#clinical-nlp

3 curated events

papersTODAY 04:00 UTC

GAVEL: LLM Judge Protocol for Comparing Extracted Clinical Timelines Against Case Reports

Researchers introduce GAVEL, a protocol that uses a large language model as a judge to compare two clinical timelines extracted from case reports, rather than relying on a single expert reference annotation. The approach aims to address limitations in existing extraction pipelines, where evaluation is constrained by imperfect reference labels and imprecise event alignment. The work is described in a new arXiv preprint.

llm-as-judge/clinical-nlp/evaluation/arxiv

papersTODAY 04:00 UTC

Calibrated Uncertainty Estimation for LLM Clinical Text Classification

A new arXiv paper addresses the risk of overconfident errors when large language models classify clinical text, where a wrong label can affect patient care. The authors note that current black-box approaches simply attach a confidence score to an unchanged LLM prediction, and they propose an uncertainty-aware method designed to produce better-calibrated results. The work targets medical NLP settings where knowing when a model is unreliable matters as much as the predicted label itself.

clinical-nlp/uncertainty/calibration/llm

papersSEP 12 04:00 UTC

Cross-Lingual Clinical Annotation Projection Framed as Constrained Text Generation

A new arXiv paper examines whether clinical annotation projection between languages can be treated as a document-level generative task that keeps the original text intact. The work spans six languages and aims to output character-level annotations that can be verified automatically. The stated goal is to support the construction of multilingual clinical corpora.

clinical-nlp/cross-lingual/annotation/multilingual