Racial Equity Implications of Artificial Intelligence in Health Care

Patrick Ross


Decision-making using artificial intelligence (AI) aims to reduce human error and bias in the clinical decision process. The advanced pattern-finding capabilities of AI and machine learning serve as both promise and pitfall, as pattern recognition can unintentionally cause algorithms to incorporate human biases, such as racial, gender, or socioeconomic biases. Considering the health care applications of these tools, ensuring they are safe to deploy is critical. This requires a clear oversight structure from development to deployment. However, the unique ability to learn and adapt that fuels AI’s promise also presents a challenge to current regulatory frameworks.