AI and Digital Inclusion in Global Health

With the advent of Artificial Intelligence (AI) in both healthcare and public health, the digital divide is seen to be growing even more substantial in Low- and Middle-Income Countries (LMICs) in comparison to High-Income Countries (HICs). The reasons for this development are various: a limited number of large companies are dominating the market, training models particularly utilizing English language documents for training data neglecting languages leading to bias, non-affordability of smart phones hold market and lack of training data from diverse cultural information lead to bias in algorithm development apart from basic issues like lack of electricity and IT-infrastructure.

This session will address these issues, thriving to discuss barriers and possible solutions to circumvent these barriers to narrow the digital gap particularly caused by the rollout of AI based applications. We focus on global public health only (communication and information gathering, infectious disease surveillance and monitoring, crisis management, service delivery etc.). Issues that should be tackled are contexts for measurement (identify AI components and functions within operational systems, outputs and risks, service context and boundaries, identifying key stakeholders and monitoring goals (e.g. safety, usability, transparency, reliability, trust), as well as methods for indicator selection and categorization and feedback from different user groups.