Google AI Helps Doctors Decide When to Trust AI Diagnoses

AI has changed the face of many sectors, but in healthcare its adoption is moving at snail’s pace. The sector faces several challenges such as data privacy, security, lack of interoperability, and the absence of regulation, which have restricted its adoption.  AI models are prone to mistakes, and as we know it is human to err. Google Research has asked the question, what would be the error rates when you combine the expertise of predictive AI models and clinicians? In July this year, Google DeepMind joined hands with Google Research and introduced the Complimentary-driven-Deferral-to-Clinical-Workflow (CoDoC), a system that maximises accuracy by combining human expertise with predictive AI. The system essentially decides if the AI model is more accurate than a hypothetical clinician’s workflow of diagnosis. It does this using a confidence score of the predictive model as one of the inputs.  The comprehensive tests of the CoDoC with multiple real-world datasets ha