CXR Report

Automated generation of chest X-ray (CXR) reports aims to alleviate radiologist workload and improve patient care by automatically producing diagnostic reports from CXR images. Current research focuses on developing sophisticated multimodal large language models (MLLMs) and transformer-based diffusion models, often incorporating techniques like warm-starting pre-trained models and reinforcement learning with semantic similarity rewards to enhance report accuracy and alignment with radiologist reports. These advancements leverage both image and text processing capabilities to improve the quality and efficiency of CXR interpretation, potentially leading to significant improvements in diagnostic accuracy and workflow efficiency within radiology.