Paper: Multi-Agent AI System for Radiology Report Structuring and Quality Assurance (arXiv 2608.18072)

Forum topic · 小凯 · 2026-08-20

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A forum post introduces an arXiv paper (2608.18072) by Hartsock et al. on a locally deployed multi-agent AI system that combines radiology report structuring with quality assurance. The retrospective study analyzed 638 chest, abdomen, and pelvis CT reports dictated by 15 board-certified radiologists in 2023–2024. Using regex rules and local large language models, the system structured findings sections (22,270 sentences) into standardized anatomical sections while preserving original content, and flagged 90 reports (14.1%), most commonly Findings–Impression mismatches (80 reports, 12.5%). In a 45-report subset reviewed independently by two radiologists, both agreed 31 reports (69%) were correctly restructured, 2 (4%) incorrectly, and disagreed on 12 (27%); no clinically important information was omitted and no fabricated content introduced. Overall QA performance was rated excellent or good for 84% of evaluated reports. The authors conclude such systems can support report standardization and QA in radiology practice.

Paper Overview

Field: NLP Authors: Iryna Hartsock, Cesar Lam, Christopher Otteni, Aliya Qayyum, Robert Gatenby, Cyrillo Araujo, Ghulam Rasool Published: 2026-08-18 arXiv: 2608.18072

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Purpose: To develop and evaluate a locally deployed multi-agent AI system for radiology report structuring and quality assurance.

Materials and Methods: This retrospective study included 638 radiology reports from chest, abdomen, and pelvis CT examinations dictated by 15 board-certified radiologists in 2023–2024. A multi-agent AI pipeline was developed to perform report structuring and quality assurance (QA). The system structured reports into standardized anatomical sections at the sentence level using regex rules and local large language models. It also detected mismatches between the Findings and Impression sections (or within sections), gender-anatomy conflicts, and undocumented communication of critical findings. Two board-certified radiologists independently evaluated a 45-report subset.

Results: The multi-agent system structured the Findings sections of all reports (22,270 sentences) into a predefined anatomical format while preserving the original report content. The system flagged 90 reports (14.1%), most commonly section mismatches (80 reports, 12.5%). In the radiologist evaluation, both reviewers agreed that 31 reports (69%) were correctly restructured and 2 (4%) incorrectly restructured, with disagreement on the remaining 12 (27%). Both reviewers agreed that no clinically important information was omitted and no fabricated (hallucinated) content was introduced. Overall QA performance was rated "excellent" or "good" for 84% of evaluated reports, with the rest rated "fair."

Conclusion: The locally deployed multi-agent AI system combines radiology report structuring and quality assurance into a single workflow. The system showed favorable performance in radiologist evaluation. Such systems may support report standardization and quality assurance in radiology practice.

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Paper: Multi-Agent AI System for Radiology Report Structuring and Quality Assurance (arXiv 2608.18072)

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