Bridging AI and Clinical Reasoning: Abductive Explanations for Alignment on Critical Symptoms
This addresses trust and adoption issues in medical AI for clinicians, though it appears incremental as it builds on existing explanation methods.
The paper tackles the problem of AI reasoning diverging from clinical frameworks in diagnostics, which limits trust and interpretability, by using formal abductive explanations to align AI with clinical reasoning while preserving predictive accuracy.
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.