Standardizing Longitudinal Radiology Report Evaluation via Large Language Model Annotation
Quick summary
arXiv:2601.16753v2 Announce Type: replace-cross Abstract: Longitudinal information in radiology reports refers to the sequential tracking of findings across multiple examinations over time, which is crucial for monitoring disease progression and guiding clinical decisions. Many recent automated radiology report generation methods are designed to capture longitudinal information; however, validating their performance is challenging. There is no proper tool to consistently label temporal changes in both ground-truth and model-generated texts for meaningful comparisons. Large language models (LLM
Key takeaways
- arXiv:2601.16753v2 Announce Type: replace-cross Abstract: Longitudinal information in radiology reports refers to the sequential tracking of findings across multiple examinations over time, which is crucial for monitoring disease progression and guiding clinical decisions.
- Many recent automated radiology report generation methods are designed to capture longitudinal information; however, validating their performance is challenging.
- There is no proper tool to consistently label temporal changes in both ground-truth and model-generated texts for meaningful comparisons.
Why it matters
“Standardizing Longitudinal Radiology Report Evaluation via Large Language Model Annotation” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.
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