Using AI to Diagnose Small Liver Tumors
Author Information
Author(s): Huang Jiayan, Yang Rui, Huang Xiaotong, Zeng Keyu, Liu Yan, Luo Jun, Lyshchik Andrej, Lu Qiang
Primary Institution: West China Hospital of Sichuan University
Hypothesis
Can large language models improve the diagnosis of small hepatocellular carcinoma in high-risk patients using CEUS LI-RADS?
Conclusion
Large language models integrated with CEUS LI-RADS can effectively diagnose small hepatocellular carcinoma in high-risk patients, with ChatGPT-4.0 showing superior sensitivity compared to human readers.
Supporting Evidence
- ChatGPT-4.0 showed higher sensitivity in detecting small hepatocellular carcinoma than ChatGPT-4o.
- ChatGPT-4.0 demonstrated superior sensitivity compared to human readers in diagnosing small hepatocellular carcinoma.
- LLMs achieved substantial to almost perfect intra-agreement for CEUS LI-RADS categorization.
Takeaway
This study shows that AI can help doctors find small liver tumors better, especially in patients who are at high risk for liver cancer.
Methodology
The study evaluated the performance of four large language models in diagnosing small hepatocellular carcinoma using structured CEUS LI-RADS reports from high-risk patients.
Potential Biases
Potential biases in LLM outputs due to reliance on structured reporting and the subjective nature of radiological assessments.
Limitations
The study relied on structured reports, limiting access to full imaging data, and had a small sample size for certain CEUS LI-RADS categories.
Participant Demographics
Mean age of participants was 52.3 years, with 80.1% being men.
Statistical Information
P-Value
p<0.004
Confidence Interval
95% CI: 73%, 90%
Statistical Significance
p<0.004
Digital Object Identifier (DOI)
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