Towards Explainable Diagnosis: A Self-learned Explanatory Knowledge Base Approach

Explainable diagnosis requires that authoritative medical knowledge provide the rationales linking a patient's clinical manifestations to the diagnostic conclusion. Although large language models (LLMs) hold great potential to facilitate explainable diagnosis, their effectiveness is often constrained by insufficient diagnostic expertise. To address this limitation, we propose Self-learned Explainable Knowledge Augmented Diagnosis (SEKAD), a unified LLM-based framework for faithful and explainable diagnosis. Our approach builds a high-quality diagnostic knowledge base through a record-driven explanation learning paradigm, as well as applies this knowledge via an explanation-based diagnostic process that ensures faithful inference. Experiments on the DiReCT and JAMA benchmarks show that SEKAD consistently outperforms strong baselines across the metrics. In particular, on the DiReCT benchmark, SEKAD improves the explanation completeness metric from 64.5% to 76.9% over the best existing methods, highlighting its effectiveness in enhancing diagnostic explainability and showing that our text mining approach produces knowledge that is both reliable in quality and large in quantity 1 . * Corresponding author. 1 We provide our code on Github: https://github.com/ Vanthoci/acl2026-sekad-explainable-diagnosis Chief complaint: frequent urination ① ; increased fatigue … Labs: 06:29PM BLOOD Glucose-267 ② UreaN-10 Creat-0.6… GADA test result is positive ③ . ① Frequent urination is a symptom of diabetes. Suspected diabetes. ② Abnormal blood glucose is a diagnostic criteria of diabetes. Diabetes. ③ GADA antibodies are positive in most patients with type I diabetes. Type 1 diabetes. Medical Record (Abbreviated) Explainable Diagnosis Observed Clinical Manifestations Pertinent to the Diagnosis Rationale for the Diagnosis Diagnosis