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LLMs evaluated for radiology report accuracy and longitudinal data extraction · 2 sources tracked

By PulseAugur Editorial · [2 sources] ·

Researchers are exploring the use of large language models (LLMs) for improving radiology report quality and extracting longitudinal information. One study compared domain-specific BERT models with open-weight LLMs like Qwen3-32B, Gemma-3:27B, and Llama-3.3-70B for detecting errors in PET/CT reports, finding that compact, domain-specific models achieved higher accuracy. Another paper developed an LLM-based pipeline, utilizing Qwen2.5-32B, to automatically annotate longitudinal information in radiology reports, creating a standardized benchmark for evaluating report generation models. AI

IMPACT LLMs show promise in improving the accuracy and efficiency of radiology report analysis, potentially aiding clinical decision-making.

RANK_REASON Two arXiv papers presenting research on applying LLMs to radiology report analysis.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

LLMs evaluated for radiology report accuracy and longitudinal data extraction · 2 sources tracked

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Two arXiv papers presenting research on applying LLMs to radiology report analysis.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Hermione Warr, Harry Anthony, Lilli J Freischem, Yasin Ibrahim, Daniel R McGowan, Konstantinos Kamnitsas ·

    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 often subtle and require domain expertise to detect. Although large language models …

  2. arXiv cs.AI TIER_1 Italiano(IT) · Xinyi Wang, Grazziela Figueredo, Ruizhe Li, Xin Chen ·

    Standardizing Longitudinal Radiology Report Evaluation via Large Language Model Annotation

    arXiv:2601.16753v2 Announce Type: replace-cross Abstract: Longitudinal information in radiology reports refers to the sequential tracking of findings across multiple examinations over time, which is crucial for monitoring disease progression and guiding clinical decisions. Many r…

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