Connect Sign InEquitable Diagnostics: What Laboratory Medicine Can Learn from Algorithmic Fairness
As laboratory medicine moves toward greater integration of AI-driven tools, we must recognize the tendency of these algorithms to produce biased outputs, potentially exacerbating existing healthcare disparities. The field of algorithmic fairness provides a rigorous framework for defining fairness, identifying bias mechanisms, and engineering strategies to mitigate inequities in AI models. However, these lessons are not just relevant to the future of AI-based laboratory diagnostics—they also apply to our current laboratory practices, where bias arising throughout the total laboratory testing process can lead to inequitable patient outcomes. By drawing on insights from algorithmic fairness, laboratory medicine can proactively address inequities in both present-day workflows and future AI-driven decision-making, ensuring more just and effective patient care.
Mark A. Zaydman, MD, PhD, is an assistant professor of Pathology and Immunology at Washington University School of Medicine in St. Louis, Missouri. His work helps clinical laboratory professionals improve the quality, safety, and value of laboratory testing by enabling them to leverage healthcare data and data analytics to inform operational and clinical decisions so that patients can enjoy better healthcare at reduced costs to the patient and their institutions. He serves on the ADLM Data Analytics Steering Committee.
