Why The Future Of AI In Pathology Will Be Decided By Infrastructure, Not Algorithms

Promise Okeke is CEO of NovoPath, a premier Laboratory Information System (LIS) for AP, Molecular and Veterinary labs.

The conversation about artificial intelligence (AI) in pathology is almost entirely about models. Foundation models are trained on millions of slides. Algorithms flag mitoses, grade tumors or quantify a stain in seconds. It is an exciting moment, and the science is real.

But an algorithm cannot read a piece of glass. Before any laboratory gets value from AI, it has to become digital. That means doing the unglamorous infrastructure work first.

As the CEO of a company that specializes in digital pathology solutions, I’ve become well acquainted with the work that turns a physical operation built on paper and glass into a digital system an algorithm can plug into. ​

The Scanner Is Just The Starting Point

Most labs assume going digital starts with choosing a scanner. The scanner matters, but it is the narrowest decision in the project. The harder work, making sure every image is tied to the right patient, happens before a slide reaches it.

A lab has to know, with certainty and without anyone retyping a label, which case, block and slide an image belongs to. A specimen passes through several hands between accessioning and the microscope, and every handoff where someone reads a label and writes it again is a chance for a mismatch.

On glass, a pathologist holds the physical slide and sees its handwritten markings and the tray it arrived in. In a digital workflow, that context disappears. The scanner reads the barcode, and the barcode becomes the slide’s identity. If the label is wrong, the image becomes confidently attached to the wrong patient.

The fix is less exciting than any scanner spec: labels generated from a single source of truth and printed at the moment each block or slide is created, not batch-printed and applied later. It is tedious work, and it’s the first thing a lab should get right.