App/Software

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.

Suhani Jalota of the Myna Mahila Foundation in India will build a chatbot, Myna Bolo, by incorporating Large Language Models (LLMs) into their health application to provide tailored sexual and reproductive health services through smartphones, via text or audio, in local languages to women in India. In India, 71% of girls report not knowing about menstruation before their first period. This is because of limited access to unbiased information due to stigma, discrimination, and lack of resources.

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.

Essa Mohamedali and Kalebu Gwalugano of the Tanzania AI Community in Tanzania will use ChatGPT-4 to develop a chatbot and support tool to help healthcare workers adhere to the Integrated Management of Child Illness (IMCI) guidelines and access updates and alternative treatment options by linking them to the latest research via their mobile phones. Access to formal training on the IMCI guidelines is limited for healthcare workers, particularly in the private sector, and its duration makes it prohibitively expensive for companies.

Michael Leventhal of the Association RobotsMali in Mali will determine whether ChatGPT-4 can support curriculum development and teacher training to improve literacy in Mali, which has 65% illiteracy. The West African language Bambara is the most widely spoken language of Mali, but there is almost no literature in Bambara and few Malians can read their mother tongue. Education is provided almost entirely in French, a language most Malians do not understand, and in a cultural context foreign to Malian children.

Suzanne Staples of the THINK Tuberculosis and HIV Investigative Network (RF) NPC in South Africa and Kristina Wallengren of THINK International in Denmark will produce a toolkit that leverages ChatGPT for the analysis and interpretation of health program data in low- and middle-income countries (LMICs). Due to resource constraints, data analysis takes a back seat to diagnostics and treatments and is a scarce skill in LMICs, particularly in the public health sector. In addition, health data management is hindered by manual and fragmented electronic datasets.

Joseph Mulabbi of Comzine Tech And Investments Limited - Dromedic Health Care in Uganda will use ChatGPT-4 to optimize the surveillance of zoonotic diseases and predict future pandemics. Zoonoses are infectious human diseases that originate from animals and represent over 75% of all emerging diseases. Predicting the emergence of a zoonotic disease currently requires manual monitoring of the dynamic interactions between humans and livestock, which is time-consuming, resource-intensive, and prone to delays.

Leonora Tima of Kwanele - Bringing Women Justice in South Africa will develop a mobile application and chatbot to provide understandable legal information on gender-based violence (GBV) to vulnerable groups, including high school learners, young women, survivors of GBV, members of the LGBTQIA+ community and sex workers. South Africa faces disproportionately high rates of GBV but lacks access to justice and understandable legal information for survivors.

Henrique Dias of the Instituto de Inteligencia Artificial na Saude in Brazil will determine whether AI can produce an accurate hospital discharge summary to ensure that essential information is passed to the next healthcare provider and patient care is maintained. Discharge summaries are often incomplete, unclear, or delayed in terms of their delivery due to the document construction process.

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.