Multidisciplinary tumor boards (MTBs) bring specialists together to identify therapies for complex cancer cases, but preparing for them is time-intensive. Clinicians must extract key details from extensive records and evaluate treatment options. While large language models (LLMs) show promise in medicine for basic tasks like summarizing notes, little is known about their role in high-stakes tasks like MTB preparation. We conducted a mixed-methods study with 16 oncologists using two AI systems to prepare patient cases for MTB: an off-the-shelf assistant (Copilot) and a task-specific multi-agent system (Healthcare Agent Orchestrator, HAO). We analyzed oncologist prompts, AI responses, and oncologists’ perception of AI. Participants showed greater willingness to adopt HAO but were often overconfident in AI summaries and skeptical of AI-recommended therapies. Trust calibration strategies, such as source links and agent-trajectories, failed to align trust with system capabilities. We conclude with how AI systems should be built to support clinicians in high-stakes tasks.

Large Language Models (LLMs) have shown impressive abilities in many applications.When a concrete and precise answer is desired, it is important to have a quantitative estimation of the potential error rate.However, this can be challenging due to the text-in-text-out nature of generative models.We present a method based on Pareto optimization that generates a risk score to estimate the probability of error in an LLM response by integrating multiple sources of information.We prove theoretically that the error estimator optimized in our framework aligns with the LLM and the information sources in an Pareto optimal manner.Experimental results show that the risk scores estimated by our method are well correlated with the true LLM error rate, thus facilitating error correction.By dynamically combining with prompting strategies such as self-verification and information retrieval, we demonstrate the proposed method can be utilized to increase the performance of an LLM, surpassing state-ofthe-art task specific models.

We present a novel method for image registration via a piecewise diffeomorphic deformation which accommodates sliding motion, such as that encountered at organ boundaries. Our method jointly computes the deformation as well as a coherent sliding boundary, represented by a segmentation of the domain into regions of smooth motion. Discontinuities are allowed only at the boundaries of these regions, while invertibility of the total deformation is enforced by disallowing separation or overlap between regions. Optimization alternates between discrete segmentation estimation and continuous deformation estimation. We demonstrate our method on chest 4DCT data showing sliding motion of the lungs against the thoracic cage during breathing.