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.

David Kamugundu of eFiche Limited in Rwanda will develop an AI-based platform to support nurses in Rwanda in accurate and efficient diagnosis and patient treatment. The platform will be integrated into the already-operational, web-based electronic medical records system eFiche. It will serve as a virtual assistant for nurses, including providing diagnostic support, suggesting treatment plans based on the latest medical guidelines and assessing adherence, and identifying potential adverse events. They will train a Large Language Model with historical health data to help predict diagnostic outcomes, laboratory orders, and subsequent procedures and prescriptions, and they will develop a chatbot to provide information and recommendations to nurses in a conversational manner. They will evaluate the platform based on the accuracy of the model, satisfaction of users, and the impact on healthcare delivery, including diagnostic accuracy, treatment efficacy, and adherence to health guidelines.

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.

Mihaja Raberahona of Equipe de Recherche Clinique en Maladies Infectieuses in Madagascar will develop an AI-based triage tool to identify patients likely to have pulmonary TB based on combining physiological data from wearable devices, cough acoustics, and anthropometric data. They will create an AI model that can find patterns in the combined data to help staff with minimal medical training quickly prioritize patients needing confirmatory testing. From a cohort of patients with symptoms suggestive of TB from primary healthcare centers in Madagascar, they will collect physiological and anthropometric data and perform a digital chest X-ray, cough sound analysis, and a GeneXpert molecular assay for the presence of Mycobacterium tuberculosis. They will evaluate performance of the AI-based triage tool primarily as compared to the microbiological assay, but also to other current screening methods, including chest X-rays analyzed by clinicians with or without AI support.

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.

Fred Kaggwa of Mbarara University of Science and Technology in Uganda will develop an LLM for answering questions related to prenatal and postpartum care in Uganda in three languages: Swahili, Runyankore-Rukiga, and Luganda. This will also serve to create a scalable, open-source pipeline for developing AI models incorporating underrepresented languages more broadly. They will identify the most suitable LLM and train it with existing databases and with new, high-quality medical text, including that extracted from textbooks and patient education materials as well as new question-and-answer pairs written by local clinicians. They will iteratively evaluate and improve the LLM, working directly with community health workers and expectant mothers to ensure the model’s responses are safe, accurate, relevant, and accessible.

Dennis Maorwe of DPE Company Limited in Kenya will develop a Large Language Model (LLM) to support the planning and execution of behavior change interventions to improve health outcomes for Kenyans living with SCD. Guided by insights from SCD patients and medical specialists, they will train an LLM with SCD management practices and additional open data relevant to designing interventions for social behavior change for SCD. The LLM will be used to generate tailored health messages to support SCD patients through the different disease management stages and to empower them to take an active role in their health. This will help reduce the stigma around the disease and improve their quality of life.

Brian Turyabagye of MamaOpe Medicals in Uganda will develop an AI-based platform integrated with the MamaOpe screening tool to enhance the diagnosis and management of respiratory illnesses. MamaOpe is a pneumonia screening tool based on vital signs, including respiratory rate and lung sounds, with results presented via a mobile app into which healthcare workers can enter patient symptoms and store patient histories. They will train a Large Language Model with the MamaOpe database to help guide differential diagnosis by highlighting critical indicators and suggesting supplementary tests for more accurate diagnosis. In settings with limited direct access to specialists, the platform can facilitate remote consultations. They will perform an efficacy study to evaluate how well the platform correctly predicts clinical outcomes.

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.

Kevin Korir of Visortech Solutions in Kenya in partnership with Yemaya Health Advisory will develop an LLM to map the process of health policy creation and approval, serving as a tool for quicker translation of new evidence into policies. They will pilot test the tool by using it to develop a policy framework for strengthening national HIV prevention management systems. They will develop the LLM as support for policy simulation and scenario planning to inform the policy approval process, for gap analysis to optimize current policies, and for identification of conflicts across related policies and their implementation. The LLM will facilitate generating written documents useful for policymakers, including briefs for new policies and summaries of proposed changes to existing ones.

Prince Adjei of Kwame Nkrumah University of Science and Technology in Ghana will develop an AI-based tool to support clinicians and patients in managing complications and comorbidities in pregnancies, focusing on pre-gestational and gestational diabetes. They will train a Large Language Model with relevant data and health guidelines from Ghana, including a glossary of medical and nutritional terms in the local language Twi. They will also design a user-friendly interface for interactions in English and Twi, with the patient interface incorporated into WhatsApp. The resulting tool, RAID-MaP-Gh, will provide practical guidance to clinicians, ranging from specialists to midwives and traditional birth attendants, and guidance to patients that takes into consideration the socioeconomic and cultural contexts most relevant to Ghana. The tool will help close health information gaps associated with gestational diabetes, reducing the number of undiagnosed cases and improving maternal and infant health outcomes.

Polly Okello of Medbook Kenya Limited in Kenya will develop a set of LLMs to support frontline healthcare workers in rural areas and among marginalized populations in Kenya. The LLMs will be based on the clinical LLM Med42 and trained with information representative of the Kenyan healthcare system, including clinical data and guidelines. The LLMs will be used to provide information to clinicians about medical conditions, treatments, and medications; summaries of patient records and other medical documents; personalized treatment plans for patients; and educational materials for patients and healthcare workers. They will deploy the LLMs and evaluate their impact through surveys with healthcare workers and patients and through case studies. They will assess the knowledge and skills of frontline healthcare workers, quality of patient care, efficiency of healthcare delivery, and patient satisfaction, with the goal of making healthcare more efficient, effective, and accessible in Kenya.

Andrew Maranhão Ventura Dadario of Hospital Israelita Albert Einstein in Brazil will test and evaluate LLMs for their ability to structure and anonymize clinical notes. Such structured notes would promote data interoperability, which would improve patient care, research, and health policies for the diversity of Brazilian public health patients; and establishing standardized evaluation of LLMs for public health would promote their future applications. They will prepare a representative dataset by extracting, processing, and annotating clinical notes, and then test and compare a set of existing LLMs in extracting information from text, including their ability to preserve patient privacy, their applicability to the Portuguese language, and their suitability to primary healthcare in Brazil encompassing equitable performance across patients stratified by gender, race, ethnicity, and age. The best performing LLM will be used to transform free text from clinical notes, and the structured product will be assessed by health managers for accuracy and usefulness.

Madji Sock of Haskè Ventures in Senegal will develop an AI-based platform for patient intake in Senegalese hospitals that will streamline intake and monitor disease trends to improve healthcare delivery and outcomes. The platform will use a medical Large Language Model (LLM) to help hospital reception staff make initial assessments of patients for quick and accurate triage. The LLM will be adapted for local dialects, medical practices, and patient demographics, and it will interact with patients by voice and text display. Daily automated analysis of patient data from multiple hospitals will identify disease patterns and trends, enabling quicker response to disease outbreaks. They will pilot test the platform in selected hospitals and health centers, including training for staff and continuous evaluation and improvement of the platform.

Hugo Fernandes of Universidade Federal de São Paulo in Brazil will create a university center focused on using AI to support primary healthcare professionals in surveillance and clinical decision making for complex cases of interpersonal violence in vulnerable populations in Brazil. AI systems could help in diagnosis and classification, risk determination, recommendation of interventions, referrals to relevant care providers, socio-emotional support plans, support for perpetrators in avoiding repeated offenses, mandatory notifications, as well as generalization across contexts for improved understanding of patterns of violence. They will aggregate information technology at a campus site, train a Large Language Model (LLM) with appropriate data and create a prototype tool, and gather relevant experts to judge the quality of the tool for its potential contributions to Brazil’s health system in targeting interpersonal violence.

