Teaming Up with AI in PCa Diagnosis

I realized that there is not much review literature on radiologist-AI collaboration, so I decided to do a talk about it, taking cues from the lung and breast cancer literature and applying the principles to the usual early prostate cancer detection scenario (single reader), not population screening (which is likely to be double reading).

This is now published: Padhani AR, Papanikolaou N. AI and human interactions in prostate cancer diagnosis using MRI. Eur Radiol. 2025 Mar 7. doi: 10.1007/s00330-025-11498-0. Epub ahead of print. PMID: 40055229.

There is a notebookLM podcast, too; watch out for this, which I will release about 2 weeks after this one goes live. Enjoy.

Learning objectives

= Explore different scenarios for integrating AI into the diagnostic workflow.
= Analyze the advantages and disadvantages of each AI-integrated scenario regarding efficiency, ethical implications, and potential for bias.
= Understand the factors that influence the effectiveness of AI assistance, including AI accuracy, radiologist characteristics, and task complexity.
= Consider the ethical implications of using and not using AI in prostate cancer diagnosis, emphasizing the need to balance AI assistance with human oversight.
=Identify key performance requirements for AI systems in different scenarios.
=Recognize the need for prospective studies to evaluate the clinical applicability of AI systems in prostate cancer detection using MRI.