Dr. Ziad Obermeyer discovers that by training an algorithm to listen to patients—not doctors—researchers can diagnose and fix racial biases built into medical knowledge.

Dr. Ahmed Alaa et al develop a unified evaluation protocol, the echocardiographic task adaptation benchmark (ETAB). Echocardiography is one of the most commonly used non-invasive imaging techniques in Cardiology. ETAB is a benchmarking framework for developing deep learning models of echocardiograms using publicly available data sets.

Dr. Irene Chen, et al frame ethics of ML in healthcare through the lens of social justice. They describe ongoing efforts and outline challenges in a proposed pipeline of ethical ML in health, ranging from problem selection to post deployment considerations.

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.