Cecília Claudia Costa Ribeiro of Universidade Federal do Maranhão in Brazil will develop an LLM to help frontline healthcare workers identify risks and take action to prevent NCDs in the first 1000 days of life. There are connections between NCDs in early childhood that are affected by social inequities as well as pregnancy-associated factors. They will apply machine learning algorithms to data for women during and shortly after pregnancy to build risk prediction models of NCDs for children in the BRISA Cohort of São Luís, with predictor variables encompassing socioeconomic, behavioral, and biological stressors. The estimated risks will be input to an LLM that incorporates the best available scientific evidence on each NCD. This LLM, when given data from women patients, can then determine the risk of NCDs for the patient’s children, along with an explanation to help CHWs effectively communicate these risks and make evidence-based recommendations in real-time for prevention of NCDs.

Elisdete Maria Santos de Jesus of Universidade Estadual de Campinas in Brazil will train an LLM to read Brazilian medicine package leaflets and generate accurate, up-to-date summaries of how to use the medicine that is easier to understand for patients who take the medicine and for healthcare professionals who prescribe, dispense, and administer it. They will do a literature review, including articles in English and Portuguese, to better understand the types of information in leaflets and how understandable it is to users of the medicine. Subsequently, medicine leaflet information will be collected, processed, and used to train an LLM to create summaries in plain language, after which the summaries will be evaluated for their quality. The trained LLM could be used to help develop additional health education tools.

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.

Zilma Silveira Nogueira Reis of Universidade Federal de Minas Gerais in Brazil will develop a Large Language Model (LLM) that can generate personalized instructions for taking medications that are tailored to a user’s level of literacy, cultural context, and special needs to increase equitable access to medications and promote safe and effective patient self-care. They will determine the contexts that medicines are prescribed in the Portuguese-speaking countries Brazil, Mozambique, and Portugal; and they will train an LLM with a Brazilian public corpus of medical leaflets, social media text, and other relevant documents. A team of prescribing professionals will evaluate the LLM product for use as a web-based platform, including its ability to use an individual patient profile to generate personalized text instructions with supporting pictograms, such as those indicating relevant body parts, dose, unit of measurement, and mechanism of administration.

João Paulo Papa of Sao Paolo State University in Brazil will develop a platform in which Large Language Models (LLMs) will extract information from published research articles and generate one-page fact sheets highlighting the potential implications for public health policy in an accessible form for researchers and health system managers. They will integrate into the LLM training a methodology for knowledge translation that has been used to train health system researchers to communicate results in a form they can most readily be used.

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.

Shirley da Silva Jacinto de Oliveira Cruz of Universidade Federal de Pernambuco in Brazil will develop an LLM-based chatbot and use it in a pilot study to support post-discharge follow-up communication with surgical wound patients to promote a faster and safer recovery. A chatbot would provide a personalized and accessible mechanism for overcoming geographical, socioeconomic, and communication barriers between the healthcare team and the patient, including care reminders and regular check-ins via text and voice to acquire data on the patient's recovery and alert healthcare professionals to signs of complications. They will survey the needs at the Hospital das Clínicas de Pernambuco pilot site, train an LLM using electronic patient records from the hospital, and perform a series of evaluations using feedback from surgery patients and hospital staff to assess the usefulness and relevance of the information the chatbot provides.

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.

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.

Nicolas Poussielgue of Dakar Institute of Technology in Senegal will expand the Weerwi platform by incorporating a Large Language Model (LLM) into the existing chatbot to enable more personalized advice and information on menstrual health for young women. The LLM will enable interactions in a conversational style to better engage users. It will also incorporate conversations across engagements with individual users to better identify their priorities and gaps in knowledge and give personalized recommendations for where to find additional information. The enhanced chatbot will be designed in consultation with users, and like the existing version, it will be available through a website and mobile app.

Ziyaad Dangor of the Vaccines and Infectious Diseases Analytics Research Unit of Wits Health Consortium (Pty) Limited and Jenny Coetzee with Minja Milovanovic of African Potential Management Consultancy (Pty) Ltd, together with Andreas Cambitsis and Emma Gibson of Business Science Corporation Global and Mirjam van Reisen and Samson Yohannes of Virus Outbreak Data Network (Vodan)-Africa, all in South Africa, will develop an LLM supporting clinicians’ note taking to improve patient care. The LLM will be integrated into 1beat, a South African-developed electronic medical record system. They will fine-tune the LLM as an AI assistant during patient intake and discharge to improve quality as well as consistency in digital patient records, and they will ensure the clinical data is Findable, Accessible, Interoperable, and Reusable (FAIR). This will improve patient care as well as health research. They will also survey relevant stakeholders to develop a business model to support the further development and sustainability of the 1beat system.

Neil Martinson of Perinatal HIV Research Unit of Wits Health Consortium (Pty) Limited in South Africa will apply AI-based approaches to support increased screening for high-risk HPV and its treatment to reduce the morbidity and mortality associated with cervical cancer. The scale-up of cervical HPV screening will lead to more cervical biopsies as part of follow-up care and treatment where indicated. Automated approaches for biopsy analysis would overcome the limited number of specialist pathologists otherwise required. They will train an LLM with pathology reports of cervical biopsies to extract critical wording, and they will apply computer vision for analysis of the matching digital images from each patient’s pathology slide. They will then develop a machine learning algorithm for automated reporting that accurately separates normal images from those with pathology, categorizes the pathology, and determines if the pathology extends to the biopsy margins, which indicates it is likely to recur.

Jennifer Chipps of the University of the Western Cape and Damian Clark of the University of KwaZulu-Natal in South Africa will apply machine learning to a hospital digital registry of trauma and surgical patients to develop an algorithm for predicting unplanned and avoidable readmissions to improve patient outcomes and reduce the burden on the healthcare system. Unplanned hospital readmission within 30 days is an important indicator of the quality of patient care. They will use de-identified patient data from a South African public hospital to train an AI model to predict readmissions avoidable under the current standard of care. The resulting algorithm will be validated using real-time patient data, integrated into the hospital workflow as a Readmission Prediction Classifier Tool, and tested in different clinical settings. The tool will enable treatment plans tailored to individual patients and their risks, improving health outcomes and reducing financial burdens for patients and healthcare providers.

Kahesh Dhuness and Charita Bhikha of the Council for Scientific and Industrial Research in South Africa will use statistical analysis and machine learning techniques on existing data to increase the effectiveness, as a triage tool, of Umbiflow, a Doppler ultrasound device used for antenatal screening. Umbiflow monitors umbilical artery blood flow to help determine the risk of impaired fetal growth. Statistical analysis of Umbiflow data will identify patterns of fetal risk across clinics and hospitals to help guide effective resource allocation. Application of machine learning to Umbiflow data from clinical trials will enable identification of complex patterns to improve determination of fetal risk and subsequent triage. They will also integrate a Large Language Model to support healthcare providers in evidence-based decision making using Umbiflow data.

