ArticleFrontiers in radiology2024
ChatGPT yields low accuracy in determining LI-RADS scores based on free-text and structured radiology reports in German language.
Article in Frontiers in radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed.
- Commercial large language models for oral cavity cancer staging using descriptive pre-treatment MRI reports: ready for standalone use in clinical practice?Insights into imaging · 2026Article
- Diagnostic accuracy and consistency of ChatGPT-4o in radiology: influence of image, clinical data, and answer options on performance.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Article
- Large Language Models Versus Human Readers in CAD-RADS 2.0 Categorization of Coronary CT Angiography Reports.Journal of imaging informatics in medicine · 2026Article
- Automated RECIST tumor response classification through prompt-guided large language models.Scientific reports · 2026Article
- Leveraging Large Language Models for Accurate AO Fracture Classification from CT Text Reports.Journal of imaging informatics in medicine · 2026Article
- Insufficient reporting quality in large language model studies in the field of radiology.Insights into imaging · 2026Article
- Natural language processing and LLMs in liver imaging: a practical review of clinical applications.Abdominal radiology (New York) · 2026Review
- [Research progress of large language models in tumor diagnosis: applications in textual reports and medical imaging].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2026Review
- Performance of ChatGPT-4o, Claude 3 Opus, and DeepSeek-R1 in BI-RADS Category 4 Classification and Malignancy Prediction From Mammography Reports: Retrospective Diagnostic Study.JMIR medical informatics · 2025Article
- LLM-powered TNM staging of neuroendocrine tumors from PET/CT reports.BMC medical imaging · 2025Article
- Evaluating Multiple Input Strategies of Large Language Models for Gallbladder Polyps on Ultrasound: Comparative Study.JMIR medical informatics · 2025Article
- Trends and Trajectories in the Rise of Large Language Models in Radiology: Scoping Review.JMIR medical informatics · 2025Article
- In-context learning enables large language models to achieve human-level performance in spinal instability neoplastic score classification from synthetic CT and MRI reports.La Radiologia medica · 2025Article
- Current State of Evidence for Use of MRI in LI-RADS.Journal of magnetic resonance imaging : JMRI · 2025Review
- ChatGPT vs Gemini: Comparative Accuracy and Efficiency in CAD-RADS Score Assignment from Radiology Reports.Journal of imaging informatics in medicine · 2025Article
- Article
- Structured Transformation of Unstructured Prostate MRI Reports Using Large Language Models.Tomography (Ann Arbor, Mich.) · 2025Article
- Multi-modal large language models in radiology: principles, applications, and potential.Abdominal radiology (New York) · 2025Review
- Leveraging large language models for accurate classification of liver lesions from MRI reports.Computational and structural biotechnology journal · 2025Article
- Development and validation of a novel AI framework using NLP with LLM integration for relevant clinical data extraction through automated chart review.Scientific reports · 2024Article
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8 authors.
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Abstract
Background: To investigate the feasibility of the large language model (LLM) ChatGPT for classifying liver lesions according to the Liver Imaging Reporting and Data System (LI-RADS) based on MRI reports, and to compare classification performance on structured vs. unstructured reports. Methods: LI-RADS classifiable liver lesions were included from German written structured and unstructured MRI reports with report of size, location, and arterial phase contrast enhancement as minimum inclusion requirements. The findings sections of the reports were propagated to ChatGPT (GPT-3.5), which was instructed to determine LI-RADS scores for each classifiable liver lesion. Ground truth was established by two radiologists in consensus. Agreement between ground truth and ChatGPT was assessed with Cohen's kappa. Test-retest reliability was assessed by passing a subset of Results: 205 MRIs from 150 patients were included. The accuracy of ChatGPT at determining LI-RADS categories was poor (53% and 44% on unstructured and structured reports). The agreement to the ground truth was higher ( Conclusions: ChatGPT attained only low accuracy when asked to determine LI-RADS scores from liver imaging reports. The superior accuracy and consistency throughout free-text reports might relate to ChatGPT's training process. Clinical relevance statement: Our study indicates both the necessity of optimization of LLMs for structured clinical data input and the potential of LLMs for creating machine-readable labels based on large free-text radiological databases.
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