The study presents a locally deployed multi‑agent AI pipeline that structures radiology reports into standardized anatomical sections and performs quality assurance by detecting mismatches, gender‑anatomy conflicts, and missing critical findings. In a retrospective analysis of 638 CT reports, the system successfully restructured all Findings sections and flagged 14.1% of reports for potential issues, with radiologist reviewers rating 84% of evaluated reports as excellent or good. The evaluation confirmed no clinically important information was omitted or fabricated, supporting the system’s effectiveness in standardizing and assuring report quality.
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...
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.
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...
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.
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.
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.
MedPAO-Assist is an edge‑deployable, agentic AI framework that structures radiology reports from radiologist dictation and voice commands using only about 3.3 GB of RAM and an average latency of 31 seconds on a standard laptop CPU. It supports four imaging modalities—Chest X‑ray, Chest CT, Abdomen CT, and Brain MRI—and operates entirely with a 0.6 B‑parameter small language model, avoiding cloud APIs or high‑end GPUs. The system follows an Extract‑Ground‑Generate pipeline, performing concept extraction, SNOMED‑CT grounding, structured report generation, and a speech‑driven human‑in‑the‑loop correction phase, enabling a fully “no‑touch” reporting workflow that requires no keyboard input or internet connectivity.
arXiv:2604.09537v2 Announce Type: replace Abstract: A grounding verifier checks a statement about a patient's image against the radiology report and the cited evidence. The state-of-the-art methods f...
arXiv:2610.34599v1 Announce Type: new Abstract: Despite advances in automated liver lesion segmentation, a gap remains between image-derived predictions and clinically interpretable radiology reports...
The study examines how differences in radiologists’ reporting styles—such as terminology, shorthand, formatting, and detail—affect the evaluation of AI-generated chest X‑ray reports. By quantifying the sensitivity of common metrics to these variations, the authors show that changes in reference reports can shift model rankings. They introduce a taxonomy of reporting variations and a rewriting method, ReRef, that preserves clinical meaning while altering style, and release a validated dataset of paired reference reports to aid future research.
MedPAO‑VL is a multimodal agent that generates structured radiology reports from chest X‑rays using an explainable plan‑act‑observe loop. The system first interprets the image with a vision module that captures diagnostic uncertainty, then iteratively refines the report by correcting semantic discrepancies to better match visual findings. Evaluation shows improved clinical correctness and structural compliance, with expert ratings of 3.5/5 for accuracy and 4.3/5 for structure.
Purpose: To evaluate whether large language model (LLM)-assisted label cleaning can identify label-report discordance in CT-RATE, a large-scale public chest CT dataset. Materials and Methods: After report-level deduplication, 24,446 unique radiology reports were identified.