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Navigating Uncertainty in MRI Diagnosis: A Human-AI Collaborative Strategy for Stratifying Clinically Significant

Xu Fu^1,2, Jie Bao³, Xiaomeng Qiao³

¹School of Engineering Medicine, Beihang University, Beijing, China.

Journal of Magnetic Resonance Imaging : JMRI

|June 16, 2026

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This summary is machine-generated.

A deep learning model, UFormer, improved prostate cancer prediction when combined with radiologists. This human-machine collaboration enhanced accuracy and reduced experience gaps in csPCa detection using bpMRI.

Area of Science:

Radiology
Artificial Intelligence
Oncology

Background:

Deep learning (DL) shows promise for predicting clinically significant prostate cancer (csPCa).
Radiologists encounter challenges in effectively utilizing DL for csPCa prediction.

Purpose of the Study:

Develop an automated DL model using biparametric-MRI (bpMRI) for csPCa prediction.
Propose a human-machine collaborative strategy to enhance csPCa prediction.

Main Methods:

A retrospective study involving 4305 patients.
Developed a DL model (UFormer) using bpMRI for prostate segmentation and csPCa prediction.
Evaluated model performance in external validation cohorts and compared it with radiologist assessments.

Main Results:

Keywords:

clinically significant prostate cancer deep learning magnetic resonance imaging prostate imaging reporting and data system

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The UFormer-radiologist collaboration significantly improved AUC and accuracy compared to radiologists alone.
Specificity increased substantially, particularly for less-experienced radiologists.
UFormer identified a high percentage of non-csPCa cases initially assessed as PI-RADS 3.

Conclusions:

The UFormer model enhances radiologist predictive performance and reduces experience-based disparities.
A UFormer-radiologist collaborative approach offers a viable strategy for clinical csPCa prediction by integrating AI and radiologist expertise.