Discovering mechanisms underlying medical AI prediction of protected attributes

Abstract

Recent advances in Artificial Intelligence (AI) have started disrupting the healthcare industry, especially medical imaging, and AI devices are increasingly being deployed into clinical practice. Such classifiers have previously demonstrated the ability to discern a range of protected demographic attributes (like race, age, sex) from medical images with unexpectedly high performance, a sensitive task which is difficult even for trained physicians. Focusing on the task of predicting sex from dermoscopic images of skin lesions, we are successfully able to train high-performing classifiers achieving a ROC-AUC score of ∼0.78. We highlight how incorrect use of these demographic shortcuts can have a detrimental effect on the performance of a clinically relevant downstream task like disease diagnosis under a domain shift. Further, we employ various explainable AI (XAI) techniques to identify specific signals which can be leveraged to predict sex. Finally, we introduce a technique to quantify how much a signal contributes to the classification performance. Using this technique and the signals identified, we are able to explain ∼44% of the total performance. This analysis not only underscores the importance of cautious AI application in healthcare but also opens avenues for improving the transparency and reliability of AI-driven diagnostic tools.