Abstract: AI tools designed to aid clinicians in complex diagnostic decisions have the potential to enhance treatment selection and consequently improve patient outcomes. Despite their promise, simply applying existing AI approaches carries a significant risk of inadvertently perpetuating or even exacerbating biases present in clinical care. In this talk, I will describe our work in developing AI systems to diagnose common causes of acute respiratory failure (i.e., pneumonia, heart failure and/or chronic obstructive pulmonary disease), highlighting challenges and presenting potential strategies to mitigate the risk of these systems replicating harmful biases.