Radiology AI validation

Radiology AI validation that checks the clinical claim.

Review the ground truth, reader disagreement, and failure cases behind the metric.

Request Consultation View Validation Framework

Chest CT study displayed on a radiology diagnostic workstation

ABR-certified radiologistClinical trial imaging strategyRadiology AI validationFDA-cleared AI directoryBody and oncology imaging review

Buyer fit

Imaging AI, workflow automation, and med-tech product teams

The metric looks good, but the clinical claim is still vague.
Ground truth rules are being made case by case.
The test set is missing the awkward cases readers remember.
Product and clinical teams need the same language for failure.

Typical outputs

What the engagement delivers

Validation plan review with radiology comments
Ground truth and adjudication rules
Failure-case review
Clinical claim and reader-facing language

Engagement model

How the work moves.From clinical question to usable output.

Step 1

Start with the claim

Name exactly what the model is supposed to help a reader or team do.

Step 2

Check the labels

Review consensus, arbitration, and ambiguity rules before trusting the metric.

Step 3

Look for failures

Focus on cases where a technically correct output could still mislead the user.

Current leadership roleDirector of AI Strategy

Physician lead for radiology AI initiatives.

Clearance-oriented experiencePrior FDA validation consulting

Validation support for AI clearance efforts.

Startup and advisory depthMultiple med-tech advisory roles

Advisory work across product and clinical evaluation.

Supporting tools and resources

Related tools.Useful references for the same decision pattern.

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Related resources

Radiology AI Validation Framework | Guide

A checklist for the cases, labels, and claims behind a model result.

Radiology Imaging Review QC Checklist | Checklist

A short check on acquisition quality, reader drift, escalation, and handoffs.

FAQ

Fit questions.Short answers before outreach.

Can this help even if the product is not yet near FDA submission?

Yes. Early review helps test whether the planned metric matches real reader behavior.

Is the focus limited to model performance review?

No. It can also cover reader disagreement, failure cases, workflow fit, and clinical claims.

Why involve an actively practicing radiologist here?

Because many failures are interpretive or workflow-related, not purely computational.

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Ready to scope the work?

Share the organization, project type, imaging domain, and timeline.

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