White Coat, Black Box: Augmenting Clinical Care with AI in the Era of Deep Learning

Though the potential impact of machine learning in healthcare warrants genuine enthusiasm, the increasing computerization of the field is still often seen as a negative rather than a positive. In this talk, I will highlight two key challenges related to applying machine learning in healthcare: i) interpretability and ii) small sample size. First, machine learning has often been criticized for producing ‘black boxes.’ In this talk, I will argue that interpretability is neither necessary nor sufficient, demonstrating that even interpretable models can lack common sense. To address this issue, we propose a novel regularization method that enables the incorporation of domain knowledge during model training, leading to increased robustness. Second, machine learning techniques benefit from large amounts of data. However, oftentimes in healthcare we find ourselves in data poor settings (i.e., small sample sizes). I will show how domain knowledge can help guide architecture choices and efficiently make use of available data. In summary, there’s a critical need for machine learning in healthcare; however, the safe and meaningful adoption of these techniques requires close collaboration in interdisciplinary teams.