UNSTRUCTURED Background Africa has the lowest density of healthcare workers globally and digital tools using artificial intelligence (AI) could bridge that gap affordably. A partnership between Ada Health and Praekelt.org sought to integrate Ada for users in South Africa (SA). Challenges and solutions Three challenges were identified to improve the efficacy of health AI in the local setting: Localization: disease incidences and presentations of maternal and child health in SA differ from those in Europe and North America. We adapted Ada’s knowledge base to meet these challenges. For this project, 25 maternal and 25 pediatric health conditions were localized, and one new pediatric condition was created for users of the Ada app. This included adding region-specific incidences and adapting the knowledge base for variations in disease presentation. Readability: the existing readability score of Ada’s consumer-facing medical content was calculated using the Automated Readability Index as grade 11.0 (± 1.8, range = 5.8-17.5). Using Content Design London’s Readability Guidelines, the readability score of Ada’s content was lowered to below grade 8 (7.4 ± 0.8, range = 4.6-10.2) while maintaining medical accuracy. Different approaches to AI: most medical research is conducted in high-income countries and among people with high literacy levels, thereby leading to bias in the system. Using a white-box approach allows intelligent solutions to be delivered that incorporate the needs of the population, including in low and middle income countries. Conclusion By partnering with grassroots and local organizations, such as Praekelt.org, AI companies can reduce the burden on vulnerable healthcare systems, but should be designed and adapted to the needs of the local population to account for regional differences in incidences and disease presentations, and language barriers. Furthermore, wherever possible, efforts should be made to reduce bias in AI by using white-box systems. It is possible to use simple language for consumer-facing medical text without compromising on medical quality. With a few minor adaptations AI-technology can be localized to serve global public health needs.
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