Evaluate healthcare AI with rigorously verified medical professionals

Ensure outputs are safe, accurate, and validated before they reach patients. Access 20,000+ credentials-checked doctors, nurses, and professionals across 35+ specialties.
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What we mean by verified

We check the credentials and skills of every medical professional in our pool through a rigorous multi-step process.

01
ID and registry verification
Every participant must pass 110+ ID, IP, and response quality checks. We then check them against the GMC (UK), NPI (US), and equivalent registries in real time.
02
Specialty confirmation
We verify professionals according to their specific specialties and sub-specialties, from GPs to Neurosurgeons and Radiologists.
03
AI evaluation proficiency
Clinicians assess whether a model can reason as a trained professional would, across diagnostic reasoning, treatment review, triage assessment, and documentation quality.

How frontier labs use verified medical expertise

Each Prolific study is scoped to your requirements and delivered by medical professionals who have passed an independent assessment in the languages your task needs. 

Decision trajectory capture

Clinicians review AI-generated differentials, prior-authorization decisions, and triage recommendations.

Patient communication evaluation

AI-generated guidance rated for trustworthiness, tone, and clinical appropriateness before it reaches patients.

Clinical tone calibration

AI conversations are evaluated against clinical context and calibrated by clinicians who know what the moment requires.

Medical accuracy review

Diagnostic reasoning checked against clinical evidence, including real and synthetic case reports.

Safety evaluation

Outputs are flagged for potential harm, from inappropriate crisis responses to incorrect authorizations.

Specialty-specific evaluation

Specialist outputs are graded against the standard that a specialist in that field would expect.

What gets captured

When a verified medical professional works through a healthcare task, the submission is the entire working session rather than a single diff. 

Record the decision path: the approach they started with, the point where they abandoned it, each tool call and its result, test runs and their failures, retries, timing, and the edits in between. The final pull request is one row of signal. The session behind it is hundreds.


 

Features

How it is structured

Each session resolves into a sequence of steps a model can learn from: the state the medical professional saw, the action they took, and the outcome it produced, with enough context to reconstruct why. We can also capture their rationale at key decision points, turning a raw log into graded reasoning data.

What it unlocks

Process-level data supports work that outcome grading cannot: process reward modeling, where good intermediate decisions earn signal alongside good endings; agentic evaluation, where a run is judged against how a medical professional would have proceeded; and behavioral cloning, where models train on how experts actually work. 

Quality that holds at scale

Verification happens before the work: identity checks, credibility signals, and an independent assessment matched to your requirements. Review happens on every submission: behavioral cheat detection covers copy-paste, tab switching, plagiarism, and ChatGPT matching, with AI use blocked or permitted per task depending on your design. 

See our full approach to data quality.
In practice

1,200+ healthcare-targeted studies in the last 12 months

Clinical workflow simulation · Decision trajectory capture · Patient communication evaluation · Clinical tone calibration · Medical accuracy review · Safety evaluation · Specialty-specific evaluation
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How fast-moving AI teams use Prolific

Trusted by AI/ML developers, researchers, and leading organizations across industries.

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Scope your next healthcare study

Speak with our team about your requirements, or tell us your use case, and we will follow up with a relevant sample.
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