 With advances in machine learning and artificial intelligence, a new role is emerging for machines as intelligent assistants to radiologists in their clinical workflows. But what systematic clinical thought process are these machines using? Are they similar enough to those of radiologists to be trusted as assistants? In this live demonstration, clinicians can select a case from various sub-specialties, start to make a diagnosis and see how a work in progress Watson technology attempts to assist the same case. Watson uses sophisticated medical imaging, deep learning, and clinical inference technologies to analyze patient cases using a systematic clinical thought process. To experience the eyes of Watson, you can select a case for analysis. After verifying your credentials, you will be presented with the case. You can examine the imaging study and read the associated case description. Select the appropriate conclusion. To see how Watson would attempt this case in real time, select Ask Watson. Watson examines the case description first, analyzes text, and highlights the relevant clinical concepts found. It summarizes the findings and updates the evolving clinical inference. First Watson analyzes the 3D CT study to first locate relevant anatomical structures visible such as the heart, the pulmonary artery, and the aorta. It then looks systematically at each anatomical structure, such as the aorta here, and observes an anomaly of dissection. The detected dissection is placed in the context of the 3D CT study, and the region of span of the dissection is noted. Further, the type of dissection can also be inferred. Watson then takes the clinical concepts derived from the imaging exam and case descriptions, and begins its reasoning process using clinical knowledge. Paths are explored and scored in the knowledge graph while searching for related concepts and facts that lead to the specified conclusions starting from the chosen clinical concepts. The retained conclusion is the one which has overwhelming evidence from the number of high-scoring paths that lead up to it. The final clinical inference can be seen on the right. If you concur with Watson's inference, you can change your conclusion if you like. You can continue with another case or take our evaluation survey. Eyes of Watson is a joint effort by RSNA and IBM Research to show how machines of the future may be able to assist radiologists not only in their training but also by reducing the time to diagnose and increasing efficiency in their clinical workflows.