Radiology: Artificial Intelligence

Although AI has generated excitement for the future of radiology, hopes for an automated radiological future have been dashed by reports of poor generalization of deep learning models. Models trained on images from one hospital can perform poorly when tested on images from a different one, often related to differences in disease prevalence between hospitals. Perhaps more concerning, deep learning models trained on chest radiographs (CXRs) with an underrepresentation of females have been shown to be biased for a variety of thoracic diseases; not surprisingly, these models performed better on CXRs of male patients. Biases and underrepresentation in datasets was one of several topics covered at this year's Conference on AI, Ethics, and Society, organized by the Association for the Advancement of Artificial Intelligence (AAAI) and the Association for Computing Machinery (ACM). Because AI models can reflect biases in the datasets used to develop them, detecting the presence of biases and addressing them is an important task.