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.

翻译：人工智能在临床诊断中展现出巨大潜力，其准确度常达到甚至超越人类专家水平。然而，核心挑战在于人工智能的推理过程常偏离结构化临床框架，这限制了其可信度、可解释性与实际应用。对快速准确决策至关重要的关键症状，即使预测结果正确，仍可能被人工智能模型所忽略。现有的事后解释方法透明度有限，且缺乏形式化保证。为此，我们引入形式化溯因解释方法，该方法能在最小充分特征集上提供具有一致性保证的推理。这使人工智能决策过程得以清晰呈现，并能与临床推理框架实现对齐。我们的方法在保持预测准确性的同时，提供具有临床可操作性的洞见，从而为医疗诊断领域构建可信赖的人工智能建立了稳健框架。