Optimizing Equity: Working towards Fair Machine Learning Algorithms in Laboratory Medicine.

Journal: The journal of applied laboratory medicine
Published Date:

Abstract

BACKGROUND: Methods of machine learning provide opportunities to use real-world data to solve complex problems. Applications of these methods in laboratory medicine promise to increase diagnostic accuracy and streamline laboratory operations leading to improvement in the quality and efficiency of healthcare delivery. However, machine learning models are vulnerable to learning from undesirable patterns in the data that reflect societal biases. As a result, irresponsible application of machine learning may lead to the perpetuation, or even amplification, of existing disparities in healthcare outcomes.

Authors

  • Vahid Azimi
  • Mark A Zaydman
    Department of Pathology & Immunology, Washington University School of Medicine, Campus Box 8118, 660 South Euclid Avenue, St Louis, MO 63110, USA.