Rahel Mekonnen with Hiwot Mekonnen of the Ethiopian Artificial Intelligence Institute in Ethiopia will develop a multilingual speech-to-speech translation system to remove language barriers in communication between healthcare providers and their patients. This system would use three of the local languages: Amharic, Afan Oromo, and Somali, making healthcare less dependent on the availability of translators, which is a challenge due to the multiple languages spoken in Ethiopia, and enabling direct and open communication with patients without the need for an intermediary. They will customize an existing LLM by training it with medical text data and transcribed audio data from anonymized doctor-patient conversations, including text-to-speech model training using text read by language experts. They will create both a mobile phone-based translation application for patients and a web-based application for physicians.

Martha Yifiru with Dinksira Bekele of Addis Ababa University in Ethiopia will develop a smartphone-based conversational agent in the Amharic language to provide information and answer questions for women on pregnancy, childbirth, and their children’s health. This could reduce maternal and neonatal mortality by providing advice on best practices, including sensitive discussions in private on personal health; detecting early warning signs to enable prevention of health complications for mother and child; and ensuring timely visits to healthcare facilities while avoiding unnecessary visits. They will identify a suitable medical LLM, adapt it for use in Amharic with translations of relevant manuals and other documents, develop the agent with its interfaces between the LLM and user, and validate that the agent’s healthcare advice is as expected, with the possibility of expanding the number of users by including additional Ethiopian languages and creating a speech interface.

Lynn Hendricks with Simone Titus and Karolien Perold-Bull of Stellenbosch University and Annibale Cois of the South African Medical Research Council, all in South Africa, will use multiple AI applications to develop a culturally sensitive, visual storytelling intervention for patients that supports their communication with healthcare providers and enhances their knowledge and management of their health status and care regimen. With patients and healthcare providers, they will collect high-quality evidence-based data, representative of the African context and related to symptoms, diagnosis, patient experiences, treatment plans, and disease information. This data will be used with a set of AI tools to generate a repository of text stories with associated images and short videos. They will then recruit a cohort of patients with a new diagnosis of a non-communicable or infectious disease and test whether giving them personalized relevant stories in various formats increases their understanding of their diagnosis and treatment plan.

Mbaye Faye of Tech Care For All – Africa in Senegal will develop Large Language Models to support continuing education for healthcare professionals and their mastering of critical guidelines and procedures for patient care. The Medical Learning Hub and its mobile app already supports hospitals and healthcare training organizations as a tool to track training attendance, host content, distribute certificates, and conduct assessments. They will integrate ChatGPT to transform static patient-care guidelines into dynamic, interactive learning modules, enabling real-time updates as guidelines evolve and more effective e-learning. They will pilot the approach through the Continuous Medical Education processes of four large hospitals in Senegal. Their platform will serve healthcare organizations in overseeing staff competencies and compliance with standards, and it will foster the professional development of healthcare practitioners for enhanced patient care.

Anne Njeri Kamere and Mamothe Makgabo of The Aurum Institute in South Africa will develop an AI-based tool for clinicians managing complex HIV cases to support them in accurately and quickly making clinical decisions, thereby improving patient health outcomes. The tool will support clinicians in determining multiple elements: the appropriate treatment regimen and management approach for opportunistic infections, the need for preventive measures, potential referrals to higher-level facilities, and the initiation of the correct antiretroviral therapy regimen. They will train a Large Language Model and integrate ChatGPT-4 so the tool can synthesize information on a case, based on manually-entered patient details, and provide interactive guidance to clinicians based on current clinical guidelines, medication lists, and research. They will engage healthcare providers to iteratively evaluate and refine the tool in different healthcare scenarios, including integration with TB prevention and treatment.

Scott Mahoney and Lara Fairall of The Health Foundation of South Africa in South Africa will enhance the AI-Supported Healthcare Assistant (AISHA) prototype, which summarizes clinical recommendations from diverse evidence sources, as a public health tool supporting clinicians, policymakers, and health educators. They will incorporate a Large Language Model to track and analyze updates in healthcare guidelines, including identifying inconsistencies between them at the global, regional, and local levels. They will also expand the database of guidelines to comprehensively cover prevalent health conditions, starting with those managed in a primary care setting in South Africa. For ongoing improvement of the AISHA tool, they will create programs bringing together local stakeholders with AI and healthcare experts from the global community. Better synthesis of healthcare evidence through AISHA will improve and expedite public health policy decision-making.

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.

Candice Chetty-Makkan and Sophie Pascoe of the Health Economics and Epidemiology Research Office of Wits Health Consortium (Pty) Limited in South Africa will develop an AI-based mobile phone application for use after HIV self-testing to increase access to counseling and advice on seeking healthcare services. They will build on an existing Large Language Model-based chat interface named Your Choice, which stands for “Your Own Unique Risk Calculation for HIV-related Outcomes and Infections using a Chat Engine.” The new app will have improved conversational abilities and generate conversation summaries for patients and healthcare providers. They will pilot test the app using a hypothetical HIV test result, engaging a subset of individuals and healthcare providers previously recruited for their insights on health-seeking behavior. The app will be integrated into the HealthPulse AI mobile app platform, providing a more accessible alternative to facility-based HIV testing and the associated guidance on next steps.

Orapan Sripichai of the National Institute of Health of Thailand in Thailand will engage a national network of laboratories for the genomic surveillance of Salmonella, involving sequencing clinical isolates to characterize strains, virulence factors and mechanisms of antimicrobial resistance. Salmonella infection is prevalent in Thailand and can be life-threatening. The emergence of multidrug-resistant Salmonella strains in Southeast Asia is an additional major concern. They will collect approximately 1,500 clinical isolates from 77 provincial hospitals across Thailand over one year, and train local laboratory scientists and bioinformaticians to produce and analyze genomics data. The data will be uploaded to a standard repository in the National Center for Biotechnology Information (NCBI) and will help to guide prevention and control measures.

Rifky Waluyajati Rachman of the West Java Provincial Health Laboratory in Indonesia will employ targeted next-generation sequencing (NGS) to support genomic surveillance of drug-resistant tuberculosis (TB) in Indonesia. Indonesia has the second highest number of TB cases globally and a growing burden of largely undetected multidrug-resistant TB, yet no drug resistance surveillance in place. They will perform targeted NGS on over 5,000 positive sputum samples to more accurately estimate drug-resistant TB prevalence. They will also conduct whole genome sequencing at the community level to understand transmission patterns and help guide public health interventions. To build capacity, they will provide tailored training on the experimental, bioinformatic, public health, and epidemiological aspects of infectious disease surveillance. They will also establish a public center of expertise for pathogen surveillance in West Java, which has a population of 48 million.

Jacqueline Weyer of the National Institute for Communicable Diseases in South Africa and Jinal Bhiman of Wits Health Consortium (Pty) Ltd also in South Africa will leverage a rapid monoclonal antibody (mAb) isolation and screening pipeline to develop diagnostics that differentiate between pathogens to support epidemic responses. Africa’s burden of many zoonoses and vector-borne diseases (VBD), such as Lassa fever and yellow fever, remains largely unknown, mainly due to diagnostic costs and limited access to reagents. They will leverage an existing screening pipeline, with infrastructure established by the Global Immunology and Immune Sequencing for Epidemic Response - South Africa (GIISER-SA) project, using a mouse model as a more readily available source of pathogen-specific B cells to identify mAbs that detect three ebolavirus species. These mAbs will be tested for sensitivity and specificity using patient samples and can be used to develop immunoassays, including rapid lateral flow assays, which are important for rapid, field-based diagnosis.

