ASTAR: Automated Induction of Standardized Radiology Reporting Templates from Large-Scale Clinical Free-Text Corpora

A framework for extraction of clinical information from radiological mammography reports using large language models and retrieval augmented generation

Extracting structured information from free-text radiology reports is essential for downstream clinical analysis and decision support. In mammography, this challenge is amplified by heterogeneous writing styles, the absence of standardized terminology across institutions, and limited availability of annotated Spanish d...

Exploratory Implementation and Feasibility Report of CLASS (Clinical LLM Abstraction & Structuring System), A Large Language Model Pipeline for Extracting Unstructured Data From Clinical Notes

Practical Guide to Large Language Models for Information Extraction in Behavioral Health Notes: Tutorial

Abstract Background Mental health clinical notes contain decision-critical information often absent from structured electronic health record fields. Large language models (LLMs) can extract clinically relevant signals from narrative text; however, variability in output format, limited reproducibility, and inconsistent...

SCAN-R: bridging precision imaging and natural language for automated stroke diagnosis

This work presents Stroke CT Analysis and Natural Language Reporting (SCAN-R), a unified end-to-end framework that integrates multiclass stroke detection, Transformerenhanced U-Net segmentation with task-specific pre-trained backbones, and Retrieval-Augmented Generation for evidence-based clinical report generation.

SIFTING: A Novel LLM-Based Framework for Structured and Transparent Information Extraction from Clinical Free-Text Reports, with Application to Tumor Staging in Lung Cancer

Background: Large language models (LLMs) show promise for extracting information from clinical free-text documents, but their outputs are often unstructured and lack traceability, complicating validation and adoption in clinical workflows. In this work we introduce SIFTING, an LLM-based framework designed to address th...

An end-to-end-trained vision-language model for native-language prostate pathology report generation

This work presents a slide-level framework generating prostate biopsy reports that is language-independent by construction: tokenizer and model are trained from scratch, and institutions can thus train native-language reporting models on their own archives.