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 triaging to drug pricing.
Yet, as researchers across technology and medical fields agree, “AI systems are only as good as the data we put into them.” When AI systems are trained on patient datasets that are incomplete or under/misrepresentative of certain populations, they stand to develop discriminatory biases in their outcomes. In this article, we present three examples that demonstrate the potential for racial bias in medical AI based on training data. Read More