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. However, real-time data shortages prevent interventions, such as balanced energy-protein supplements, from reaching the highest-risk groups. Using Bayesian Hierarchical Spatial modeling, they will develop geospatial models for countries vulnerable to conflict and climate change, such as Ethiopia and Yemen. By incorporating socio-demographic and economic indicators, and climate-related and conflict-related shocks from national databases, they can estimate risks based on exposure and predict outcomes, such as undernutrition and anemia.

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. They have exploited a state-of-the-art 3D-printing process to rapidly engineer IVRs in a cost-effective, single-step process enabling the controlled release of multiple drugs for HIV prevention and contraception. They will recruit around 16 women, aged 18–45 from Kampala Health Centre, and use focus groups to evaluate their views on the proposed 90-day timeframe of use for four different IVR prototypes to guide the final design.

Aida Sadikh Badiane of the Universite Cheikh Anta Diop de Dakar in Senegal will use a metabolomics platform to identify cervicovaginal metabolites and inflammatory mediators associated with high-risk human papillomavirus (HPV) infection, which cause the majority of cervical cancer cases, in Senegalese women. Cervical cancer is the leading cause of cancer deaths in women in sub-Saharan Africa. Metabolic and immune markers could enable more effective diagnoses for these diseases than the current methods used in low-resource settings. They will perform a prospective, cross-sectional study on a cohort of 385 women using an untargeted metabolomics platform to identify molecules within the cervicovaginal microenvironment that are predictive of infection and cancer risk. They will also use Luminex assays to evaluate inflammatory molecules and other markers associated with infection, and sequence the L1-HPV gene in the samples to better track the genotypes in Senegal.

Pragya Yadav of the Indian Council of Medical Research - National Institute of Virology in India will strengthen genomic and epidemiological surveillance in different locations across India to enhance preparedness against high-risk viral diseases. With India's extreme geo-climatic diversity, it is under constant threat of emerging and reemerging viral infections. They will enhance surveillance of endemic diseases in India, including Zika and Dengue, by establishing a network of seven laboratories and training staff in molecular diagnostic techniques, including sequencing, data analysis, and biosafety. They will also select surveillance sites for collecting samples and expand next-generation sequencing capacity to identify variants.

Simon Kariuki of the Kenya Medical Research Institute in Kenya will use an antibody platform to characterize children's immune responses to the new malaria vaccine to determine the impact of any accompanying infections. The WHO recently approved a new malaria vaccine that will mainly be deployed in sub-Saharan Africa. During its development, HIV-infected children were found to mount weaker immune responses. Helminth infections, which are prevalent in sub-Saharan Africa, are also suspected to negatively impact vaccine efficacy. To test this, they will use an antibody-dynamics platform to assess the impact of helminths and other current or prior parasitic, bacterial, and viral infections on humoral and cellular immune responses following the 4th dose of the new malaria vaccine in two- to three-year-old children at six hospitals in western Kenya. This will help design more effective deployment strategies such as deworming before vaccination.

Senjuti Saha of the Child Health Research Foundation in Bangladesh will use a single-cell analytics platform to track the immune responses of babies before and after receiving a pneumococcal conjugate vaccine to determine the impact of various factors, including nutritional status and seasonality, on vaccine efficacy. Vaccines have successfully reduced childhood morbidity and mortality; however, their efficacy can be influenced by host factors and extrinsic factors through unknown cellular mechanisms. They will recruit 50 newborns in a rural district north of Dhaka and collect blood and nasopharyngeal swabs before, during and after a routine vaccination series. They will extract peripheral blood mononuclear cells and use them to perform single-cell RNA sequencing to identify cell subtypes and link differential vaccine responses to factors including gestational age, nutritional status and sex.

Yasir Shafiq of Aga Khan University in Pakistan and Anne Lee of Brigham and Women's Hospital in the U.S. 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. However, real-time data shortages prevent interventions, such as balanced energy-protein supplements, from reaching the highest-risk groups. Using Bayesian Hierarchical Spatial modeling, they will develop geospatial models for countries vulnerable to conflict and climate change, such as Ethiopia and Yemen. By incorporating socio-demographic and economic indicators, and climate-related and conflict-related shocks from national databases, they can estimate risks based on exposure and predict outcomes, such as undernutrition and anemia.

Raghavan Varadarajan in collaboration with Sudha Kumari, both of the Indian Institute of Science in India and Nico Callewaert of the VIB-UGent Center for Medical Biotechnology in Belgium will modify the microorganism, Pichia pastoris, used to produce lower-cost vaccines in low-resource settings, to generate more effective vaccines. Many vaccines are composed of pathogen-derived proteins that require production inside other cells. Although P. pastoris can produce these antigens at a lower cost than mammalian or insect cells, the viral proteins it produced for the SARS-CoV-2 vaccine were hyperglycosylated and poorly immunogenic, unlike those produced in mammalian cells. They will express different antigen forms in mammalian cells, and in different Pichia hosts, to determine whether altering glycosylation and protein size affects immunogenicity. They will also glycoengineer Pichia hosts to determine whether they can produce more effective vaccines. Ultimately, this approach could improve vaccine production for COVID-19 and other viruses.

Jinal Bhiman of Wits Health Consortium (Pty) Limited in South Africa and Jacqueline Weyer of the National Institute for Communicable Diseases also in South Africa will leverage a rapid monoclonal antibody (mAb) isolation and screening pipeline to develop diagnostics that differentiate between pathogens to support epidemic responses. Africa's burden of many zoonoses and vector-borne diseases (VBD), such as Lassa fever and yellow fever, remains largely unknown, mainly due to diagnostic costs and limited access to reagents. They will leverage an existing screening pipeline, with infrastructure established by the Global Immunology and Immune Sequencing for Epidemic Response - South Africa (GIISER-SA) project, using a mouse model as a more readily available source of pathogen-specific B cells to identify mAbs that detect three ebolavirus species. These mAbs will be tested for sensitivity and specificity using patient samples and can be used to develop immunoassays, including rapid lateral flow assays, which are important for rapid, field-based diagnosis.

Simon Kariuki of the Liverpool School of Tropical Medicine, Kenya in Kenya and Holden Maecker of Stanford University in the U.S. will determine whether probiotics and synbiotics can boost infant immune responses to vaccines. Diarrhea is the second leading cause of death in young children, with rotavirus a leading culprit. Oral rotavirus vaccines are routinely administered in low- and middle-income countries (LMIC) but are only 50% effective compared to 85–98% effectivity in high-income countries. One major cause could be environmental enteric dysfunction (EED), which is pervasive in children in LMIC. Their clinical trial of 600 newborns from western Kenya indicated that administering weekly probiotics and synbiotics (Lactobacilli and Bifidobacteria) up to age six months improved gut health and prevented EED-associated inflammation. They will use stored plasma samples and vaccination records to determine the impact of EED and systemic inflammation, as well as pro- and synbiotic effects on rotavirus vaccine efficacy.

