用对话+可视化解释糖尿病风险预测，让医生信得过AI
Visual-Conversational Interface for Evidence-Based Explanation of Diabetes Risk Prediction
- 结合对话系统与交互图表，动态解释风险评估
- 30名医生测试显示，90%认为系统提升了对模型判断的理解
- 关键特征分析+科学证据引用，增强临床可信度
医疗专业人员需要有效的方法来使用、理解并验证基于AI的临床决策支持系统。现有系统存在两大局限：可视化复杂，且缺乏科学证据支撑。本文提出一个集成系统，结合交互式可视化与对话代理，解释糖尿病风险评估。采用混合提示处理策略：微调语言模型应对分析性问题，通用大模型回答广义医学问题；提出一种将AI解释锚定在科学证据上的方法，并引入特征范围分析技术，以深化对特征贡献的理解。通过包含30名医疗专业人士的混合方法研究发现，对话交互帮助医生清晰理解模型评估，科学证据的整合则增强了对系统决策的信任。大多数参与者表示该系统有助于患者风险评估与建议生成。
原文摘要 · Abstract (English)
Healthcare professionals need effective ways to use, understand, and validate AI-driven clinical decision support systems. Existing systems face two key limitations: complex visualizations and a lack of grounding in scientific evidence. We present an integrated decision support system that combines interactive visualizations with a conversational agent to explain diabetes risk assessments. We propose a hybrid prompt handling approach combining fine-tuned language models for analytical queries with general Large Language Models (LLMs) for broader medical questions, a methodology for grounding AI explanations in scientific evidence, and a feature range analysis technique to support deeper understanding of feature contributions. We conducted a mixed-methods study with 30 healthcare professionals and found that the conversational interactions helped healthcare professionals build a clear understanding of model assessments, while the integration of scientific evidence calibrated trust in the system's decisions. Most participants reported that the system supported both patient risk evaluation and recommendation.
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