Healthcare AI Validation: Real-World Clinical Validation & Benchmarking

Your Healthcare AI Model Needs Real Clinical Feedback,
We Built the Infrastructure for That.

85% of healthcare AI pilots globally never reach full clinical deployment — stuck in what the industry calls "pilot purgatory." Not because the models don't work. Because the teams can't produce the real-world validation evidence that hospitals, health systems, and regulators now require before signing.

Radiology AI models lose up to 24% specificity when moved from internal validation to external datasets. FDA 510(k) review averaged 151 days in 2024 — before you submit, you need the evidence. TGA (Australia) and MHRA (UK) require clinical evidence proportional to risk class. (Gartner 2024 · FDA CDRH 2024 · TGA guidance 2026 · PMC systematic review 2024–25)

Schedule a Demo
Live Demo · Shadow Mode
Your AI runs silently inside the hospital.
Doctors work normally. You get the data.

If your model has never run on real patient cases outside your training population, your benchmark numbers are not evidence — and a hospital procurement committee in the US, UK, Australia, or India will tell you that directly.

Find out in 30 days
Step 1 · Deploy
We integrate your AI into a live hospital
No data leaves the hospital. No disruption to the clinical team. Your model runs quietly alongside real cases.
Step 2 · Observe
Your AI sees real cases, in shadow mode
It processes the same patient cases doctors handle, without affecting care or decisions. The doctor works normally; your AI watches, predicts, and logs against the ground truth. Studies show AI models regularly underperform in populations they weren't trained on — this is where you find out.
Step 3 · Report
You get structured, regulatory-ready validation data
Every case scored against the physician's actual decision: accuracy, agreement rate, failure modes. Formatted for FDA 510(k), CE marking, and hospital procurement.
Architecture
Patient data never leaves the hospital server. Not policy. Not a promise. The architecture makes it impossible.
Speed
First validation report within 30 days of deployment. Not months. Not when the pilot wraps.
Real Cases
Your model runs on real patient cases in an active clinical setting. Not a curated benchmark set, not synthetic data — the kind regulators and procurement teams in the US, UK, and Australia actually require.
Regulatory Output
Reports structured for FDA 510(k), CE marking, UKCA (MHRA), TGA (Australia), and CDSCO — evidence your regulatory and procurement team can submit directly.
Currently running live shadow pilots with AI teams from 3 countries. Capacity for 2 additional teams this quarter.
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Early Partner
"We had trained on public radiology datasets for 18 months. We assumed the model would generalise. Garbha ran it in a live hospital for four weeks. It didn't — and we found out before our first enterprise customer did."
Founding team, Series A radiology AI company · 2025
4 wks
First validation report delivered
0
Patient records left the hospital
3
Critical failure modes found before launch
510(k)
FDA-ready report format delivered
Why Diagnostic AI Isn't Mature Enough

Your model is ready.
Your clinical validation isn't.

Common Blockers
Expert Access Takes Months
Validation Data is Hard to Get Right
Hospital Pilots Are a Black Hole

Healthcare AI teams spend years building and training models, then hit a wall when regulators, hospital procurement, or enterprise customers ask for real-world clinical evidence. Getting physicians to review model outputs at scale, sourcing diverse and compliant datasets, running structured benchmarks against actual clinical decisions, and accessing live hospital environments for shadow pilots: none of this has existed in a single place.

Until Garbha Labs.

Built for teams that can't afford to get validation wrong.

Garbha Live Sandbox

Shadow-mode deployment,
without the data movement.

Your AI runs inside real hospitals. The doctors work normally. You get structured, regulatory-ready performance data.

We deploy your model into a partner hospital's clinical workflow in shadow mode. It sees the same cases the hospital's own doctors see, in real time, but never affects care. Patient records never leave the hospital; only structured outputs do.

Live shadow deployment inside partner hospital workflows
Per-case structured scoring against the treating physician
Real-world performance benchmarking, not synthetic
Workflow friction and UX feedback from real users
Schedule a Demo
Garbha Live Sandbox

Garbha Live Sandbox in Action

Patient Chart
→
AI Validation
→
AI Developer
→
Dataset Export
Shadow Mode Complete Flow · Case GRB-2026-0047 2:39
01
Patient Chart
0:00
02
AI Validation
1:05
03
AI Developer
1:55
04
Dataset Export
2:17
Individual Portals
0:50
Patient Records
Triage list with severity filters, search, and one-click chart access for admitted patients.
1:45
Patient Chart & AI Shadow
Doctor documents diagnosis, labs, and meds. AI shadow model reveals its parallel predictions on finalize.
1:30
Peer Review Workflow
When the treating physician and the shadow AI disagree, the case is queued for structured review building a labelled signal of real-world model errors.
1:30
AI Developer Portal
Model performance dashboard, 3-way case comparison (doctor vs AI vs peers), version history, and NDJSON export.
1:21
Validation Dataset
Browse, filter, and annotate the curated training dataset. Quality check queue and one-click export for model retraining.
Who This Is For

You will have a validation report in 30 days. Or we tell you why not.

Tell us what you're building and we'll scope the pilot in one call: which hospital department, how many cases per week, what the output report covers, and the cost. If your model isn't the right fit for live shadow deployment yet, we'll say so.

Healthcare AI teams preparing for hospital procurement, enterprise sales, or regulatory submission (FDA, MHRA, TGA, CDSCO) who need real-world clinical validation evidence — not just benchmark performance on public datasets.
Teams whose models are trained on public or synthetic datasets and have not yet been validated on a real, diverse clinical population across geographies — including emerging markets where population-level data diverges most from Western training sets.
Founders and research leads who want to know how their model actually performs before a hospital CTO or procurement committee finds out.
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