Comprehensive Summary
This editorial by Riaz et al delves into the outlooks and shortcomings of current pathological analyses when considering prostate cancer diagnoses and classifications. They present an argument for AI-based histologic evaluation that would provide an objective tissue analysis without observer variability, and detection of features that cannot be seen with human perception. This group discusses the breakthroughs of Artera AI, the utilization of whole-slide image (WSI) algorithms with tissue microarray (TMA) models, and their predictive capabilities as compared to classical risk assessment tools. They discuss the need for more genomic classifiers, along with further prospective, multi-institutional studies to validate capabilities.
Outcomes and Implications
Analyzing current breakthroughs in histopathology AI leads to the development of technology to the benefit of the patient. Riaz et al suggests the next steps toward determining the efficacy of these new tools involve broadening the scope of current research and applying these methods rigorously. To later incorporate AI into guidelines for future patient safety and care, these new technologies must be thoroughly validated.