CAST让临床模型决策可追溯，能区分真实症状和记录模板，医疗AI透明度提升。
研究团队提出CAST框架，使用稀疏自编码器从Transformer激活中提取可审计特征。该框架通过LLM辅助解释流程和ICD-10检索约束标记潜在特征，在微调过程中通过残差减法抑制验证过的伪影特征。在MIMIC-IV出院记录死亡率预测任务上，CAST表现优于对应微调编码器基线，并与强LLM基线保持竞争力。
Making Clinical Language Models Auditable: Concept-Guided Fine-Tuning for Robust Prediction
Clinical language models can achieve strong in-hospital accuracy yet fail under deployment shifts because they exploit note-specific artifacts (e.g., templates, separators, boilerplate) that do not reflect patient state. We propose CAST (Concept-guided Artifact Suppression Tuning), an SAE-based framework for auditable clinical text classification. CAST uses Sparse Autoencoders to expose sparse, human-auditable features from intermediate Transformer activations, labels SAE latents with an LLM-assisted interpretation pipeline and ICD-10 retrieval constraints, suppresses verified artifact latents via residual subtraction during fine-tuning, and provides post-hoc per-concept attributions for auditing model decisions. On MIMIC-IV discharge-note mortality prediction, CAST improves over its corresponding fine-tuned encoder baselines and remains competitive with strong LLM baselines, while producing a feature-level audit trail of the clinical concepts that support each prediction and the artifact concepts suppressed during training.