The biggest threat to the adoption of artificial intelligence (AI) in healthcare is the concern that training algorithms on real world data will encrypt societal, institutional and individual biases, legitimize them and propagate them at scale. At present, the evaluation metric for machine learning in healthcare is accuracy. But just because an algorithm is accurate does not mean it should be implemented. If all that matters is accuracy, then algorithms developed using real-world data will encrypt the biases and prejudice that taint clinical decision-making. In an ideal world, only patient health and disease factors would determine — and guide the prediction of — clinical outcomes. However, studies have repeatedly demonstrated that this is far from the case. Women with heart attacks have worse outcomes when cared for by male cardiologists. Black newborns have better outcomes when their pediatricians are Black. Outcomes from sepsis are worse in hospitals that disproportionately treat minority patients after adjusting for illness severity and other confounders. To prevent AI from encoding social and cultural biases, we would like to predict an outcome if the world were fair, and the quality of care is the same across populations. We need algorithms that are better than humans - less prejudiced and more fair. 

Speaker Bio

Dr. Leo Celi is the clinical research director and principal research scientist at the MIT Laboratory for Computational Physiology (LCP), and a practicing intensive care unit (ICU) physician at the Beth Israel Deaconess Medical Center (BIDMC). In his work, Leo brings together clinicians and data scientists to support research using data routinely collected in the process of care. His group built and maintains the publicly-available Medical Information Mart for Intensive Care (MIMIC) database and the Philips-MIT eICU Collaborative Research Database, with more than 25,000 users from around the world. In addition, Leo is one of the course directors for HST.936 – global health informatics to improve quality of care, and HST.953 – collaborative data science in medicine, both at MIT. He is an editor of the textbook for each course, both released under an open access license. "Secondary Analysis of Electronic Health Records" has been downloaded more than a million times, and has been translated to Mandarin, Spanish, Korean and Portuguese. He is the inaugural editor of PLOS Digital Health.

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