Radiology Report
Radiology reports, crucial for medical diagnosis and treatment, are increasingly being analyzed and generated using artificial intelligence. Current research focuses on improving the accuracy and efficiency of automated report generation and extraction of structured data from unstructured reports, employing large language models (LLMs) like LLAMA and GPT, often augmented with retrieval-augmented generation (RAG) and other techniques like contrastive learning. This work aims to reduce radiologist workload, improve diagnostic accuracy, and facilitate data analysis for research and clinical decision support, with a strong emphasis on ensuring data privacy and clinical validity. The development of new evaluation metrics specifically tailored for radiology reports is also a key area of ongoing investigation.
Papers
SERPENT-VLM : Self-Refining Radiology Report Generation Using Vision Language Models
Manav Nitin Kapadnis, Sohan Patnaik, Abhilash Nandy, Sourjyadip Ray, Pawan Goyal, Debdoot Sheet
MRScore: Evaluating Radiology Report Generation with LLM-based Reward System
Yunyi Liu, Zhanyu Wang, Yingshu Li, Xinyu Liang, Lingqiao Liu, Lei Wang, Luping Zhou
A Novel Corpus of Annotated Medical Imaging Reports and Information Extraction Results Using BERT-based Language Models
Namu Park, Kevin Lybarger, Giridhar Kaushik Ramachandran, Spencer Lewis, Aashka Damani, Ozlem Uzuner, Martin Gunn, Meliha Yetisgen
Reshaping Free-Text Radiology Notes Into Structured Reports With Generative Transformers
Laura Bergomi, Tommaso M. Buonocore, Paolo Antonazzo, Lorenzo Alberghi, Riccardo Bellazzi, Lorenzo Preda, Chandra Bortolotto, Enea Parimbelli