Abdoulaye Djimde of the University of Sciences, Techniques, and Technologies of Bamako in Mali will use a metabolomics platform to identify biomarkers to detect dormant Plasmodia hypnozoites in a previously malaria-infected individual as a diagnostic method and to screen for new therapeutics. Malaria remains one of the deadliest parasitic diseases in the world, with 95% of deaths occurring in sub-Saharan Africa. Most research focuses on the most prevalent causative parasite, Plasmodium falciparum, but other strains, including P. vivax and P. ovale, are likely to become more dominant. These strains uniquely produce hypnozoites, which can lay dormant for years in the liver where they are undetectable and resistant to treatment. They will generate hypnozoite-containing liver cells in vitro and subject them to metabolomics analysis to identify hypnozoite-associated biomarkers. Candidate biomarkers will then be validated in serum samples from thirty infected individuals.

Georgia Tomaras and Nathanial Chapman of Duke University and Girija Goyal and Don Ingber of the Wyss Institute at Harvard University, both in the U.S., will test whether Organ-on-a-Chip technology can inform how antibodies protect humans from pathogen infections to design more effective vaccines. Identifying protective vaccine features and validating them in human clinical trials is time-consuming and costly. An alternative is to use primary human organ chips that reproduce human physiology in vitro. They will stimulate peripheral blood mononuclear cells on the human lymph-node-on-a-chip with existing COVID vaccines and extensively characterize the resultant antibodies, including evaluating epitope specificity, and isotype and glycan profiling. They will also assess the capacity of these antibodies to prevent or reduce SARS-CoV-2 infection using the lung-on-a-chip technology. This approach can ultimately be applied to other pathogens, such as those causing malaria.

Audrey Dubot-Pérès of the Lao-Oxford-Mahosot Hospital-Wellcome Trust Research Unit (LOMWRU) in Lao PDR will establish a pilot respiratory syncytial virus (RSV) genomic surveillance system to determine disease burden and monitor strain circulation in Lao PDR. RSV is the leading cause of viral pneumonia in young children in low-income countries. Accurate data on disease burden, transmission and viral evolution are critical to successfully introduce emerging vaccines and therapies. Leveraging their experience as a national center for SARS-CoV-2 genomic surveillance, they will develop an RSV genomic sequencing protocol using samples collected from children at two central and four provincial hospitals. They will also investigate whether RSV RNA can be purified directly from rapid diagnostic tests to improve surveillance in remote areas. The data will be displayed on a national health dashboard. If successful, their approach could be expanded into a national surveillance system.

This grant is one of three grants that are funded and administered by the Programme for Research in Epidemic Preparedness and Response (PREPARE) in Singapore.

Chhorvann Chhea of the National Institute of Public Health in Cambodia will expand Cambodia’s Severe Acute Respiratory Infections (SARI) surveillance network by integrating metagenomic next-generation sequencing to better diagnose and monitor severe respiratory infections. Pneumonia is the leading cause of death globally in children under five years old, with the majority of severe cases classified as viral. To successfully develop treatments and vaccines, a comprehensive understanding of viral genetic diversity is required; however, this remains largely uncatalogued for common respiratory viruses, such as respiratory syncytial virus (RSV). They will collect oropharyngeal swabs, blood culture isolates, or lower respiratory tract samples from adults and children with SARI at nine sites. They will extract RNA and leverage pandemic sequencing infrastructure for sequencing, taxonomic identification and phylogenetic analyses to guide molecular epidemiology and outbreak investigations. The data will be integrated with a country-wide genomic surveillance strategy, currently under development.

This grant is one of three grants that are funded and administered by the Programme for Research in Epidemic Preparedness and Response (PREPARE) in Singapore.

Mai Le of the National Institute of Hygiene and Epidemiology in Vietnam will expand Vietnam’s systematic surveillance and sequencing capacities to detect potential pandemic pathogens, including influenza and coronaviruses, and incorporate agnostic sequencing of conventionally undiagnosed pathogens. They will build on the existing infrastructure of the influenza-like illnesses sentinel surveillance network, which collects samples from four outpatient clinics, to include testing for both influenza A and B and SARS-CoV-2 viruses, with the possibility to expand. They will also revive the hospital-based Severe Acute Respiratory Infections (SARI) surveillance network, which works with three hospital emergency departments and ICUs, to focus on 12 pathogens and incorporate an agnostic sequencing component. Their activities will include training health workers in sample collection and scientists in directed and agnostic sequencing of respiratory pathogens and bioinformatics analysis. The data produced will be shared in real-time on an online dashboard.

This grant is one of three grants that are funded and administered by the Programme for Research in Epidemic Preparedness and Response (PREPARE) in Singapore.

Olayinka Omigbodun of the University of Ibadan in Nigeria will build a critical mass of female researchers and policymakers to adapt and apply diverse mathematical models to better understand the epidemiology of depression in young women in sub-Saharan Africa and identify more effective preventative measures and treatments. Adolescent girls and young women in sub-Saharan Africa are three times more likely than their male counterparts to have a depressive disorder. Mathematical modeling provides a powerful means of predicting the dynamics of depression. However, there is a paucity of models that inform mental health strategies in this region. They will leverage existing research networks across the region to train new female modelers and, together with them, critique existing mathematical models of mental health and depression. This will enable the development of more suitable models, populated with local data, to identify predictors of depression in this group.

Cameron Myhrvold of Princeton University and Mireille Kamariza of the University of California, Los Angeles, both in the U.S., will develop an assay to rapidly detect multiple drug resistance mutations in Plasmodium falciparum and Mycobacterium tuberculosis for malaria and tuberculosis (TB) surveillance, respectively. Malaria and TB are two of the world's deadliest infectious diseases. Rapid and accurate drug resistance testing can save lives but current assays are slow or difficult to scale. Combinatorial Arrayed Reactions for Multiplexed Evaluation of Nucleic acids (CARMEN) is a CRISPR-based diagnostic test that detects nucleic acid biomarkers, such as those in pathogens, with high specificity and throughput. They have developed microfluidic CARMEN (mCARMEN), which produces results in under five hours, and will use an algorithm to design assays that detect the top ten drug-resistant P. falciparum mutations from blood samples, and M. tuberculosis mutations from saliva samples that confer resistance to two first-line TB drugs.

Alex Ario of Makerere University in Uganda, together with the Uganda National Institute of Public Health, the Ministry of Health of Uganda, and sister organizations in the East Africa region will expand their modeling capacities and establish collaborative research groups to apply modeling and data analytics to study health issues disproportionately affecting women. They will set up a multi-country steering committee to identify teams of modelers in Uganda, Kenya, and Rwanda. This committee will also select the most pressing women’s health issues and assign them to the modeling teams for investigation. They will also train modelers, particularly women, through on-the-job teaching and mentorship. The main findings from the collaborative studies will be disseminated to decision-makers and they will also advocate to influence policy.

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.

Esnat Chirwa of the South African Medical Research Council in South Africa will strengthen modeling and data science capacities by incorporating training and networking approaches, particularly for female researchers in Malawi and South Africa. The rising disease burden in sub-Saharan Africa has resulted in the generation of many large, complex datasets; although these provide rich research resources, local analytical capabilities are limited. They will increase the number of female modelers and statisticians by providing financial support to seven female Biostatistics and Statistics master’s students, who will be mentored by their team, and a series of free, short in-person and online advanced statistics courses to over 90 more female researchers. They will also build networks between female researchers to facilitate collaborations on defined topics, including identifying the mechanisms driving women’s health outcomes in Southern Africa and the long-term impact of rape on mental health.

Project intends to discover high affinity and specific ligands called suprabodies as the biomarkers of Lymphatic Filariasis.

