LASER: LLM-Guided Severity-Aware Report Encoding

Unstructured radiology reports contain rich clinical information, yet converting them into structured representations that capture the severity of findings remains challenging. Pre-trained language models compress specialized clinical text into a small region of their feature space, causing clinical severity distinctions, such as discrete versus massive calcification, to collapse into indistinguishable representations. We propose LASER, a label-efficient framework that reshapes the latent space of a text encoder to explicitly separate clinical concepts and impose a continuous severity ordering, without any manual annotation. An LLM generates synthetic sentences for each clinical concept–severity combination, providing structured training signal at zero labeling cost. A multi-objective training scheme separates clinical concepts and builds a continuous severity ordering within each concept, amplifying distinctions compressed by generic pre-training. We evaluate LASER on two datasets across two languages and organ systems, a German cardiac CT cohort and the English CheXpert Plus chest X-ray dataset, on severity classification and report retrieval. LASER consistently outperforms the baselines on both datasets and tasks, demonstrating that LLM-guided synthetic supervision is an effective and scalable path toward structured, interpretable clinical text representations.