Two ways an AI oncology diagnostic gets paid, and only one scales

An oncology AI company usually has the part everyone said was hard. The algorithm reads the scan, or the slide, or the assay, and it reads it well. A regulator has cleared it. A few hospitals use it. And still the revenue line is flat and the round will not close. The founders decide the problem is sales, or awareness, or one more clinical paper. It is none of those. The problem is that nobody has answered the only question that sets the value of a diagnostic: who pays for it, and why.

A diagnostic is not a drug. A drug is paid for because a patient takes it and a health system reimburses it through a channel that already exists. A diagnostic has to earn its payment. In oncology AI there are really only two ways to do that. They look adjacent. They are different businesses, with different evidence, different buyers and different ceilings. One of them scales. The other is a good product trapped inside a small company.

The first way: sell to the people who use it

The obvious route is to sell to the hospitals and laboratories that run the test. Clearance gets you onto the market. It does not get you paid. Software that analyses in-vitro samples falls under CE-IVDR, the EU regulation 2017/746. Software that reads images, radiology and most pathology AI, falls under MDR, regulation 2017/745. Either route earns the right to be used. Neither earns a price.

In most health systems an AI diagnostic has no reimbursement code of its own. The pathologist is already paid to read the slide. The radiologist is already paid to read the scan. The AI sits on top as an added cost, not a new billable line. So the value argument shrinks to efficiency: faster reads, fewer misses, lower downstream spend. Efficiency is real, but it is a weak thing to sell, because the budget for it is small and the saving is spread thin across a system that does not feel it in one place.

This is the route where cleared, accurate products quietly die. Not because the science failed. Because the science was never the thing that decided who pays. The clinical route is not closed, and in some markets a coded, guideline-backed test does earn reimbursement over years. But it is slow, fragmented across payers, and rarely the thing that makes the company large.

The second way: let pharma pay

The buyer who pays a price that builds a company is usually pharma. Pharma pays for a diagnostic when it makes a drug work better: when it selects the patients who respond, enriches a trial, or de-risks a program. Here the diagnostic stops being a cost and becomes leverage on an asset worth far more than the test itself.

But "pharma pays" splits again into two shapes, and the split is the whole point. They are sold differently, valued differently, and they do not grow the same way.

Shape one: the companion test tethered to a drug

A companion diagnostic is built for one drug. It identifies the patients that drug treats, it is approved alongside it, and it lives on the label. When it works, it is paid through the drug, and the economics can be excellent.

But the company is a hostage. Its value inflection points are not its own milestones. They are the drug's trial readouts. If the drug fails its pivotal trial, the test is worth nothing, however good the algorithm. And it is a one-shot. A companion test for one drug is one product. The next drug is a new coupling, built from the beginning. A single companion diagnostic is a strong product with a hard ceiling, and the ceiling is the fate of one molecule.

Shape two: the platform that de-risks many programs

The other shape does not tether to one drug. It is a stratification or biomarker engine that pharma uses across many programs: to enrich a trial, to select responders, to rescue a failing study by finding the population that does respond. The same engine, drawing on the same data, serves one sponsor after another. Revenue is recurring and repeatable. Each program adds data, and the data compounds the advantage.

This is the shape that scales, because it is a platform and not a product. It is also the shape the large players have built, Tempus, Foundation Medicine, Guardant among them, which tells you two things at once: the model works, and the ground is occupied.

Why only one scales

A product is capped by the thing it is attached to. A platform is capped by how many things it can attach to. A companion test rises and falls with one drug. A stratification platform rises with every program it touches. That is the distance between a company acquired for a single asset and a company acquired for an engine.

Both can be worth building. A companion test on a successful drug can return more than most ventures ever will. But it does not grow on its own, and an investor pricing growth will see the ceiling before the founder does. Only the platform compounds.

The moat is the data, not the model

In either shape, the defensible asset is rarely the algorithm and almost never the patent. Models are reproduced. Software patents are thin and designed around. The moat is the data: annotated, clinical-grade, and generalisable across populations and machines. An algorithm trained on one hospital's scanner and one population often fails on the next, and that fragility is exactly what a buyer's due diligence is built to find.

So the scarcest resource in oncology AI is not another point of accuracy. It is access to the right clinical data from the right centres of excellence. Italy's IRCCS network is one of the few places in Europe where that data and that validation sit under one roof, which is why it belongs in the conversation earlier than founders expect. See the research use only trap in Europe for the regulatory mirror of the same problem: an algorithm sold as "research use only" to avoid CE-IVDR is the software version of a line that does not hold.

Which one you are building changes everything

The choice is not cosmetic. A companion test and a stratification platform need different evidence, attract different buyers, and trade at different multiples. They are also sold to pharma differently. The companion test is a co-development deal tied to a single program. The platform is a series of partnerships across many.

In both, the value is captured or lost in how the partnership is structured: the pipeline gap it fills, the data package that survives due diligence, the milestones, the terms. The science earns the conversation. The deal architecture decides what you keep. That is true whether your buyer is a venture investor or a pharma partner, and in this segment it is almost always the second. See why VCs keep passing and the five conditions a pharma company needs before it buys.

The hard part was never the algorithm. It is knowing who pays, choosing the shape that scales, and building the deal that captures the value before a pharma committee decides what the asset is worth.

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