A Reasoning Pathway Explanation Framework for Clinical AI: Methods and Evaluation

Objective : When AI predicts acute myocardial infarction, existing explanation methods identify which features mattered (e.g., elevated troponin) but not how risk factors lead to the diagnosis through biological mechanisms. We developed a framework that generates reasoning pathways—clinically grounded chains linking risk factors, pathophysiology, and evidence—to address this gap. Methods : Using a 34-node cardiology reasoning graph, MIMIC-III data, and medical ontologies (SNOMED CT, UMLS), we built an explanation engine that maps AI predictions to temporally ordered, evidence-linked pathways. We evaluated 100 AMI cases (87 expanded, 13 independent) with six structural metrics, adversarial validation, and comparison with BioBERT and SHAP. Three physicians independently rated 11 cases across five clinical quality dimensions. Results : The framework generated consistent reasoning pathways across all cases (pathwayprediction consistency 0.85 ± 0.01). Adversarial validation confirmed discriminative power for this metric (AUC-ROC 0.81) but not others. In the physician pilot, inter-rater agreement was strong (ICC = 0.83) and all three evaluators detected the deliberately flawed control case (2.13/5 vs. 3.92/5 for genuine cases). Evidence sufficiency for complex independent cases was the primary concern identified. Conclusion : Structured reasoning pathways that trace clinical logic from risk factors to diagnosis can be generated and evaluated systematically. Physicians agreed on pathway quality (ICC = 0.83) and consistently rejected flawed explanations, though evidence depth for complex cases requires improvement.