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Top-performing models in automated pathology rely on structured representations and hierarchical diagnostics, not just vision-language models alone.
Aligning AI-generated reports with human pathologist attention boosts accuracy and interpretability, revealing the critical regions that inform cancer diagnoses.
Conventional language metrics overestimate report quality, while the new Clinical Report Quality Score uncovers clinically significant differences in pathology report generation.
