By Adriana Krasniansky Interest in artificially intelligent (AI) health care has grown at an astounding pace: the global AI health care market is expected to reach $17.8 billion by 2025 and AI-powered systems are being designed to support medical activities ranging from patient diagnosis and triagin…
AI-powered systems are being designed to support medical activities ranging from patient diagnosis and triaging to drug pricing. But when AI systems are trained on misrepresentative data sets they stand to develop discriminatory biases. Three case studies are explored that demonstrate the potential for racial bias in medical AI, including how AI may be used to improve care delivery by locating gaps in the care system, rebalance resources and review large sets of anonymized patient data.