Grand Challenges is a family of initiatives fostering innovation to solve key global health and development problems. Each initiative is an experiment in the use of challenges to focus innovation on making an impact. Individual challenges address some of the same problems, but from differing perspectives.
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Olubayo Adekanmbi of Data Science Nigeria in Nigeria will develop a multilingual, voice-based chatbot to demystify complex financial concepts and provide customized financial support to informal traders, women business owners, and smallholder farmers in Nigeria. These groups are often disadvantaged due to their low income and literacy and are historically underserved by conventional financial systems. They will create a chatbot capable of recording transactions from verbal inputs, such as "I bought four oranges at N50 naira," and answering financial questions. Customized financial guidance will be communicated back to the end user via voice note using text-to-speech. They will engage users in the design process and build large, locally-orientated financial datasets. They will then merge speech-to-text technology and GPT learning capabilities with an AI-driven financial management tool. The chatbot will be tested using their existing network of informal traders, and the feedback used for refinement and improvement.
Chinazo Anebelundu of DSN Ai Innovations Limited in Nigeria will develop a Science, Technology, Engineering, and Mathematics (STEM)-focused multimedia learning platform by leveraging GPT and DeepBrain text-to-video AI tailored to rural students to increase their engagement. Nigeria has an estimated 18.5 million students out of school, a population that can potentially be reached through this more engaging and personalized modality. This platform will integrate local contexts and nuances to enhance student comprehension of STEM subjects. It will also tailor the learning to the preferences of each student and employ visual activities such as interactive STEM laboratory simulations. For each subject, they will collect a diverse array of educational resources and construct the curriculum. This curriculum will then be transformed by Large Language Models (LLMs) into a script and then into video lessons that can be personalized to a specific student. The platform will be evaluated for the degree of student engagement and accuracy of content, amongst other measures, using their existing network of students and teachers.
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.
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.