Friction in AI-Assisted Clinical Decision-Making: A Case Study on The Role of Questions and 'What-if' Scenarios

A major challenge in AI-supported decision-making is achieving appropriate reliance on AI. To reduce overreliance on AI, approaches for cognitive friction have been proposed that promote reflection and critical thinking. In this paper, we investigate how two such approaches are perceived by medical experts in a real-world decision task. We replicated an existing decision support system used in clinical practice and extended it with outputting reflective questions and what-if scenarios. We gathered clinicians' (n=7) feedback on this prototype through in-situ interviews. While the questions were perceived as unhelpful for reflective thinking in itself, their potential was seen in serving as reminders to consider relevant information, e.g., when attention wanes. Inspecting what-if scenarios was found useful for considering alternative outcomes and informing discussions with patients. Our participants regarded the prototype as a promising training tool for novice clinicians. From the clinicians' feedback, we make recommendations for designing friction in human-AI interactions.