arXiv:2609.01470v1 Announce Type: new
Abstract: As AI systems are increasingly used to draft radiology reports, reliably evaluating their clinical quality remains a critical challenge. Large language...
By Charles Corbi\`ere, L\'eo Machado, Aubin Charley, Baptiste Callard, Pierre Manceron, Corentin Dancette
The paper introduces a human-supervised, evidence-linked reference architecture for structured radiology reporting. It integrates exam-specific templates, speech-to-structure processing, measurement and segmentation capture, AI-assisted drafting, and standards-based interoperability (DICOM, HL7 FHIR, RadLex, SNOMED CT, LOINC, UCUM). The system is designed as a structured intelligence layer that supports reviewed reporting, longitudinal comparison, data reuse, and integration with PACS, RIS, EHR, analytics, and registry workflows, while addressing modality-specific deployment, safety, validation, cybersecurity, privacy, quality management, and regulatory considerations.
By Houman Kazemzadeh, Kamyar Naderi
arXiv:2609.27607v1 Announce Type: cross
Abstract: An AI-generated radiology report can resemble a physician's report while omitting an abnormality, adding an unsupported finding, or reversing its pre...
By Jiaju Huang, Hao Yang, Xinyu Ma, Xinglong Liang, Kunyan Cai, Junqiang Ma, Shaobin Chen, Yue Sun, Tao Tan
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.
By Suneeta Mall, Vladimir Nekrasov, Ashnil Kumar, Sajith Karunasena, Aiden Nibali, Alix Bird, Mateo Diaz Shine, Jarrel Seah
The paper evaluates large language model (LLM) judges for radiology report assessment across multiple imaging modalities and anatomical regions, comparing four existing metrics (RadFact, GREEN, FineRadScore, CRIMSON) with a new metric called VERT. GREEN emerges as the most robust among current metrics, while VERT improves correlation with radiologist judgments by up to 26% over GREEN. The study also shows that ensembling and lightweight fine‑tuning of the Qwen3‑30B model can yield additional performance gains and significant latency reductions.
By Federica Bologna, Jean-Philippe Corbeil, Matthew Wilkens, Asma Ben Abacha
arXiv:2607. 25589v1 Announce Type: cross Abstract: Medical-imaging AI benchmarks combine datasets, DICOM rendering, prompts, provider APIs, automated labels, statistical code, manuscripts, and repository releases.
By Mateusz Koz{\l}owski