arXiv Computer Vision
RadMatch: Auditable Radiology Report Evaluation via Finding-Level Matching
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RadOT-Eval: Auditable Structured-Evidence Transport for Radiology Report Evaluation
arXiv:2606. 08769v1 Announce Type: cross Abstract: Automatic evaluation is critical for high-stakes text generation, where errors often involve omitted findings, hallucinated content, polarity reversals, location changes, uncertainty mismatches, and temporal-comparison errors rather than low surface similarity alone.
arXiv Machine Learning
Jun 17
RadSEM: A Finding-by-Finding Metric for Clinical Consistency in Radiology Reports
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.
arXiv AI
Jul 8
Harrison.Rad 1.5 Technical Report: A radiology foundation model that can draft reports from images, priors and clinical context
arXiv:2607. 05880v1 Announce Type: cross Abstract: Imaging demand is growing faster than the radiology workforce can expand, and reporting backlogs cannot be resolved through training and recruitment alone.
arXiv AI
Aug 12
RadFusion: Towards Threshold-Controllable Radiology Report Generation
arXiv:2608. 10505v1 Announce Type: new Abstract: Automated radiology report generation is advancing rapidly in response to the shortage of radiologists, yet unlike a perception model, existing generation models offer no control over the sensitivity-specificity trade-off of their diagnostic content.
arXiv Machine Learning
Aug 4
RadPRISM: Schema-stratified radiology-report supervision for concept-disentangled image representations and visual grounding
arXiv:2608. 00147v1 Announce Type: cross Abstract: Vision-language pretraining learns rich medical image representations from radiology reports, but previous model variants commonly operate within a single shared embedding space, so concept-level structure and interpretability must be recovered post hoc, limiting model transparency and, hence, clinical utility.
arXiv AI
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Error Detection for PET/CT Radiology Reports: Domain-Specific vs Large Language Models
arXiv:2608.30021v1 Announce Type: cross Abstract: Errors in radiology reports can adversely affect patient treatment, yet automated report quality assurance remains challenging because errors are oft...