让AI与医生动态协作，实时调整诊断假设。
Supporting Data-Frame Dynamics in AI-assisted Decision Making
- 基于认知框架理论，实现人机协同构建和更新假设。
- 原型系统支持皮肤癌诊断中假设的持续动态更新。
- 通过概念瓶颈模型提升可解释性，适合高风险医疗决策。
高风险决策常需不断整合新证据并调整假设，而现有AI决策支持系统难以有效支持这种动态过程。本文提出一种混合主动性框架，基于数据框架理论与评估型AI范式，使人类与AI能共同构建、验证并动态调整假设。我们以皮肤癌诊断原型系统为例，采用概念瓶颈模型实现可解释的人机交互，并支持诊断假设的实时更新，从而增强决策过程的适应性与可信度。
原文摘要 · Abstract (English)
High stakes decision-making often requires a continuous interplay between evolving evidence and shifting hypotheses, a dynamic that is not well supported by current AI decision support systems. In this paper, we introduce a mixed-initiative framework for AI assisted decision making that is grounded in the data-frame theory of sensemaking and the evaluative AI paradigm. Our approach enables both humans and AI to collaboratively construct, validate, and adapt hypotheses. We demonstrate our framework with an AI-assisted skin cancer diagnosis prototype that leverages a concept bottleneck model to facilitate interpretable interactions and dynamic updates to diagnostic hypotheses.
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