Computational Pathology Promises Automation but Faces Major Adoption Hurdles
This review maps the current landscape of AI-driven computational pathology, from automating routine diagnostic tasks to discovering novel prognostic and predictive biomarkers directly from tissue morphology. Despite rapid innovation, clinical integration remains limited by operational, regulatory, ethical, and financial barriers that the pathology community must address.
The original study
Computational pathology in cancer diagnosis, prognosis, and prediction - present day and prospects.
- Authors
- Verghese G, Lennerz JK, Ruta D, Ng W, Thavaraj S, Siziopikou KP, et al.
- Journal
- The Journal of pathology
- Type
- Journal Article, Review, Research Support, Non-U.S. Gov't, Research Support, N.I.H., Extramural
- PMID
- 37580849
Original abstract
Computational pathology refers to applying deep learning techniques and algorithms to analyse and interpret histopathology images. Advances in artificial intelligence (AI) have led to an explosion in innovation in computational pathology, ranging from the prospect of automation of routine diagnostic tasks to the discovery of new prognostic and predictive biomarkers from tissue morphology. Despite the promising potential of computational pathology, its integration in clinical settings has been limited by a range of obstacles including operational, technical, regulatory, ethical, financial, and cultural challenges. Here, we focus on the pathologists' perspective of computational pathology: we map its current translational research landscape, evaluate its clinical utility, and address the more common challenges slowing clinical adoption and implementation. We conclude by describing contemporary approaches to drive forward these techniques. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.