Overcoming Limitations in Artificial Intelligence‐Based Prostate Cancer Detection With Improved Datasets and a Bayesian Framework for Panel Predictions

Despite significant advances in artificial intelligence (AI) algorithms for prostate cancer detection from whole slide images, the clinical applicability of these models remains limited. Variability in inter‐ and intrapathological grading, low generalizability of training datasets, and insufficient annotation precision restrict the performance of downstream models. This article introduces a novel Bayesian framework that addresses these challenges by generating pixel‐wise posterior distributions, thereby providing a probabilistic output that enables the simulation of a panel consensus and enabling seamless integration of new data and models as they become available. The framework is demonstrated by integrating a Bayesian prior with a trained AI model to produce a per‐pixel distribution of Gleason patterns. It is shown that using this distribution of Gleason patterns rather than a ground‐truth label can improve model applicability, mitigate errors, and highlight areas of interest for pathologists. Furthermore, we present a high‐quality, hand‐curated dataset of prostate histopathological images annotated at the gland level by trained premedical students and verified by an expert pathologist. We highlight the potential of this adaptive and uncertainty‐aware framework for developing clinically deployable AI tools that can support pathologists in accurate prostate cancer grading, improve diagnostic accuracy, and create positive patient outcomes. This work is presented as an early‐stage, proof‐of‐concept study; the framework has not been validated for clinical use and is not intended for diagnostic deployment in its current form.