Optimizing risk-based breast cancer screening policies with reinforcement learning

Dr. Adam Yala et al developed Tempo, a reinforcement-learning based framework for deriving personalized screening policies. Tempo policies were significantly more efficient than existing clinical guidelines across diverse test sets from four hospitals.