让超声心动图分析更可信，通过角色分工实现可解释推理
Evidence-Based Actor-Verifier Reasoning for Echocardiographic Agents
- 采用演员-验证器框架，分步生成可解释的诊断依据
- 结构化中间表示提升决策可靠性，减少错误关联
- 适合医疗AI可信推理研究者和临床辅助系统开发者
超声心动图在心血管疾病筛查与诊断中至关重要，但其自动化智能分析仍面临心脏动态复杂和视图差异大等挑战。近年来，视觉语言模型（VLM）为构建临床决策支持的超声理解系统提供了新路径。然而，现有方法多将视频与问题直接映射为答案，易受模板捷径和虚假解释影响。为此，我们提出EchoTrust——一种基于证据的演员-验证器框架，用于提升基于VLM的超声心动图智能体的可信推理能力。该框架生成结构化的中间表示，并由不同角色分别分析，从而实现高风险临床应用下的更可靠、可解释决策。
原文摘要 · Abstract (English)
Echocardiography plays an important role in the screening and diagnosis of cardiovascular diseases. However, automated intelligent analysis of echocardiographic data remains challenging due to complex cardiac dynamics and strong view heterogeneity. In recent years, visual language models (VLM) have opened a new avenue for building ultrasound understanding systems for clinical decision support. Nevertheless, most existing methods formulate this task as a direct mapping from video and question to answer, making them vulnerable to template shortcuts and spurious explanations. To address these issues, we propose EchoTrust, an evidence-driven Actor-Verifier framework for trustworthy reasoning in echocardiography VLM-based agents. EchoTrust produces a structured intermediate representation that is subsequently analyzed by distinct roles, enabling more reliable and interpretable decision-making for high-stakes clinical applications.
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