Illustration of multicolored profiles. An overlay of strings of ones and zeroes is visible

Understanding Racial Bias in Medical AI Training Data

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

Cover of the book "Transparency in health and health care in the US"

Order Now: “Transparency in Health and Health Care in the United States”

Transparency is a concept that is becoming increasingly lauded as a solution to a host of problems in the American health care system. Transparency initiatives show great promise, including empowering patients and other stakeholders to make more efficient decisions, improve resource allocation, and better regulate the health care industry.

Nevertheless, transparency is not a cure-all for the problems facing the modern health care system. The authors of this volume present a nuanced view of transparency, exploring ways in which transparency has succeeded and ways in which transparency initiatives have room for improvement. Read More