How Machine Learning Can Improve Medical Outcomes


Eric Oermann, MD, Neurosurgeon at Mount Sinai Health System, New York, discusses a slightly different topic in this talk: How machine learning can improve medical outcomes. He begins by emphasizing that deep learning is automated feature engineering and that deep neural networks learn hierarchical feature representations. To apply this to the work of physicians, Dr. Oermann poses the questions: What problems do we face as physicians and how can we translate those into machine learning to create better outcomes? He then provides examples of areas where machine learning could play a role in the hospital: Assessment in the ICU, brain biopsies, faster interpretation of imaging and AI augmented radiosurgery (AIAR).


Video Transcript

First, I'd like to thank Brainlab for inviting me to come here and allow me to talk about a slightly different topic from some of the other speakers. I promised to remove all the math equations, which I've succeeded at. I have a few disclosures but none of that are relevant to the current talk. So if I have one slide that I want ever everyone to take away from my talk and to remember it's this one, and it's that deep learning is automated feature engineering. And so what I mean by that is, let's say you are seeing...

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