用逻辑推理让AI看病更像医生，关键症状不遗漏。
Bridging AI and Clinical Reasoning: Abductive Explanations for Alignment on Critical Symptoms
- 用形式化反演推理，找出诊断必需的最小特征集
- 在保持准确率的同时，揭示临床决策关键依据
- 适合医疗AI可信性研究与临床系统开发人员
人工智能在临床诊断中展现出与人类专家相当甚至更高的准确性，但其推理过程常偏离结构化临床框架，影响信任度、可解释性与实际应用。关键症状虽对快速精准决策至关重要，却可能被AI模型忽略，即便预测结果正确。现有事后解释方法透明度有限，且缺乏形式化保证。为此，本文采用形式化反演解释，提供对最小充分特征集的一致且有保障的推理，使AI决策过程清晰可理解，并实现与临床推理的对齐。该方法在保持预测准确性的同时，提供临床可操作的洞察，构建了可信医疗AI诊断的坚实框架。
原文摘要 · Abstract (English)
Artificial intelligence (AI) has demonstrated strong potential in clinical diagnostics, often achieving accuracy comparable to or exceeding that of human experts. A key challenge, however, is that AI reasoning frequently diverges from structured clinical frameworks, limiting trust, interpretability, and adoption. Critical symptoms, pivotal for rapid and accurate decision-making, may be overlooked by AI models even when predictions are correct. Existing post hoc explanation methods provide limited transparency and lack formal guarantees. To address this, we leverage formal abductive explanations, which offer consistent, guaranteed reasoning over minimal sufficient feature sets. This enables a clear understanding of AI decision-making and allows alignment with clinical reasoning. Our approach preserves predictive accuracy while providing clinically actionable insights, establishing a robust framework for trustworthy AI in medical diagnosis.
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