Bridging AI and Clinical Reasoning: Abductive Explanations for Alignment on Critical Symptoms

• URL: http://arxiv.org/abs/2602.13985v1
• Date: Sun, 15 Feb 2026 04:27:59 GMT
• Title: Bridging AI and Clinical Reasoning: Abductive Explanations for Alignment on Critical Symptoms
• Authors: Belona Sonna, Alban Grastien,
• Abstract summary: Key challenge is that AI reasoning diverges from structured clinical frameworks.<n>We leverage formal abductive explanations, which offer consistent, guaranteed reasoning.<n>This enables a clear understanding of AI decision-making and allows alignment with clinical reasoning.
• Score: 5.220940151628734
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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