ArticleFrontiers in oncology2024
Feasibility of large language models for CEUS LI-RADS categorization of small liver nodules in patients at risk for hepatocellular carcinoma.
Article in Frontiers in oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Large language models for analyzing contrast-enhanced ultrasound reports of pancreatic cystic lesions.Quantitative imaging in medicine and surgery · 2026Article
- Large Language Models for Diagnosis and Prognosis of Chronic Liver Diseases: A Systematic Review.Health science reports · 2026Review
- Empowering liver cancer diagnosis and treatment with foundation models: technological innovation and clinical practice.Clinical and experimental medicine · 2026Review
- Evaluating Chain-of-Thought reasoning in large language models for thyroid ultrasound interpretation: a dual-information approach.Frontiers in artificial intelligence · 2026Article
- A Multimodal Large Language Model as an End-to-End Classifier of Thyroid Nodule Malignancy Risk: Usability Study.JMIR formative research · 2025Article
- Large Language Models in Medical Diagnostics: Scoping Review With Bibliometric Analysis.Journal of medical Internet research · 2025Article
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Authors and funding
8 authors.
Funding
Abstract
Background: Large language models (LLMs) offer opportunities to enhance radiological applications, but their performance in handling complex tasks remains insufficiently investigated. Purpose: To evaluate the performance of LLMs integrated with Contrast-enhanced Ultrasound Liver Imaging Reporting and Data System (CEUS LI-RADS) in diagnosing small (≤20mm) hepatocellular carcinoma (sHCC) in high-risk patients. Materials and Methods: From November 2014 to December 2023, high-risk HCC patients with untreated small (≤20mm) focal liver lesions (sFLLs), were included in this retrospective study. ChatGPT-4.0, ChatGPT-4o, ChatGPT-4o mini, and Google Gemini were integrated with imaging features from structured CEUS LI-RADS reports to assess their diagnostic performance for sHCC. The diagnostic efficacy of LLMs for small HCC were compared using McNemar test. Results: The final population consisted of 403 high-risk patients (52 years ± 11, 323 men). ChatGPT-4.0 and ChatGPT-4o demonstrated substantial to almost perfect intra-agreement for CEUS LI-RADS categorization (κ values: 0.76-1.0 and 0.7-0.94, respectively), outperforming ChatGPT-4o mini (κ values: 0.51-0.72) and Google Gemini (κ values: -0.04-0.47). ChatGPT-4.0 had higher sensitivity in detecting sHCC than ChatGPT-4o (83%-89% vs. 70%-78%, Conclusion: LLM integrated with CEUS LI-RADS offers potential tool in diagnosing sHCC for high-risk patients. ChatGPT-4.0 demonstrated satisfactory consistency in CEUS LI-RADS categorization, offering higher sensitivity in diagnosing sHCC while maintaining comparable specificity to that of human readers.
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