Awards

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.

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.

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.

Darlington Akogo of MinoHealth AI Labs in Ghana will leverage a multimodal Large Language Model (LLM) to generate accurate and comprehensive medical reports based on the analysis of medical images to reduce the need for manual reports and enhance diagnostic capabilities for radiologists and clinicians. African healthcare systems have excessively high patient-to-doctor ratios and prevalent diseases and severely inadequate numbers of radiologists. They will fine-tune a multimodal LLM applied to radiology and medical imaging using a supervised approach with a labeled dataset of medical images and corresponding reports collected from facilities across Ghana and Africa. The platform will enable interactive conversations with clinicians seeking answers to specific queries or clarifications regarding medical images. They will use metrics and humans to evaluate the model and assess its ability to generate accurate and comprehensive medical reports. They will also conduct field testing with clinicians and individuals from diverse demographics.

Floris Sonnemans of Degas Ghana Limited in Ghana will apply AI technology to support African smallholder farmers to implement more climate-adaptive and regenerative agricultural (RA) techniques, such as crop diversification, and scale climate action across the continent. Africa only contributes 3.8% of global greenhouse gas emissions but experiences the harshest impacts, particularly on food production. However, protective RA techniques are relatively new and challenging to adopt, and there are not enough field agents to support farmers and respond to queries. To address this, they will integrate a Large Language Model (LLM)-based system trained on RA manuals into their existing agent-facing application. This system will provide an easy interface for farmers to access information and guidance to effectively implement RA practices, such as biochar application, minimal tillage, and permanent organic soil coverage. The application will be field tested amongst field agents and farmers.