Medical AI annotation & evaluation

Prove your medical AI works.

Licensed clinicians review your model’s real outputs and show you where it’s right, where it fails, and what it critically missed.

Start a pilot →or just email us →
The problem

Most medical AI ships on trust. A validation set that looks nothing like the clinic will not survive a hospital, a regulator, or an investor asking how you know it is right.

What you get

Expert validation, not labels.

A licensed clinician reviews every output and tells you three things.

01

Was it right?

Every output confirmed or rejected against a clinician’s read.

02

The correct answer

Where the model is wrong, the clinician’s correct read — not just a flag.

03

What it critically missed

The dangerous cases: findings a clinician catches and the model did not.

The deliverable

A report you can defend.

Accuracy, precision and recall per finding, a confusion matrix, and every failure named — measured on the reviewed sample.

What we evaluate

Across every medical modality.

Wherever your model touches medicine, a licensed specialist can evaluate it.

Clinical text

Notes, extraction, and coding.

Radiology

X-ray, CT, and MRI review.

Pathology

Whole-slide and histology.

Medical LLMs

Output evaluation and RLHF.

Genomics & omics

Genomic and multi-omic annotation.

De-identification & PHI

Redaction and PHI review.

About

How the review works.

Licensed clinicians conduct structured, case-by-case review — built so the result holds up.

Who reviews

Licensed clinicians, case by case

Every case is reviewed by a licensed clinician as a structured expert assessment, one case at a time. Not crowd labelers, and not a model grading a model.

Why it holds up

Defensible by construction

Each case gets a structured, multi-dimension assessment. Data is de-identified and isolated per client, and every review is recorded — who reviewed what, and when.

Why us

Compliance-grade by default.

How the data is handled separates a defensible result from a liability. Built for medical data, not crowdsourced.

Credible

Licensed clinicians

Medical specialists review your data, not a crowd.

Safe

De-identified

Identifiers stripped before anyone sees a case.

Private

Isolated per client

Your data walled off from every other client’s.

Defensible

Fully audited

Every review logged: who, what, and when.

How it works

Prove it on a slice first.

Start small. See the value before you commit to volume.

Step 01

Send your outputs

A sample of your data and your model’s predictions.

Step 02

Clinicians review

Licensed specialists assess each case, de-identified and isolated.

Step 03

You get the report

A performance breakdown you can act on and show.

Prove your medical AI works.

Start with a pilot. See where your model is right, where it fails, and what it missed.

Start a pilot →or just email us →