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This paper investigates LLM-based metrics for evaluating clinical significance in radiology report generation. It identifies discrimination bias in existing LLM evaluators and proposes training lightweight interpretable metrics to improve the balance between error detection and tolerance of harmless variations.
This paper evaluates the open-weight LLM LLaMA 3.1 for automatic extraction of structured data from Dutch brain MRI reports, achieving high performance on visual rating scores and accurate detection of findings, with few-shot prompting improving extraction of numerical variables.