An explainable deep learning AI model using gadoxetic acid-enhanced MRI improves sensitivity in diagnosing hepatocellular carcinoma (HCC).
Key Details
- 1The AI model was trained on 1,023 liver lesions from 839 patients using multi-phase MRI.
- 2It classified lesions as HCC or non-HCC and provided visual explanations (LI-RADS feature identification).
- 3On a test set, the model achieved AUC 0.97 for HCC diagnosis.
- 4Compared to LI-RADS 5, AI had higher sensitivity (91.6% vs. 74.8%) with similar specificity (90.7% vs. 96%).
- 5Radiologists assisted by the AI showed improved sensitivity (up to 89%) with no loss in specificity.
- 6Explainability is highlighted, aligning with regulatory emphasis on interpretable AI.
Why It Matters

Source
AuntMinnie
Related News

LLMs Demonstrate Strong Potential in Interventional Radiology Patient Education
DeepSeek-V3 and ChatGPT-4o excelled in accurately answering patient questions about interventional radiology procedures, suggesting LLMs' growing role in clinical communication.

Women's Uncertainty About AI in Breast Imaging May Limit Acceptance
Many women remain unclear about the role of AI in breast imaging, creating hesitation toward its adoption.

Stanford Team Introduces Real-Time AI Safety Monitoring for Radiology
Stanford researchers introduced an ensemble monitoring model to provide real-time confidence assessments for FDA-cleared radiology AI tools.