{
  "id": 3875510,
  "title": "Making Clinical Language Models Auditable: Concept-Guided Fine-Tuning for Robust Prediction",
  "url": "https://urgent.news/2026/08/27/making-clinical-language-models-auditable-concept-guided-fine-tuning",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-27T17:28:35.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2608.27397v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "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,…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}