AI and Pathology: Steering Treatment and Predicting Outcomes

TL;DR

This paper reviews how AI enhances histopathology by enabling detailed tissue analysis, improving disease characterization, patient outcome prediction, and guiding treatment decisions through advanced data analysis and computational methods.

Contribution

It provides a comprehensive survey of AI methods applied to histopathology, highlighting current challenges and potential solutions for tissue interpretation and disease analysis.

Findings

AI methods facilitate multi-scale tissue analysis

Enhanced prediction of patient outcomes using AI

AI-driven treatment steering improves clinical decision-making

Abstract

The combination of data analysis methods, increasing computing capacity, and improved sensors enable quantitative granular, multi-scale, cell-based analyses. We describe the rich set of application challenges related to tissue interpretation and survey AI methods currently used to address these challenges. We focus on a particular class of targeted human tissue analysis - histopathology - aimed at quantitative characterization of disease state, patient outcome prediction and treatment steering.