Radiology Report Generation

Radiology report generation studies how to turn images such as chest X-rays, CT scans, and MRI into clinical findings and impressions. Common approaches use vision-language models with image-text alignment, anatomical or disease-specific features, retrieval of similar cases, and structured clinical concepts; newer systems also use agents, reinforcement learning, and prior studies to guide or refine reports. The field has improved on language and clinical-label benchmarks, but fluent text can still omit findings, invent details, or miss their location and change over time. A central challenge is evaluating factual accuracy and usefulness across hospitals and modalities, since text similarity alone does not reliably measure clinical correctness.