Requirements for AI Development and Reporting for MRI Prostate Cancer Detection in Biopsy-Naive Men: PI-RADS Steering Committee, Version 1.0

This document defines the key considerations for developing and reporting an artificial intelligence (AI) interpretation model for the detection of clinically significant prostate cancer (PCa) at MRI in biopsy-naive men with a positive clinical screening status. Specific data and performance metric requirements and a checklist are provided for this use case. Data requirements emphasize the need for sufficient information to provide transparency and characterization of training and test data. The definition of a true-negative examination (which includes a minimum of 2-year follow-up), the need for image quality assessments, and nonimaging metadata requirements are provided. Performance metrics ranges are included, such as a cancer detection rate of 40%–70% for Prostate Imaging Reporting and Data System, or PI-RADS, 4 or higher lesions and demonstration of equivalent or better than human performance using receiver operating characteristic and precision-recall curves. The use of open datasets such as those used in the AI challenge model is encouraged. The study design should include conformity with the Checklist for Artificial Intelligence in Medical Imaging requirements. This article should be taken in the context of the current and evolving regulatory landscape. This review provides guidance based on subspeciality expertise in prostate MRI and will hopefully accelerate the clinical translation of AI in PCa detection.