Haywire Algorithms

The pandemic changed how we lived. And this had a ripple effect on the performance of machine learning models. Ravi Parikh joins us today to discuss how the pandemic has affected the performance of machine learning models in clinical care and some actionable steps to fix it.

[Click here for additional show notes](https://dataskeptic.com/blog/episodes/2022/haywire-algorithms)

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Guest

Ravi Parikh: Ravi B. Parikh, MD, MPP is a medical oncologist and Assistant Professor in Medical Ethics and Health Policy and Medicine at the University of Pennsylvania, Staff Physician at the Philadelphia VA Medical Center, and Senior Fellow at the Leonard Davis Institute. His research examines ethical and policy questions in artificial intelligence in healthcare. His research group runs clinical trials testing machine learning interventions to improve patient outcomes. Dr. Parikh’s work has been published in Science, The New England Journal of Medicine, JAMA, and other high-impact publications. He sits on the Board of the Coalition to Transform Advanced Care (C-TAC) and the Leadership Consortium of the National Quality Forum. Dr. Parikh is a graduate of Harvard Medical School, Harvard College, and the Kennedy School of Government. He completed residency in internal medicine at Brigham and Women’s Hospital and fellowship in Hematology/Oncology at the University of Pennsylvania.