Start a Radiology AI Validation and Auditing Service

1. 1

   Understand the validation gap you fill

   The 2025 JAMA Network Open review of 717 radiology AI devices found only about 5 percent were prospectively tested and just 8 percent had any human-in-the-loop testing before clearance. So a cleared tool is not a validated tool for a given hospital's population and scanners. Your business exists to close that gap with independent evidence, before and after deployment.

2. 2

   Build the validation methodology

   Create a rigorous, repeatable methodology to test an AI tool on a hospital's own data and population: accuracy, calibration, subgroup performance, failure modes, and human-in-the-loop effects. This method, grounded in clinical-research and data-science standards, is your product. Credibility depends on statistical and clinical rigor, not vendor marketing.

3. 3

   Assemble clinical and data-science expertise

   You need radiology clinical knowledge and machine-learning and biostatistics capability together, because validating medical AI requires both. This blended expertise is the moat and the hiring challenge. It is what lets you speak credibly to both hospitals and vendors.

4. 4

   Sell pre-deployment validation to hospitals

   Offer health systems an independent check before they deploy a tool: does it perform on our patients, on our scanners, safely. As buyers grow wary of the evidence gap, this pre-purchase validation becomes a standard step. Position as the independent party neither the vendor nor the hospital can be.

5. 5

   Add ongoing post-market surveillance

   AI performance can drift as populations, scanners, and protocols change, so sell continuous monitoring of deployed tools, not just a one-time check. Post-market surveillance is recurring revenue and a genuine safety service. This ongoing relationship is more valuable than a single audit.

6. 6

   Note the related advisory cards

   This is distinct from generic clinical-AI validation consultancies already in the library because it is radiology-imaging-specific, and it pairs with the performance-heterogeneity consultancy and the value-assessment service (separate cards). You can start narrow (validation) and expand across the evidence and value agenda for imaging AI.

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Best for: Clinical researchers and data scientists who can independently validate medical AI

Not for: This is not for someone without both clinical imaging knowledge and data-science rigor. The credibility of the validation is the whole business. If you have that background, it is a low-capital, high-margin entry; if you do not, the methodology will not hold up to scrutiny.