Digital Pathology: Slides, AI, & Challenges

Pathology has progressed from glass slides to AI-driven digital diagnostics, promising to transform disease detection while facing adoption and market challenges

Bo Wang, Head of Biomedical AI at Xaira Therapeutics, puts it simply: “Pathology is the cornerstone of diagnosis, especially when it comes to cancer.” While continuously developing since ancient times, by the late 2000s the pathology workflow with glass slides, physical archives, and in-person consultations couldn’t keep pace with modern demands. It slowed diagnoses, made collaboration cumbersome, and limited access. High-resolution images were massive—often gigabytes each—and required costly whole-slide imaging (WSI) systems, which lacked FDA clearance for years.

By the mid-2010s, that changed. Advances in WSI, falling storage costs, and FDA approval sparked the rise of digital pathology—scanning slides for easier sharing, annotation, and remote consultation. Computationally minded researchers quickly recognized the value of these datasets, and startups emerged, often spun out of collaborations with hospitals and labs holding vast archives of digitized slides.

In this article: How it started — How it’s going — Time for Foundation Models — The Market Players — Not Only Cancer — Current State of the Industry

How it started

Pathology history begins with the careful observations of ancient doctors, who recognized the importance of tracking how diseases developed even without understanding their causes. The 17th-century BC Edwin Smith Papyrus, written in hieroglyphs, described cases like skin ulcerations but could not explain their origins.

Centuries later, Hippocrates proposed the influential Humoral Theory, suggesting illness arose from imbalances in four bodily fluids—a framework that guided medicine until the 17th century. In the Middle Ages and Renaissance, figures like Antonio Benivieni advanced the field by systematically recording autopsy findings, marking a shift toward pathology as a distinct discipline.

The invention of the microscope in the late 16th century, refined by Robert Hooke, took on new significance in the 19th century when Rudolf Virchow used it to establish that diseases originate at the cellular level. Improvements in tissue preparation (fixation, embedding, staining) helped shape modern histopathology. The 20th century brought integration with other sciences, as advances in immunology, chemistry, and molecular biology deepened understanding.

Antibody discovery enabled immunohistochemistry, allowing for the precise detection of proteins within tissues and more accurate diagnoses. The invention of PCR in 1983 pushed diagnostics forward by capacitating genetic material to be amplified from very small samples. By the 21st century, pathology had gained the ability to examine single cells, offering unprecedented precision in detecting disease and guiding prognosis.

Pathology evolution over time. From: Digital and Computational Pathology: A Speciality Reimagined. License: CC-BY-4.0

How it’s going

Over time researchers started trying to integrate digital solutions into pathology. In 1965, Judith Prewitt and Mortimer Mendelsohn from Upenn performed a first computerized analysis of microscopy images of cells and chromosomes. Over three decades later, 1999 was marked by the introduction of the whole slide imaging (WSI). WSIs are created by scanning glass microscope slides to produce a high resolution digital image, which is later reviewed by a pathologist to determine the diagnosis. Fast forward to 2017 and Phillips received a milestone FDA approval for its IntelliSite, the first WSI system for primary diagnosis in surgical pathology.

Essentially, WSI analysis can be encoded as the image recognition problem, which made digital pathology a fertile soil for the involvement of machine learning algortihms.

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