BUFFALO, NY - April 8, 2025 – A new #editorial was #published in Oncotarget, Volume 16, on April 4, 2025, titled “Deep learning-based uncertainty quantification for quality assurance in hepatobiliary imaging-based techniques."

Dr. Yashbir Singh from Mayo Clinic and his colleagues discussed how artificial intelligence (AI) can improve liver imaging by recognizing when it might be wrong. This approach, called “uncertainty quantification,” helps clinicians better detect liver cancer and other diseases by pointing out areas in medical scans that need a second look. The authors explain how these AI tools could make imaging results more accurate and reliable, which is especially important when diagnosing serious conditions like liver tumors.

Liver and bile duct imaging is difficult because of the organ’s complex structure and differences in image quality. Even skilled radiologists can struggle to identify small or hidden tumors, especially in patients with liver damage or scarring. The editorial explains how new AI models not only read medical images but also measure their own confidence. When the AI system is unsure, it can alert clinicians to take a closer look. This extra layer of information can reduce missed diagnoses and improve early detection of liver cancer.

One of the most advanced tools described in the editorial is called AHUNet (Anisotropic Hybrid Network). This AI model works with both 2D and 3D images and can highlight which parts of a scan it is most confident about. It performed well when measuring the entire liver and showed how its confidence dropped when scanning smaller or multiple lesions. This feature helps clinicians know when more testing or review is needed.

The authors also looked at other AI models used in liver imaging. Some tools were able to analyze liver fat using ultrasound images and give clinicians both a result and a confidence score. Others improved the speed and accuracy of liver magnetic resonance imaging (MRI) scans, helping to create clear images in less time. These advancements could help hospitals work faster and provide better care.

The editorial highlights how this technology can be especially helpful in smaller clinics. If they do not have liver specialists, they could still use AI systems that flag uncertain results and send them to larger centers for review. Such an approach could improve care in rural or less-resourced areas.

“Radiology departments should develop standardized reporting templates that incorporate uncertainty metrics alongside traditional imaging findings.”

By using AI tools that know when to second-guess themselves, clinicians may soon have more reliable methods for detecting liver cancer and monitoring liver disease. The authors suggest that uncertainty-aware AI may soon become a vital part of everyday medical imaging, supporting faster and more accurate decisions in liver disease care.

DOI: https://doi.org/10.18632/oncotarget.28709

Correspondence to: Yashbir Singh — singh.yashbir@mayo.edu

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Keywords: cancer, deep learning, uncertainty quantification, radiology, hepatobiliary imaging

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AI That Measures Its Own Uncertainty Could Improve Liver Cancer Detection

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AI That Measures Its Own Uncertainty Could Improve Liver Cancer Detection

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