02/23/2026
For more than a century, diagnosing cellular abnormalities has relied on trained experts looking through microscopes.
That model works — but it is labor intensive, subjective, and increasingly strained by workforce shortages.
A new AI-driven cytology platform reimagines this process. Instead of reviewing static images, it scans entire slides in three dimensions, reconstructs focal layers in real time, identifies individual nuclei, and classifies cells before generating a quantitative “census” of cellular states across the whole slide.
This is more than automation.
It is a redesign of how we measure disease.
Rather than forcing cells into simple normal versus abnormal categories, the system maps them along gradients of probability. That shift reflects a broader truth in medicine: disease is rarely binary. It exists along a spectrum.
From a precision medicine perspective, this matters deeply. Before we can personalize treatment or predict risk, we must first measure phenotype consistently and at scale. High-fidelity cellular assessment is the foundation of accurate stratification.
The future of cancer screening and diagnostic pathology may depend not only on better therapies, but on better measurement.
The microscope is evolving — and with it, the way we define disease.
https://www.nature.com/articles/d41586-026-00288-3
