Abstract: Advances in machine learning and the explosion of clinical data have demonstrated immense potential to fundamentally improve clinical care and deepen our understanding of human health. However, algorithms for medical interventions and scientific discovery in heterogeneous patient populations are particularly challenged by the complexities of healthcare data. Not only are clinical data noisy, missing, and irregularly sampled, but questions of equity and fairness also raise grave concerns and create additional computational challenges. In this talk, I will focus on the computational challenges that impede our ability to use AI to provide equitable healthcare to all patients. The talk will span issues in censored and incomplete data collection, ethical ML task outcome definition, algorithmic development, and deployment considerations.