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This research introduces CAST, a framework using Sparse Autoencoders to make clinical language models auditable by explicitly suppressing non-patient artifacts like template boilerplate during fine-tuning. By providing feature-level audit trails that distinguish between genuine clinical concepts and spurious patterns, the method improves mortality prediction robustness while maintaining competitiveness with strong LLM baselines. This approach directly addresses the critical need for explainability and reliability in high-stakes healthcare AI deployments.
Making Clinical Language Models Auditable: Concept-Guided Fine-Tuning for Robust Prediction
arXiv:2608.27397v1 Announce Type: cross Abstract: 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.
Abstract: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.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2608.27397 [cs.CL]
(or arXiv:2608.27397v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2608.27397 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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