Feasibility of large language models for CEUS LI-RADS categorization of small liver nodules in patients at risk for hepatocellular carcinoma
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.
Authors
- Keyu Zeng (ORCID: https://orcid.org/0000-0002-0836-3186)
- Jun Luo (ORCID: https://orcid.org/0000-0001-8974-015X)
- Andrej Lyshchik (ORCID: https://orcid.org/0000-0001-8930-2213)
- Jiayan Huang (ORCID: https://orcid.org/0000-0002-1918-2874)
- Yan Liu (ORCID: https://orcid.org/0000-0002-2743-455X)
- Ruifu Yang (ORCID: https://orcid.org/0000-0003-3219-7269)
- Qiang Lu (ORCID: https://orcid.org/0000-0002-4057-1997)
- Xiaotong Huang (ORCID: https://orcid.org/0000-0002-2240-8598)
Institutions
- Sichuan University (CN)
- Thomas Jefferson University Hospital (US)
- West China Hospital of Sichuan University (CN)
- Panzhihua University (CN)
- Jefferson University Hospitals (US)
- Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2024-12-18
- DOI
- https://doi.org/10.3389/fonc.2024.1513608
- Citations
- 6
- Primary Topic
- Liver Disease Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 2.32
Funders
- National Natural Science Foundation of China