Challenges and solutions in adapting an AI-powered health management tool for South African

Abstract Background Africa has the lowest density of healthcare workers in the world and artificial intelligence (AI) technology can assist doctors and patients to bridge that gap. This paper describes the 3 challenges we identified to adapt Ada, an AI-powered health management tool, to assist pregnant women and mothers in South Africa (SA). Challenges and solutions Localization Maternal and child health challenges in SA differ from those in the West. We adapted Ada's knowledge base to account for regional differences in incidence and disease presentation. We optimized 51 maternal and pediatric health conditions. Readability An improved readability leads to better understanding of medical content. We calculated the readability score of Ada's consumer-facing medical content using the Automated Readability Index and deemed it too high (grade 11.0±1.8, range=5.8-17.5). Using Content Design London's Readability Guidelines, we reduced the readability score of Ada's content to below grade 8 (7.4±0.8, range=4.6-10.2) while maintaining medical accuracy. Different approaches to AI The majority of medical research is conducted in high-income countries and tends to recruit people with high literacy levels. This means the differences in underrepresented populations and low-income countries are not captured in medical research. When this data is applied to technology and AI, it can lead to bias. To reduce this bias, we use a white-box approach. This allows us to deliver solutions to meet the needs of a specific population. Using the expertise of medical doctors with clinical experience in low-income areas, we curated a medical knowledge base that was specific to our target population. Conclusions AI solutions can only improve health outcomes in low-income countries if they are designed and adapted with the needs of the local population in mind. By partnering with local health departments, AI health companies can assist doctors and patients to reduce the burden on vulnerable healthcare systems. Key messages White-box AI-approach makes it possible to build AI solutions that capture the differences in underrepresented populations and low-income countries. By partnering with local health departments, AI health companies can assist patients and doctors in reducing the burden on vulnerable healthcare systems.