Maurício Barreto and colleagues of Fiocruz in Brazil, together with Alexa Heeks and colleagues of the Health Foundation of South Africa in South Africa, will employ real-world data from two large countries of the Global South to develop a common data model of infectious diseases affecting pregnant women to identify causes and aid intervention development. Centro de Integração de Dados e Conhecimentos para Saúde (CIDACS), together with the Western Cape Provincial Health Data Centre (WCPHDC), have built data systems to utilize routinely collected health data for exploring disease impacts. They will leverage these data systems to explore the impact of gestational syphilis in Bahia, Brazil, and tuberculosis in the Western Cape province of South Africa, and the coverage and effects of screening interventions. Teams will include data curators, analysts and scientists, who will perform data discovery and processing, alongside epidemiologists, clinicians and public health specialists, who will perform epidemiological analyses and community engagements.

Alexa Heeks and colleagues of the Health Foundation of South Africa in South Africa, together with Maurício Barreto and colleagues of Fiocruz in Brazil, will employ real-world data from two large countries of the Global South to develop a common data model of infectious diseases affecting pregnant women to identify causes and aid intervention development. Centro de Integração de Dados e Conhecimentos para Saúde (CIDACS), together with the Western Cape Provincial Health Data Centre (WCPHDC), have built data systems to utilize routinely collected health data for exploring disease impacts. They will leverage these data systems to explore the impact of gestational syphilis in Bahia, Brazil, and tuberculosis in the Western Cape province of South Africa, and the coverage and effects of screening interventions. Teams will include data curators, analysts and scientists, who will perform data discovery and processing, alongside epidemiologists, clinicians and public health specialists, who will perform epidemiological analyses and community engagements.

Seth Adu-Afarwuah of the University of Ghana in Ghana and Julie Croff of Oklahoma State University Center for Health Sciences in the U.S. will assess the effects of nutritional supplementation on adolescent brain development in low-resource settings to support interventions. Nutritional behavior majorly impacts the rapid stage of adolescent neurodevelopment, which in turn impacts future generations through effects on maternal and paternal nutritional status, cognition and parenting. However, little is known about typical adolescent neurodevelopment in low- and middle-income countries, where 90% of the world’s adolescents live. They will recruit 40–60 post-pubertal adolescents in Accra, Ghana, measure their corticolimbic system development over nine months, and assess their problem-solving, planning and cognitive functioning. In another cohort of 40–60 post-pubertal adolescents, they will measure adherence to an eight-month twice-daily micronutrient supplementation program and associated nutritional outcomes.

Julie Croff of Oklahoma State University Center for Health Sciences in the U.S. and Seth Adu-Afarwuah of the University of Ghana in Ghana will assess the effects of nutritional supplementation on adolescent brain development in low-resource settings to support interventions. Nutritional behavior majorly impacts the rapid stage of adolescent neurodevelopment, which in turn impacts future generations through effects on maternal and paternal nutritional status, cognition and parenting. However, little is known about typical adolescent neurodevelopment in low- and middle-income countries, where 90% of the world’s adolescents live. They will recruit 40–60 post-pubertal adolescents in Accra, Ghana, measure their corticolimbic system development over nine months, and assess their problem-solving, planning and cognitive functioning. In another cohort of 40–60 post-pubertal adolescents, they will measure adherence to an eight-month twice-daily micronutrient supplementation program and associated nutritional outcomes.

Brigida Rusconi of Washington University in the U.S. will determine whether female infants develop long-lived antibodies against gut bacteria that subsequently both protect against bacterial infections and promote healthy gut immune and microbiota development in their offspring. Enteric bacterial infections are leading causes of infant morbidity in low- and middle-income countries. Using their mouse model, they found that mothers lacking IgG antibodies, which normally develop before weaning, are unable to provide passive protection against enteric infections to their pups. They will adapt their microbial flow cytometry to test whether maternal serum IgGs react more strongly to infant gut bacteria, suggesting establishment in infancy, and whether they provide passive immunity during pregnancy. They will also analyze plasma from two-year-old infants to identify those with weak IgG reactivity and potential causes. Finally, using a malnutrition cohort in Pakistan, they will train local bioinformaticians and assess whether malnutrition inhibits anti-gut commensal IgG responses.

Sandra Agudelo-Londoño of the Pontificia Universidad Javeriana in Bogota, in collaboration with various partners across Colombia including Yadira Eugenia Borrero Ramirez at the University of Antioquia in Medellín, will apply gender-transformative and feminist-based approaches to data analysis to identify the structural barriers affecting women's health in Colombia. Women's health is a complex issue with biological, historical, sociocultural, economic, and political aspects. The Global South has few female data modelers and no training or mentoring networks for women. They have therefore assembled an interdisciplinary group of female scholars and will deploy five virtual training courses on an open and free educational platform, focusing on gender, feminism, and health data analysis, alongside political advocacy, and data-driven decisions. They will also create a health data feminist network and use an existing gender-specific health and social dataset to conduct a comprehensive analysis focused on health issues disproportionally affecting women.

Laeticia Toe of the Institut de Recherche en Sciences de la Sante in Burkina Faso will use a metabolomics profiling platform to identify new biomarkers that can be used to diagnose environmental enteric dysfunction (EED) in women of reproductive age. EED affects nutrient absorption and immune function and may cause adverse birth outcomes in pregnant women. It is widespread in deprived areas in low- and middle-income settings but is often undiagnosed because the gold-standard diagnostic method requires an invasive procedure by trained personnel. They will determine the prevalence of EED by performing ELISA on existing plasma, serum and stool samples from 80 women of reproductive age living in rural Burkina Faso. They will then apply untargeted metabolomics on the samples to identify biomarkers that can be integrated with inflammatory markers and sequencing data and cross-validated for large-scale diagnoses of EED in women from low-resource settings.

Daniel Kiboi of the Jomo Kenyatta University of Agriculture and Technology in Kenya will assess whether a novel mutation in the human malaria parasite, Plasmodium falciparum, can be used as a marker to identify drug-resistant malaria and protect key antimalarial drugs. Emerging P. falciparum variants resistant to the three frontline drugs kill millions of people annually but are hard to detect. A better understanding of how these variants resist the actions of existing drugs can help to develop more effective drugs. They previously used a mouse malaria model to produce Plasmodium parasites resistant to all three main drugs and identified the candidate mutated protein likely causing this resistance. They will use in silico bioinformatics analysis, CRISPR/Cas9 approaches, and in vitro drug susceptibility assays to evaluate and validate this mutant protein and determine its role in drug resistance in the human malaria parasite.

Yingda Xie of Rutgers New Jersey Medical School in the U.S. and Joaniter Nankabirwa of Makerere University in Uganda will use CRISPR-based technology to monitor respiratory, food-borne and antimicrobial-resistant pathogens in Ugandan wastewater. A recent Ebola outbreak in Uganda highlights the need for routine multi-pathogen surveillance. However, the vast quantities and diversities of microbes in wastewater make it hard to identify those that might cause deadly outbreaks. They will combine CRISPR-based diagnostics with the recently developed multiplex assay, Combinatorial Arrayed Reactions for Multiplexed Evaluation of Nucleic acids (CARMEN), which enables highly sensitive and specific detection of over 150 nucleic acid sequences from dozens of samples in parallel. They will assess the performance of a field-deployable CRISPR assay to monitor specific pathogens in hospital sewage lines of Mulago Hospital. They will also leverage CARMEN to broadly survey for high-priority outbreak pathogens, including Ebola and yellow fever, in Kampala’s regional wastewater sources.

