Chest Radiograph

Chest radiographs (CXRs) are fundamental in diagnosing thoracic diseases, and research focuses on improving their analysis through artificial intelligence. Current efforts utilize large language models (LLMs) and vision transformers (ViTs), often incorporating multimodal learning to integrate image data with clinical reports and patient history, aiming for improved diagnostic accuracy and explainability. These advancements, including the development of novel architectures and training strategies like instruction tuning and contrastive learning, hold significant promise for assisting radiologists, enhancing diagnostic consistency, and potentially improving patient outcomes.