Towards Launching AI Algorithms for Cellular Pathology into Clinical & Pharmaceutical Orbits
It targets researchers and practitioners in histopathology and oncology by highlighting incremental steps needed to advance AI-driven diagnostics and treatments.
The paper addresses the limitations and challenges of Computational Pathology (CPath) in applying AI algorithms to clinical and pharmaceutical settings, aiming to guide future research for overcoming these barriers.
Computational Pathology (CPath) is an emerging field concerned with the study of tissue pathology via computational algorithms for the processing and analysis of digitized high-resolution images of tissue slides. Recent deep learning based developments in CPath have successfully leveraged sheer volume of raw pixel data in histology images for predicting target parameters in the domains of diagnostics, prognostics, treatment sensitivity and patient stratification -- heralding the promise of a new data-driven AI era for both histopathology and oncology. With data serving as the fuel and AI as the engine, CPath algorithms are poised to be ready for takeoff and eventual launch into clinical and pharmaceutical orbits. In this paper, we discuss CPath limitations and associated challenges to enable the readers distinguish hope from hype and provide directions for future research to overcome some of the major challenges faced by this budding field to enable its launch into the two orbits.