Скачать или смотреть CCMB Seminar 10/21/2020 - Michael Mathis, MD

"Machine Learning Applications for Anesthesiology: Overcoming Hurdles to Clinician Trust and Use"

Presented by Michael Mathis, MD

Abstract

Although machine learning applications are now pervasive to every industry, adoption into healthcare remains a challenging and arduous process. Barriers to implementation include clinician trust, algorithm credibility and actionability, promoting clinician literacy in machine learning methods, and mitigating unintended consequences.

In the high-risk operating room setting, anesthesiologists are recognized leaders in patient safety, and manage uncertainty through careful considerations of risk and benefit based upon a thorough understanding of disease processes and treatment mechanisms. In this talk, the speaker highlights how obstacles to implementation of machine-learning based healthcare applications can be mitigated, and how an understanding of such applications can be promoted among clinically-minded anesthesiologists who may not necessarily be expert data scientists.