Awards

Ida Viktoria Kolte of Fiocruz in Brazil will adapt ChatGPT for use in the languages of indigenous populations in Brazil (Guarani-Kaiowá communities) and Mozambique (Echuwabo-speaking communities in Nicoadala) to support community health workers (CWAs) in reducing the burden of tuberculosis. Translating information on tuberculosis from Portuguese would enable its integration with the traditional medicine practiced by indigenous populations particularly vulnerable to the disease, helping overcome language and cultural barriers and a history of marginalization. They will train a translation model using existing translated texts and validated common questions and answers about tuberculosis in the literature. They will test and improve the model through assessment of its accuracy and cultural appropriateness by CHWs, traditional medicine practitioners, and community members; and they will perform a two-month pilot trial of its use by CHWs in Brazil and Mozambique.

Sandra Maria do Valle Leone de Oliveira of Fiocruz in Brazil will develop an early warning system for infectious diseases that integrates community leader engagement and a Large Language Model (LLM)-based tool for health-related event-based surveillance in two Brazilian cities on the Bioceanic Route transcontinental railway. Monitoring information from formal and informal internet sources about health-related events can lead to quicker detection and response for disease outbreaks. They will develop the system with leaders across diverse roles in the community, who will advise on the list of reportable events and priority health conditions and be trained in reporting through the system. They will create an online reporting form and a process for escalating alerts to local or national health authorities for analysis to quickly enable decisions about potential responses to disease outbreaks. Data accumulated through this system will be used to train an LLM to extract patterns and generate accurate and relevant predictions.

Renee Street of the South African Medical Research Council in South Africa and Samuel Scarpino of Northeastern University in the U.S. will perform a proof-of-concept study of AI for wastewater-based epidemiology by fine-tuning Large Language Models (LLMs) with SARS-CoV-2 metagenome sequence data to detect the emergence of viral variants. Monitoring wastewater is a useful surveillance tool that encompasses infected individuals with varying disease severity and access to healthcare facilities. They will fine-tune two LLMs to identify the emergence of viral variants using two existing SARS-CoV-2 metagenome sequence data sets: data processed with established bioinformatics to identify variants and data collected from wastewater before, during, and after the emergence of the Omicron variant. Success of the models will help validate an AI-based approach for monitoring microorganisms in wastewater to track the prevalence of specific health threats and forecast disease outbreaks, enabling targeted public health interventions.

Momy Seck of Station d'Innovation Aquacole in Senegal will apply AI approaches to remote sensing data to map spatiotemporal changes in the risk of the neglected tropical disease schistosomiasis and automatically generate public health bulletins supporting geographically-targeted control of the disease. Schistosomiasis, also called bilharzia, is caused by a parasitic worm transmitted by aquatic snails. The snails’ presence can be monitored by remote sensing by virtue of their association with a particular aquatic plant. They will use machine learning to better analyze the remote sensing data and generate schistosomiasis risk maps. They will then use Vision Language Models to extract information from these maps to be used by Large Language Models to automatically generate bulletins on risk. They will refine the bulletins through workshops with relevant public health authorities to assess their needs and the usefulness of the bulletins.

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.

Mariam Parker of the University of the Western Cape in South Africa will develop an AI-based application, MzansiMed, for real-time translation of South African languages to increase healthcare access and improve health outcomes. To train an AI model, they will build a database of dialogues encompassing a variety of healthcare scenarios, initially collecting data in at least two widely spoken languages, including isiXhosa and Zulu. This will be in collaboration with linguists and participant communities to ensure incorporation of linguistic diversity, cultural nuances, and colloquial expressions. They will take a user-centric design approach to create an intuitive user interface for interactions by speech or text, integrating ChatGPT-4 and including visual aids. They will iteratively refine the app, including testing it with patients and healthcare providers, to ensure the translation is accurate, effective, and culturally sensitive.

Rose Nakasi of Makerere University in Uganda will develop an AI-based platform to support diagnosis and management of cervical cancer in Uganda. They will collaborate with the Uganda Cancer Institute to develop a set of AI tools for automated diagnosis of cervical cancer based on microscopy of patient samples and for automated generation of the associated pathology reports. This will include categorizing pathologies, enabling identification of trends over time. The AI tools will be integrated into a web-based platform along with the capacity for Visual Question Answering to support interpretation based on medical images and diagnosis in remote areas of the country with limited access to pathologists. They will evaluate the accuracy of the cervical cancer diagnoses and the quality of reports generated through the platform as compared to those generated by expert pathologists.

Obed Brew of Kwame Nkrumah University of Science and Technology in Ghana will explore applying a combination of AI-based analytical approaches to clinical data for early detection of preeclampsia in pregnancy to reduce maternal and neonatal morbidity and mortality. They will collect a diverse set of data from pregnant women, including physiological data from wearable devices, electronic health records, clinical notes, and fetal nucleic acids from non-invasive prenatal testing. They will apply a variety of AI tools to detect patterns across the data, such as associations between fetal gene signatures and maternal physiological markers. While evaluating the performance of the AI models in detecting preeclampsia, they will develop training programs for healthcare professionals in the use of AI tools in clinical settings.

Jesse Gitaka of Mount Kenya University in Kenya will develop the GPT-4 AI model to support healthcare providers with up-to-date medical information for improved clinical decision-making and neonatal care. They will identify knowledge gaps among providers, using this information to guide training for a cohort on using GPT-4 for clinical support. They will then evaluate their use of GPT-4 for its impact on clinical decisions and neonatal outcomes. A subset of the healthcare provider cohort will be engaged to identify the barriers, risks, and opportunities associated with use of the AI tool. Results from both these evaluations will be used to develop a scalable framework for the deployment of AI in similar healthcare contexts to reduce neonatal morbidity and mortality, especially where there is a shortage of medical personnel.

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.