Amira Kebir of the Pasteur Institute of Tunis in Tunisia will create an African-based and -led learning and research network that links Francophone and Anglophone African research institutions to strengthen the capacity and ecosystem for modeling and analyzing women's health in Africa. They will train eight Ph.D. and Postdoctoral researchers in an intra-African collaboration to use modeling approaches on available datasets that can inform public health decisions. They will also establish a summer school and workshops for training up to twenty students. These trainees will be incorporated into modeling groups by partners in northern, western, central, and eastern Africa that will apply mathematical modeling and gender-based data analysis to investigate four infectious disease areas that highly impact women, namely human papillomavirus, hepatitis B virus, COVID-19, and antimicrobial resistance. They will also build a software platform to standardize data collection and manage project information and data security.

Berge Tsanou of the University of Dschang in Cameroon will support trainee mathematical modelers in epidemiology, particularly women, to strengthen capacity and to investigate health problems related to human papillomavirus (HPV) and cervical cancer (CC) in four Central-East African countries. Both HPV and CC are affected by HIV, all of which disproportionately affect women, particularly in sub-Saharan Africa. However, the nature of this interplay is largely unknown. They will synergize efforts across sub-Saharan Africa and use modeling approaches to study the co-evolution, prevention, and diagnosis of these diseases to enable earlier-stage treatments. They will support 30 master’s students, 14 PhD students, and three Postdoc fellows, at least 70% of whom will be female, and hold workshops to engage stakeholders and support evidence-based policymaking. They will also develop a dashboard and interactive software for ongoing disease surveillance in the region.

Praveen Devarsetty of the George Institute for Global Health in India will integrate an LLM into their SMARThealth Pregnancy application to enable two-way communication support for frontline health workers to improve healthcare services for pregnant and postpartum women in India. Reducing maternal and newborn mortality and morbidity is a global priority, particularly in low- and middle-income countries where information about medical conditions and pregnancy symptoms is difficult to access in simple terms and local languages. Together with experts, they will create an "encyclopedia" of pregnancy advice based on Indian and WHO guidelines, integrate ChatGPT-4 into their SMARThealth Pregnancy application, and evaluate the application for providing high-quality and contextually relevant healthcare information and services following prompts from healthcare workers.

Imad Elhajj of the Humanitarian Engineering Initiative of the American University of Beirut in Lebanon will use Large Language Models (LLMs) to develop an interactive community health promotion platform with a chatbot that provides accurate health messages and real-time responses to queries on platforms like WhatsApp to vulnerable populations in Lebanon and Jordan. They will process texts from trusted websites, documents, and other text repositories, such as UNICEF and the WHO, into smaller text segments. These segments will then be converted into fixed-length vectors that capture their semantic meaning and contextual relationships. To generate answers, the GPT-3.5/4 model will retrieve the relevant vectors based on the user's query and use them together with the context taken from the conversation history. They will first evaluate the platform internally to ensure the relevancy, coherence and accuracy of the generated messages, and then conduct a pilot study with a small representative group from the target communities.

Maryam Mustafa of the Lahore University of Management Sciences in Pakistan will build a voice-enabled, mobile phone-based, conversational AI assistant, Awaaz-e-Sehat, for maternal healthcare workers in Pakistan to create and manage detailed electronic medical records. Pakistan has among the poorest pregnancy outcomes worldwide. The lack of documented medical records of pregnant women seeking care makes it challenging for doctors to provide accurate diagnoses and contextualized care based on socio-economic and lifestyle factors, which also play a vital role in maternal health outcomes. They will develop a proof-of-concept system comprising an intuitive user interface speech recognition module and a text recognition module to record audio responses in different languages following specific prompts. The system will then convert responses into text and populate a template electronic medical record in Urdu. Awaaz-e-Sehat will be evaluated by maternal healthcare workers at Shalamar Hospital for its ability to collect records from 500 patients.

Nirmal Ravi of EHA Clinics Ltd. in Nigeria will develop and test scalable and cost-effective ways to use large language models (LLMs) such as ChatGPT-4 to provide “second opinions” for community health workers (CHEWs) in low- and middle-income countries (LMICs). These second opinions would mirror what a reviewing physician might advise the provider in question after seeing or hearing their initial report. If LLMs can enhance the capabilities of CHEWs in this way, it could improve patient outcomes, free high-skill providers for other tasks, and mitigate the serious shortage of qualified health personnel in many LMICs. The specific outcomes of this project will be: a proof of concept that LLMs can be integrated within LMIC healthcare systems to improve quality of care; a proof of concept of a system architecture for LLMs that can be scaled up and deployed progressively in LMIC healthcare systems; and an initial understanding of the capacity of current LLMs to interact with CHEWs in LMIC settings.

Henrique Araujo Lima of the Universidade Federal de Minas Gerais in Brazil will develop a tool to systematically assess the accuracy and clarity of responses generated by Large Language Models (LLMs) to common questions on maternal health to increase their value in settings with limited healthcare access. To improve LLMs, it is essential to ensure the information they provide is both reliable and understandable, and for purposes such as health, LLMs will only be successful if both healthcare providers and users are confident about their benefits. They will collect the most common types of questions about maternal health in English, Portuguese, and Urdu, and submit them to the LLM. The quality of the answers will then be evaluated by medical experts from the U.S., Brazil and Pakistan, and the readability of the answers will be evaluated by individuals and a software model.

Shashi Jain of the Indian Institute of Science in India in collaboration with Uma Urs from Oxford Brookes University in the United Kingdom along with colleagues from Akaike and Kotak Mahindra Bank also in India, will build a GPT-enabled AI bot called SATHI, which stands for Scheme, Access, Training, Help, and Inclusion, to deliver information on the latest government financial schemes that support sectors, like micro-enterprises and farms, to potential customers and providers in rural and suburban India. Together with several partners, they will capture data and provide context to SATHI to enable it to answer queries related to financial schemes. They will also use a translation module so it can understand voice queries and respond with an audio answer in the local language. They will perform a field test at a bank branch to compare the use of SATHI alone with a human financial expert and with semi-experts supported by SATHI. They will collect data on customer satisfaction and their follow-up actions using standard field research methodology, including oral interviews and survey questionnaires.

Theofrida Maginga of the Sokoine University of Agriculture in Tanzania will develop a ChatGPT-powered Swahili chatbot for smallholder farmers with limited literacy and scarce resources in Tanzania to detect crop diseases quickly and easily. Maize is one of the most important crops in Tanzania and generates up to 50% of rural cash income. Several diseases that afflict maize are hard to detect visually, leading to substantial losses in crop productivity and income. They will integrate AI with Internet of Things (IoT) technologies that use non-invasive sensors to monitor the non-visual early indicators of diseases, including volatile organic compounds, ultrasound movements, and soil nutrient uptake. They will also develop and integrate a Swahili chatbot to interact with farmers in their local language in a culturally-sensitive manner and perform model validation and field testing.

