Machine Learning Stroke Identification with David Golan

When a patient comes into the hospital with stroke symptoms, the hospital will give that patient a CAT scan, a 3-dimensional imaging of the patient’s brain. The CAT scan needs to be examined by a radiologist, and the radiologist will decide whether to refer the patient to an interventionist–a surgeon who can perform an operation to lower the risk of long-term damage to the patient’s brain function.

After getting the CAT scan, the patient might wait for hours before a radiologist has a chance to look at the scan. In that period of time, the patient’s brain function might be rapidly degrading. To speed up this workflow, a company called built a machine learning model that can recognize whether a patient is at high risk of stroke consequences or not.

Many people have predicted that radiologists will be automated away by machine learning in the coming years. This episode presents a much more realistic perspective: first of all, we don’t have nearly enough radiologists, so if we can create automated radiologists that would be a very good thing; second of all, even in this workflow with a cutting-edge machine learning radiologist, you still need the human radiologist in the loop.

David Golan is the CTO at, and in today’s show he explains why he is working on a system for automated stroke identification, and the engineering challenges in building that system.


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