Working in a Healthcare startup, one of my most frustrating experiences was to ask doctors to do tedious work of data annotation or result verification. Surely there’s a better way, I told myself, to exploit the knowledge and expertise of doctors, than to turn them into a labeling conveyor belt!
Well, it turns out there is.
Human-in-the-loop ML refers to human-machine interaction in data annotation and model training. In Zebra Medical we used Human-in-the-loop techniques to compensate for lack of tagged data and to better exploit clinical expert knowledge. In this lecture I will show how to make data annotation quicker and smarter by turning it into an interactive process, and how an interactive process of experts and models writing rules together can improve your model performance without additional training.
This talk is intended for AI researchers interested in better ways to exploit the knowledge and experience of domain experts, and for people interested in the challenges of AI in the Healthcare domain.