Beyond Fluency: A Clinical Benchmark and Anomaly-Enhanced Baseline for Spine MRI Report Generation

TL;DR - An arXiv benchmark of state-of-the-art vision-language models on lumbar spine MRI report generation shows that standard lexical/semantic metrics reward fluent-sounding reports that contain real diagnostic errors, and proposes anomaly heatmaps as a fix. It matters because it exposes a fundamental evaluation gap blocking clinical deployment of automated radiology reporting.

• Benchmarks current VLMs on lumbar spine MRI reporting with an explicit focus on diagnostic accuracy rather than text quality; finds fluent, well-structured reports can score highly while being clinically wrong.
• Proposes an architecture-agnostic framework that augments VLM inputs with spatially localized, disc-level anomaly heatmaps, so the method can be layered onto existing models.
• Heatmaps are produced by a semi-supervised U-Net++ model, reducing dependence on fully labeled data.
• The heatmaps serve dual purposes: improving anatomical sensitivity via explicit visual grounding, and providing an independent interpretability signal for clinician oversight.
• Note: the provided abstract states no quantitative results, so the magnitude of improvement is unspecified here.