Towards Launching AI Algorithms for Cellular Pathology into Clinical & Pharmaceutical Orbits

TL;DR

This paper discusses the potential and challenges of deploying AI algorithms in computational pathology for clinical and pharmaceutical applications, emphasizing recent advances and future research directions.

Contribution

It provides an overview of current limitations in computational pathology and outlines key challenges to facilitate AI's integration into clinical and pharmaceutical settings.

Findings

Deep learning enhances predictive accuracy in histopathology.

Major challenges include data quality, interpretability, and clinical validation.

Future research directions are proposed to overcome these hurdles.

Abstract

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.