Machine-learning algorithms and artificial intelligence software help organizations analyze large amounts of data to improve decision-making, and these tools are increasingly used in hospitals to guide treatment decisions and improve efficiency. The algorithms “learn” by identifying patterns in data collected over many years. So, what happens when the data being analyzed reflects historical bias against vulnerable populations? Is it possible for these algorithms to promote further bias, leading to inequality in health care?
Marshall Chin, MD, MPH, the Richard Parrillo Family Professor of Healthcare Ethics at the University of Chicago Medicine, is working to ensure equity across all areas of the healthcare system, including data analysis. He has worked for three decades to examine and develop solutions addressing health disparities. Chin recently teamed up with a group of data scientists from Google to write an article in the Annals of Internal Medicine that discusses how health care providers can make these powerful new algorithms fairer and more equitable. We spoke to him about the use of machine learning in health care, and how doctors and patients can build fairness into every step of the decision-making process.
Originally published in The Forefront