Ensuring that biomedical AI benefits diverse populations

Artificial Intelligence (AI) can potentially impact many aspects of human health, from basic research discovery to individual health assessment. It is critical that these advances in technology broadly benefit diverse populations from around the world. This can be challenging because AI algorithms are often developed on non-representative samples and evaluated based on narrow metrics. Here we outline key challenges to biomedical AI in outcome design, data collection and technology evaluation, and use examples from precision health to illustrate how bias and health disparity may arise in each stage. We then suggest both short term approaches-more diverse data collection and AI monitoring-and longer term structural changes in funding, publications, and education to address these challenges. (C) 2021 The Authors. Published by Elsevier B.V.

Automated summary

AI has the potential to impact human health in various ways, but challenges such as non-representative samples and narrow evaluation metrics need to be addressed. Short-term solutions such as diverse data collection and ongoing monitoring, as well as long-term structural changes, may help mitigate these challenges.