Approaches to using AI/ML to improve health outcomes in low resource settings

Artificial intelligence and machine learning (AI/ML) is transforming healthcare in developed markets, however, evidence is limited on how best to deploy and effectively scale AI solutions in health systems across LMICs. In this session, we will hear multiple approaches organizations are employing to use AI to improve health outcomes. Jhpiego will describe the development of a locally customized HIV Interruption In Treatment (IIT) risk prediction model, predicting a client’s interruption to treatment in Nigeria with their partner Palindrome Data. FHI 360 will share lessons learned from developing an ITT risk model for patients enrolled in care in Akwa Ibom and Cross River states in Nigeria. Fraym and their partner BAO Systems will share an innovative approach to market segmentation analysis in South Africa that could be used to identify the profiles of people living with HIV/AIDS most likely to be able and willing to pay for HIV services and treatment.

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