ArticleRadiology2025
Generative Large Language Models Trained for Detecting Errors in Radiology Reports.
Article in Radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- GPT-4.1 and Llama 3.3 70 fail to detect clinically relevant errors in radiology reports in zero-shot evaluation.European radiology · 2026Article
- Diagnostic performance of large language models for discrepancy detection in non-English resident-authored abdominal CT and MRI reports: a Turkish-language evaluation.Abdominal radiology (New York) · 2026Article
- From zero-shot to fine-tuning: optimize large language models for error detection of ultrasound reports.Insights into imaging · 2026Article
- Error Detection and Correction in Chinese Radiology Reports Using Large Language Models: Real-World Clinical Validation Study.Journal of medical Internet research · 2026Article
- Article
- Improving Radiology Report Error Detection Using a Multipass Large Language Model: Framework Development and Validation.JMIR medical informatics · 2026Article
- Classifying diagnostic pitfalls in juxtapleural CT lesions: A root cause.European journal of radiology open · 2026Article
- Screening for Missed Opportunities for Diagnosis in the ED Using eTriggers and Large Language Models.JAMA network open · 2026Article
- Artificial Intelligence Across the Radiology Workflow: A Nine-Stage Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- The evolving landscape of large language models and non-large language models in health care.npj health systems · 2026Article
- Multimodal natural language processing in ophthalmology: bridging clinical text and medical imaging.Frontiers in medicine · 2026Review
- Large language models in emergency and critical care medicine: a comprehensive review of applications, challenges, and future directions.Burns & trauma · 2026Review
- Agentic artificial intelligence in radiology workflow: from image interpretation to report quality control.Frontiers in medicine · 2026Review
- Evaluation of large language models for PI-RADS score extraction from free-text prostate MRI reports: a comparative study with human readers.Frontiers in oncology · 2026Article
- Diagnostic and interpretive gains from reasoning over conclusions with a large reasoning model in radiology.NPJ digital medicine · 2025Article
- Multi-center benchmarking of large language models for clinical decision support in lung cancer screening.Cell reports. Medicine · 2025Article
- Assessment of ChatGPT performance in orbital MRI reporting with multimetric evaluation of transformer based language models.Scientific reports · 2025Article
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11 authors.
Funding
Abstract
Background Large language models (LLMs) offer promising solutions, yet their application in medical proofreading, particularly in detecting errors within radiology reports, remains underexplored. Purpose To develop and evaluate generative LLMs for detecting errors in radiology reports during medical proofreading. Materials and Methods In this retrospective study, a dataset was constructed with two parts. The first part included 1656 synthetic chest radiology reports generated by GPT-4 (OpenAI) using specified prompts, with 828 being error-free synthetic reports and 828 containing errors. The second part included 614 reports: 307 error-free reports between 2011 and 2016 from the MIMIC chest radiograph (MIMIC-CXR) database and 307 corresponding synthetic reports with errors generated by GPT-4 on the basis of these MIMIC-CXR reports and specified prompts. All errors were categorized into four types: negation, left/right, interval change, and transcription errors. Then, several models, including Llama-3 (Meta AI), GPT-4, and BiomedBERT, were refined using zero-shot prompting, few-shot prompting, or fine-tuning strategies. Finally, the performance of these models was evaluated using F1 scores, 95% CIs, and paired-sample
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