ArticleRadiology. Artificial intelligence2024
Generative Large Language Models for Detection of Speech Recognition Errors in Radiology Reports.
Article in Radiology. Artificial intelligence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 1 of them a synthesis that pooled it.
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Who cites it
39 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Performance and improvement strategies for adapting generative large language models for electronic health record applications: A systematic review.International journal of medical informatics · 2026Pooled it
- GPT-4.1 and Llama 3.3 70 fail to detect clinically relevant errors in radiology reports in zero-shot evaluation.European radiology · 2026Article
- Error Detection and Correction in Chinese Radiology Reports Using Large Language Models: Real-World Clinical Validation Study.Journal of medical Internet research · 2026Article
- Foundation models in radiology: a primer for pediatric radiologists.Pediatric radiology · 2026Review
- Reporting efficiency in diagnostic imaging: Can plug-and-play general-purpose large language models outperform conventional speech recognition?European radiology · 2026Article
- Automated generation of impressions in abdominal radiology reports using an artificial intelligence-based tool: performance compared to manual impressions.Abdominal radiology (New York) · 2026Article
- Reasoning Model-Assisted Second-Reader Quality Control of Chinese-Language Ultrasound Reports: A Retrospective Imaging Informatics Study.Journal of imaging informatics in medicine · 2026Article
- Assessing the Reliability, Accuracy, and Relevance of Artificial Intelligence Speech Recognition for Clinical Documentation: A Scoping Review.Journal of evaluation in clinical practice · 2026Article
- AI application in radiology beyond imaging: What is the role of text-based large language models?European radiology · 2026Article
- Error Detection in Emergency Radiology Reports Using a Large Language Model: Multistage Evaluation Study.Journal of medical Internet research · 2026Article
- Insufficient reporting quality in large language model studies in the field of radiology.Insights into imaging · 2026Article
- Emergency radiology: an evolving subspecialty facing rapid growth and unique stressors, with a path toward sustainable clinical practice.Emergency radiology · 2026Review
- Standardizing Heterogeneous MRI Series Description Metadata Using Large Language Models.Journal of imaging informatics in medicine · 2026Article
- Privacy-Preserving Large Language Model for Matching Findings and Tracking Interval Changes in Longitudinal Radiology Reports.Journal of imaging informatics in medicine · 2026Article
- Evaluating large language models for automated TNM staging from PET-CT reports: a multi-cancer comparative study.Frontiers in digital health · 2026Article
- RadCLARE: an automated clinical language engine for detecting semantic errors in radiology reports.European radiology experimental · 2025Article
- Multi-step retrieval and reasoning improves radiology question answering with large language models.NPJ digital medicine · 2025Article
- Evaluating Large Language Models in Interpreting MRI Reports and Recommending Treatment for Vestibular Schwannoma.Diagnostics (Basel, Switzerland) · 2025Article
- From large language models to multimodal AI: a scoping review on the potential of generative AI in medicine.Biomedical engineering letters · 2025Review
- Large language models for error detection in radiology reports: a comparative analysis between closed-source and privacy-compliant open-source models.European radiology · 2025Article
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6 authors.
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Abstract
This study evaluated the ability of generative large language models (LLMs) to detect speech recognition errors in radiology reports. A dataset of 3233 CT and MRI reports was assessed by radiologists for speech recognition errors. Errors were categorized as clinically significant or not clinically significant. Performances of five generative LLMs-GPT-3.5-turbo, GPT-4, text-davinci-003, Llama-v2-70B-chat, and Bard-were compared in detecting these errors, using manual error detection as the reference standard. Prompt engineering was used to optimize model performance. GPT-4 demonstrated high accuracy in detecting clinically significant errors (precision, 76.9%; recall, 100%; F1 score, 86.9%) and not clinically significant errors (precision, 93.9%; recall, 94.7%; F1 score, 94.3%). Text-davinci-003 achieved F1 scores of 72% and 46.6% for clinically significant and not clinically significant errors, respectively. GPT-3.5-turbo obtained 59.1% and 32.2% F1 scores, while Llama-v2-70B-chat scored 72.8% and 47.7%. Bard showed the lowest accuracy, with F1 scores of 47.5% and 20.9%. GPT-4 effectively identified challenging errors of nonsense phrases and internally inconsistent statements. Longer reports, resident dictation, and overnight shifts were associated with higher error rates. In conclusion, advanced generative LLMs show potential for automatic detection of speech recognition errors in radiology reports.
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