弥合AI与临床推理：针对关键症状对齐的溯因解释
Bridging AI and Clinical Reasoning: Abductive Explanations for Alignment on Critical Symptoms
AI总结：
本文利用形式溯因解释，通过最小充分特征集提供一致且保证的推理，使AI决策与临床推理对齐，在保持预测准确性的同时提供可操作的临床见解，建立可信AI框架。
摘要：
人工智能在临床诊断中展现出强大潜力，其准确性常达到或超过人类专家水平。然而，一个关键挑战是AI推理常偏离结构化临床框架，限制了信任、可解释性和应用。即使预测正确，AI模型也可能忽略对快速准确决策至关重要的关键症状。现有的事后解释方法透明度有限且缺乏形式保证。为解决此问题，我们利用形式溯因解释，它在最小充分特征集上提供一致且保证的推理。这使我们能够清晰理解AI决策，并使其与临床推理对齐。我们的方法在保持预测准确性的同时提供临床可操作的见解，为医疗诊断中可信AI建立了稳健框架。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
