  Despite considerable progress in developing artificial intelligence (AI)
algorithms for prostate cancer detection from whole slide images, the clinical
applicability of these models remains limited due to variability in
pathological annotations and existing dataset limitations. This article
proposes a novel approach to overcome these challenges by leveraging a Bayesian
framework to seamlessly integrate new data, and present results as a panel of
annotations. The framework is demonstrated by integrating a Bayesian prior with
one trained AI model to generate a distribution of Gleason patterns for each
pixel of an image. 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. Additionally, we
present a high-quality, hand-curated dataset of prostate histopathological
images annotated at the gland level by trained pre-medical 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.
