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Autonomous surgical robots are a promising solution to the increasing demand for surgery amid a shortage of surgeons. Recent work has proposed learning-based approaches for the autonomous manipulation of soft tissue. However, due to variability in tissue geometries and stiffnesses, these methods do not always perform optimally, especially in out-of-distribution settings. We propose, develop, and test the first application of uncertainty quantification to learned surgical soft-tissue manipulation policies as an early identification system for task failures. We analyze two different methods of uncertainty quantification, deep ensembles and Monte Carlo dropout, and find that deep ensembles provide a stronger signal of future task success or failure. We validate our approach using the physical daVinci Research Kit (dVRK) surgical robot to perform physical soft-tissue manipulation. We show that we are able to successfully detect out-of-distribution states leading to task failure and request human intervention when necessary while still enabling autonomous manipulation when possible. Our learned tissue manipulation policy with uncertainty-based early failure detection achieves a zero-shot sim2real performance improvement of 47.5% over the prior state of the art in learned soft-tissue manipulation. We also show that our method generalizes well to new types of tissue as well as to a bimanual soft-tissue manipulation task. 

Medical financial literacy is essential to make smart decisions in healthcare settings and prevent unanticipated financial hardships. Existing literature has shown that young adults often struggle to understand information associated with health insurance and the financial planning necessary for health-related costs. AI-driven chatbots are emerging as educational tools that have the potential to address this issue. This exploratory study examined an AI chatbot aimed at enhancing medical financial literacy among high school students. Participants engaged with the chatbot’s responses to medical financial questions while also rating the clarity, ease of use, trustworthiness, and educational value of the chatbot engagement. Our experiment results supported that the chatbot increased students’ understanding of the financial aspect of healthcare - 76.9 percent of students reported a high degree of understanding, 80.8 percent rated the chatbot’s responses as clear, and 73.1 percent reported they would recommend it to a peer. The responses indicated that students found the chatbot helpful, but suggested that interactive features be added and/or real-world finance features be incorporated into the chatbot.