviation between the two eyes for use during surgical repair. They will use the model to develop an automated system as a web-based tool and also as a smartphone app, making it accessible even in rural areas for early diagnosis in children.

Mamadou Bousso of Iba Der Thiam University in Senegal will develop methods for AI-based analysis of ultrasound images for cost-effective early detection of liver fibrosis caused by hepatitis B viral infection. They will improve the performance of an existing method by acquiring ultrasound data that more comprehensively encompasses the clinically-recognized stages of liver fibrosis. The expanded dataset will be used to train an AI model well-suited to capture complex patterns in imaging data. They will also establish support for healthcare professionals that facilitates the adoption and effective use of the application, including training courses, web-based and mobile phone-based tools with user-friendly interfaces, and ongoing technical support. The application would enable more screening in underserved areas, with increased early detection and awareness of liver fibrosis decreasing mortality from the disease as well as healthcare costs.

Mame Marème Fall of Kajou Senegal in Senegal will develop a platform to increase access to accurate health information, including information on available healthcare services, to improve the quality of life for rural populations in Senegal. They will use a Large Language Model to create a database of health information with a chatbot interface enabling questions and answers by either speech or text, including spoken questions in either French or Wolof. The database will be accessible online via internet technology that accommodates connections of short duration and low bandwidth, and it will be available offline as content stored on mobile phone microSD cards. They will evaluate the quality of answers to health questions through the system by engaging experts and by surveying users through pilot distribution of 1,000 microSD cards with the database.

Yap Boum II of Institute Pasteur of Bangui will develop a digital platform, called The Village, that strengthens the scientific research capacity across the Pasteur Network and beyond through conversational chatbots that forge productive links between those seeking and offering resources, ideas, and collaboration. They will identify suitable Large language Models and create a chatbot that collects unstructured data through conversations with scientists to generate user profiles with higher potential for productive matching across the research community. They will test and continually refine the platform through in-person and virtual meetings across the scientific community. As a platform making connections between scientists regardless of their location, resources, and research capacity, The Village will increase equity in global health research.

David Kamau of Mary Help of the Sick Mission Hospital in Kenya will integrate ChatGPT and a medical Large Language Model with the existing health management information systems in Kiambu County, Kenya, to provide clinical decision support for noncommunicable diseases. This integration will support healthcare providers in diagnosing diseases accurately and quickly, reducing misdiagnoses and improving patient outcomes, and it will support optimizing treatment plans, reducing unnecessary procedures and healthcare costs. They will evaluate the performance of the integrated AI tools, assess the usability of the system through surveys, and capture demographic data for patients receiving AI-assisted care. They will also provide training for healthcare professionals on effectively using AI tools to enhance patient care.

Steven Wanyee of IntelliSOFT Consulting Limited in Kenya will develop an AI-based framework for analysis of cervical cancer registry data to identify epidemiological trends and improve surveillance and prevention efforts. The analysis will incorporate variables such as demographic factors, geographic locations, screening history, HPV vaccination rates, and treatment outcomes. They will use natural language processing to extract and analyze unstructured data. Machine learning algorithms will be used to identify patterns and trends in cervical cancer incidence rates, stage at diagnosis, treatment outcomes, and survival rates. They will develop predictive models to forecast cervical cancer burdens, estimate the potential impact of interventions for prevention, and guide resource allocation and targeted prevention strategies. They will also create user-friendly interfaces and visualizations to enable policymakers, public health professionals, and researchers to easily interpret the modeled data and use it effectively.