ChatGPT-4–Driven Liver Ultrasound Radiomics Analysis: Diagnostic Value and Drawbacks in a Comparative Study
Year: 2025
Type: article
Abstract: BACKGROUND: Artificial intelligence (AI) is transforming medical imaging, with large language models such as ChatGPT-4 emerging as potential tools for automated image interpretation. While AI-driven radiomics has shown promise in diagnostic imaging, the efficacy of ChatGPT-4 in liver ultrasound analysis remains largely unexamined. OBJECTIVE: This study aimed to evaluate the capability of ChatGPT-4 in liver ultrasound radiomics, specifically its ability to differentiate fibrosis, steatosis, and normal liver tissue, compared with conventional image analysis software. METHODS: Seventy grayscale ultrasound images from a preclinical liver disease model, including fibrosis (n=31), fatty liver (n=18), and normal liver (n=21), were analyzed. ChatGPT-4 extracted texture features, which were compared with those obtained using interactive data language (IDL), a traditional image analysis software. One-way ANOVA was used to identify statistically significant features differentiating liver conditions, and logistic regression models were used to assess diagnostic performance. RESULTS: -score (0.85). When combined, ChatGPT-4 attained 76% accuracy and 83% sensitivity in classifying liver disease. Receiver operating characteristic analysis demonstrated strong discriminatory performance, with area under the curve values of 0.75 for fibrosis, 0.87 for normal liver, and 0.97 for steatosis. Compared with IDL image analysis software, ChatGPT-4 exhibited slightly lower sensitivity (0.83 vs 0.89) but showed moderate correlation (r=0.68, P<.001) with IDL-derived features. However, it significantly outperformed IDL in processing efficiency, reducing analysis time by 40%, and highlighting its potential for high throughput radiomic analysis. CONCLUSIONS: Despite slightly lower sensitivity than IDL, ChatGPT-4 demonstrated high feasibility for ultrasound radiomics, offering faster processing, high-throughput analysis, and automated multi-image evaluation. These findings support its potential integration into AI-driven imaging workflows, with further refinements needed to enhance feature reproducibility and diagnostic accuracy.
Source: JMIR AI
DOI: 10.2196/68144
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4410489296