AI and Pathology: Steering Treatment and Predicting Outcomes
It addresses challenges in tissue interpretation for medical applications, but is incremental as it primarily reviews existing methods.
The paper surveys AI methods for histopathology to quantitatively characterize disease states, predict patient outcomes, and steer treatments, leveraging data analysis and computing advances.
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.