Sophie Pascoe of Wits Health Consortium (Pty) Limited in South Africa, with support from the organizations, AUDERE in the U.S. and the Centre for HIV and AIDS Prevention Studies (CHAPS) in South Africa, will develop a Large Language Model (LLM)-based application, Your Choice, that interacts with individuals in a human-like way to respectfully obtain their sexual history and improve the accuracy of HIV risk assessments to control the epidemic in South Africa. Gathering accurate sexual history is essential for assessing HIV risk and prescribing preventative drugs but is challenging due to concerns about stigma and discrimination. Your Choice, which stands for Your Own Unique Risk Calculation for HIV-related Outcomes and Infections using a Chat Engine, leverages an LLM to ensure privacy and confidentiality, improve the accuracy of risk assessments, and increase awareness of preventative treatments. This solution would provide 24/7 access to an unbiased and non-judgmental counselor for marginalized and vulnerable populations specifically, greatly reducing the barriers and concerns around seeking advice. They will co-design the app with at-risk populations and evaluate a prototype using 550 public sector healthcare providers and clients.

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. They will source datasets for the analyst and assess the LLM's ability to automate checks and link multiple datasets. Improving interoperability between datasets will enable unique correlations to be identified between separate social indicators, which are currently recorded in distinct databases. They will also develop a user-friendly platform for output generation and visualization.

João Paulo Souza of the Fundação de Apoio ao Ensino, Pesquisa e Assistência in Brazil will determine whether Large Language Models (LLMs) can be utilized as accurate information sources to guide healthcare provider decision-making. Frontline health workers must make real-life care decisions by distinguishing between relevant and irrelevant information and contextualizing it to their setting. This is particularly challenging in remote areas with limited healthcare specialists. To support them, an information program, the Formative Second Opinion (FSO), was developed to produce curated evidence summaries based on a large repertoire of real-life clinical queries. An updated version of this program is now being developed to combine a mobile messaging platform with LLMs for regions with limited internet connectivity and computer access. Using a mixed-methods study approach, they will evaluate the accuracy of the evidence summaries generated by ChatGPT-4 and those created by humans to 450 randomly selected clinical questions.

Livia Oliveira-Ciabati of the Sociedade Beneficente Israelita Brasileira Albert Einstein Hospital in Brazil will leverage AI to produce guidance and monitoring tools for less-experienced or overworked health professionals providing prenatal care via telemedicine to people from minority groups and people with greater social vulnerabilities. During the COVID-19 pandemic, Brazil's maternal mortality rate jumped to 110 deaths per 100,000 live births, which is far from their target of reaching 30 deaths per 100,000 live births by 2030. To increase the quality of prenatal care, they will integrate LLMs with an API for converting and preparing voice data and develop a simple interface to present suggestions and results to the healthcare professional. The training database will be developed using protocols defined and validated by the scientific community, including from the Global South. They will test their model with health professionals by comparing the professional decisions with the AI's suggestions which they will then follow up with a randomized clinical trial.

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. However, to create an impact in Rwanda, LLMs must be able to converse in Kinyarwanda, and current approaches to train LLMs in relatively minor languages are too expensive for low-resource nations. They will develop a support tool for community health workers focused on malnutrition leveraging GPT-4 as a knowledge base and an Mbaza English-Kinyarwanda translation model. The integrated support tool will be evaluated by assessing the quality of its translations.

Nana Kofi Quakyi of the Aurum Institute in Ghana will develop an AI-powered decision support tool for antibiotic prescribers to improve appropriate antimicrobial usage and combat antimicrobial resistance (AMR) in Ghana. AMR is a major public health concern, with the highest mortality rates occurring in Africa. To address this, Ghana's National Action Plan for Antimicrobial Use and Resistance (2018) identified the need for support tools that provide personalized, adaptable, and context-sensitive recommendations. They will develop an interactive, AI-powered clinical decision support tool that allows prescribers to enter prompts, respond to system queries, and receive personalized, real-time antibiotic prescribing recommendations, such as drug, dosage, and duration of administration. The model will be trained with a comprehensive dataset consisting of clinical guidelines, research data, expert opinions, and surveillance evidence that has been reviewed for its representativeness. The proposal includes field testing, iterative refinement, and engagement with stakeholders to ensure effectiveness and scalability.

Nirat Bhatnagar of the Belongg Community Ventures Private Ltd. in India, in collaboration with colleagues at ARTPARK also in India, will develop a Large Language Model (LLM)-based tool to enable development practitioners, funders, and researchers to adopt more equitable approaches, particularly addressing the intersections of marginalization. They will assemble a comprehensive and trusted corpus of development research papers, reports, and media articles and use it to build a user-friendly website and a backend ChatGPT 4.0 API-based LLM model. Users will be able to upload their draft research or program proposals and receive tailored recommendations on how to increase inclusivity across dimensions such as gender, disability, caste, religion, ethnicity, and sexual orientation. The tool will also connect users with researchers and experts with experience living with marginalized identities.

Yogesh Hooda of the Child Health Research Foundation in Bangladesh will use AI-based tools to teach low- and middle-income scientists to perform modeling and prediction studies in public health, which are dominated by researchers in the Global North. The codes generated during modeling studies are not often shared amongst researchers, making the methods difficult to learn. They found that ChatGPT could produce a code using a published model in just three weeks with only a beginner-level programmer and a biostatistician. Using epidemiological and demographic data and medical records collected from a catchment area, they will adapt published code with the help of ChatGPT to predict the impact of introducing specific vaccines in Bangladesh. They will also develop a curriculum, covering the basics of ChatGPT, data preprocessing and modeling techniques, for a course that they will pilot with public health professionals and students. All materials will be openly available in Bangla and English.

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). Health disparities are also prevalent, particularly for women and children. They will leverage existing LLMs specifically designed for health data and use the SKMCH&RC database, which contains both free text and structured data for 250,000 patients, to ensure that key subjective information, such as family history, and electronic health records are included. They will validate their model using available data on COVID-19 infections in Pakistan and evaluate its performance in terms of accuracy and speed.

Khoa Doan of VinUniversity in Vietnam together with Helen Meng, Viet Anh Nguyen, and colleagues from the Centre for Perceptual and Interactive Intelligence and The Chinese University of Hong Kong, both in Hong Kong; in collaboration with the Hanoi Obstetrics & Gynecology Hospital in Vietnam, will build a conversational AI chatbot to scale up gynecological healthcare support for women and LGBT+ communities in Vietnam. Access to gynecological healthcare in Vietnam is limited, particularly in remote regions and for minority groups due to high costs, low investment, social stigmas, and misinformation. They will build a tool to provide informational and psychological support, adapted to Vietnamese linguistic and cultural contexts and able to operate in low-resource settings. The chatbot will consist of a scientific database, a GPT-like conversational system, a voice generation engine, and a sentiment analytics module to evaluate the psychological traits of the user. It will be capable of empathetic dialogues that encourage users to share their symptoms. Data from patient-clinician dialogs will be used as a reference for the design of an initial patient-AI prototype.

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. They will assess the accuracy and empathy of Kem+ChatGPT's responses to diverse inquiries from women, its improved ability to triage based on reproductive stages and risk factors, and the effectiveness of human health coaches leveraging ChatGPT-4 as a resource for answering more complex questions. This will involve field testing with a cohort of 300 women of reproductive age. They will also hold a series of iterative user-testing workshops across the states with at least 160 members from communities to observe usability and provide feedback for product refinement.

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. They will form an interdisciplinary Community Advisory Board, including government officials, researchers, and young people, to guide the design, analysis, and dissemination of the app. They will recruit Kenyans aged 18–34 from community-based sites, such as universities and malls, to evaluate the application's ability to improve knowledge, attitudes, and practices surrounding NCD risk factors.