Radiology Report Generation

Radiology report generation aims to automate the creation of radiology reports from medical images, reducing radiologist workload and improving efficiency. Current research heavily utilizes large language models (LLMs) and vision-language models (VLMs), often incorporating techniques like contrastive learning, knowledge graphs, and fine-grained image-text alignment to enhance report accuracy and clinical relevance. A key focus is on improving the evaluation of generated reports, moving beyond simple lexical similarity metrics towards more clinically meaningful assessments that capture factual accuracy and semantic coherence. This field holds significant potential for improving healthcare delivery by streamlining diagnostic workflows and potentially reducing diagnostic errors.