Robot-assisted surgery (RAS) has raised concerns within human-computer interaction, particularly regarding the socio-material configurations of robotic systems and their impact on surgical practices. To investigate these configurations in the context of the da Vinci robotic system, we used a research-through-design approach. Through this approach, we developed six Visual RAS (ViRAS) scenarios derived from RAS observations. These scenarios represent different configurations of interacting surgical team members and the robotic system in distinct adverse RAS events. In ViRAS-guided interviews with experienced RAS surgeons, we found that ViRAS scenarios help reflect on surgical practices and the specific material and spatial properties of RAS. Our findings indicated that the team’s cognitive engagement during surgery could be improved by providing sensory augmentation to facilitate task perception and individual skill development. Through our research, we show how ViRAS scenarios, as a tool for reflection, can reveal opportunities for designing socio-material configurations in RAS and beyond.

Epidemiological population-based studies collect hundreds of socio-demographic, lifestyle-related, and health related variables for thousands of individuals in order to better characterize health and disease in a defined population. To understand the relations between the variables in the study data, we employ Bayesian Networks, as they not only represent associations between variables but also assign probabilities to these associations. The probabilistic associations allow us to draw inference for unknown events in question, based on the provided evidence. In our work, we induce a Bayesian Network from the data of the population-based epidemiological Study of Health in Pomerania (SHIP), to identify variables related to the outcome "fatty liver". We report on Bayesian Network structure learning, identification of variables associated with the outcome, and the strong associations identified among the variables.

Abstract It is generally accepted practice that each cancer patient case should be discussed in a clinical expert meeting, the so‐called tumor board. A central role in finding the best therapy options for patients with solid tumors plays the Tumor, lymph Node, and Metastasis staging (TNM staging). Correctness of TNM staging has a significant impact on the therapy choice and hence on the patient's post‐therapeutic quality of life or even survival. If inconsistencies in the TNM staging occur, possible explanations and solutions must be found based on the complex patient records, which takes the costly time of (multiple) physicians. We propose a more efficient visual analysis component, which supports a physician in verifying the given TNM staging before forwarding it to the tumor board. Our component comprises a Bayesian network model of the TNM staging process. Using information from the patient records and Bayesian inference, the models computes a patient‐specific TNM staging, which is then explored and compared to the given staging by means of a graph‐based visualization. Our component is implemented in a research prototype that supports an understanding of the model computations, allows for a fast identification of important influencing factors, and facilitates a quick detection of differences between two TNM stagings. We evaluated our component with five physicians, each studying 20 cases of laryngeal cancer.