arXiv:2606.17062v1 Announce Type: cross Abstract: Radiology report evaluation must distinguish clinical compatibility from surface similarity, because negation, laterality, or normal-abnormal polarity can reverse a finding. We propose RadSEM (Radiology Sentence-Level Evaluation Metric), a constrained LLM-assisted metric for reference-based evaluation of radiology Findings. RadSEM rewrites reference and generated reports into ordered atomic finding sentences, each expressing one site-finding proposition. It then performs contradiction-constrained many-to-many matching: incompatible pairs such a
The proliferation of Large Language Models (LLMs) and the increasing need for reliable, automated clinical evaluation systems in healthcare make this development timely.
This metric addresses the critical challenge of evaluating LLM output in clinical radiology, moving beyond surface-level similarity to ensure clinical consistency, which is vital for adoption and safety.
The ability to accurately assess the clinical compatibility of AI-generated radiology reports using 'finding-by-finding' evaluation will significantly improve the development and deployment of diagnostic AI.
- · AI developers in healthcare
- · Radiology departments
- · Patients
- · Medical technology sector
- · Developers of less precise AI evaluation metrics
- · Healthcare systems slow to adopt AI-powered diagnostic tools
Improved reliability and broader adoption of AI for drafting and evaluating clinical radiology reports.
Reduced physician workload and faster, more consistent diagnostic turnarounds in radiology.
Enhanced overall accuracy of medical diagnostics through AI assistance, potentially leading to earlier disease detection and better patient outcomes.
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Read at arXiv cs.LG