TL;DR
The NLP-assisted system can identify potential reporting errors and provide real-time quality control support for radiology reports and its implementation may help improve reporting consistency and support workflow efficiency.
Enhanced LLMs, particularly DeepSeek-R1, demonstrated robust performance in error detection and correction within real-world Chinese radiology reports, supporting their clinical use for automated quality assurance and integration into workflows to improve reporting accuracy and efficiency.
Structured prompting improves detection of interpretation and section-consistency errors across seven models and consistently enhances LLM-based error detection, closing the gap between open-source and commercial models and offering a pathway for privacy-preserving, on-premises QA.
It is suggested that domain-specific training matters more than model scale for PET/CT report error detection, supporting compact models as an accurate and computationally efficient approach to automated radiology report quality assurance.
The addition of clinical information was associated with a numeric trend toward higher diagnostic accuracy overall, but this trend was heterogeneous across models and disease types, and no statistically significant improvement was demonstrated after adjustment for multiple comparisons.
Purpose: To develop and evaluate an open-weight large language model (LLM) pipeline that converts an entire archive of free-text radiology reports into structured reports without human oversight. Materials and Methods: In this retrospective study, a pipeline with 150 hierarchically organized templates was developed at...
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 CT examinations of the chest, abdomen, and pelvis dictated by 15 board-certified radiologists in 2023 and...
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