用可信风险控制生成精准医疗决策指引，提升医生效率。
Hybrid Decision Making via Conformal VLM-generated Guidance
- 基于置信区间控制选择关键诊断结果，减少信息过载
- 在真实多标签医疗诊断任务中显著降低误漏率
- 适合需要精准辅助的临床决策场景
基于人工智能最新进展，人机协同决策（HDM）有望提升人类决策质量并减轻认知负担。本文研究学习引导（LtG）框架下的新型人机协作模式，其中人类始终对最终决策负责：AI不直接提建议，而是提供有助于决策的文字性指引。现有方法的指引常包含所有可能结果的信息，导致难以理解。为此，我们提出ConfGuide，一种新的LtG方法，利用置信区域控制技术筛选出一组关键结果，确保假阴性率可控。我们在一个真实的多标签医学诊断任务上验证了该方法，实证结果表明ConfGuide能有效生成更简洁、针对性更强的指引，具备显著应用潜力。
原文摘要 · Abstract (English)
Building on recent advances in AI, hybrid decision making (HDM) holds the promise of improving human decision quality and reducing cognitive load. We work in the context of learning to guide (LtG), a recently proposed HDM framework in which the human is always responsible for the final decision: rather than suggesting decisions, in LtG the AI supplies (textual) guidance useful for facilitating decision making. One limiting factor of existing approaches is that their guidance compounds information about all possible outcomes, and as a result it can be difficult to digest. We address this issue by introducing ConfGuide, a novel LtG approach that generates more succinct and targeted guidance. To this end, it employs conformal risk control to select a set of outcomes, ensuring a cap on the false negative rate. We demonstrate our approach on a real-world multi-label medical diagnosis task. Our empirical evaluation highlights the promise of ConfGuide.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。