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Related Concept Videos

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Related Experiment Video

Updated: Mar 31, 2026

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
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Concurrent AI-human interaction in prostate cancer MRI interpretation: More hype than help?

Andrea Ponsiglione1, Giuseppe Di Costanzo2, Alfonso Maria Ponsiglione3

  • 1Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy.

European Radiology Experimental
|March 30, 2026
PubMed
Summary

Artificial intelligence (AI) did not improve diagnostic accuracy for prostate cancer detection in MRI scans across various reader expertise levels. Further research is needed to optimize AI integration for prostate cancer diagnosis.

Keywords:
Artificial intelligenceDiagnosis (computer-assisted)Multiparametric magnetic resonance imagingProstate-specific antigenProstatic neoplasms

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Prostate Cancer Detection

Background:

  • Multiparametric MRI is crucial for detecting clinically significant prostate cancer (csPCa).
  • AI tools are emerging as decision support for radiologists.

Purpose of the Study:

  • To evaluate a commercial AI system as a concurrent decision-support tool for csPCa detection using MRI.
  • To assess the impact of AI assistance on diagnostic performance and benefit-to-harm ratios across different reader expertise levels.

Main Methods:

  • Retrospective study of 100 patients with suspected prostate cancer undergoing multiparametric MRI.
  • Scans were reviewed by six readers (experts, basic radiologists, residents) with and without AI assistance.
  • Inter-reader agreement, csPCa scores, diagnostic performance, and benefit-to-harm ratios were assessed.

Main Results:

  • AI assistance did not improve inter-reader agreement (Fleiss κ 0.573 vs 0.584).
  • No significant difference in the area under the ROC curve between AI-assisted and unassisted readings (0.87 vs 0.86).
  • Residents showed the most PI-RADS score changes with AI assistance; minor improvements in grade selectivity and biopsy avoidance were noted for experts and residents, respectively.

Conclusions:

  • AI did not significantly enhance diagnostic accuracy for csPCa detection across readers of varying expertise.
  • Minor impacts on benefit-to-harm ratios were observed.
  • Further research is needed to optimize AI integration in prostate cancer detection.