CLINICAL AI PRODUCT VALIDATION & TESTING
Clinical AI validation
by practicing clinicians.
Clinical AI can look convincing long before it is clinically reliable. Internal testing tells you the system runs. It cannot tell you whether a practicing clinician considers an answer accurate, complete, useful and safe in the context where it will actually be used.
Validation built for FDA and EU AI Act requirements.
20K+
Healthcare professionals
in network
140+
Countries
represented
100+
Specialties
200+ sub-specialties
55+
Languages
supported
BUILT AROUND THE DECISIONS YOU HAVE TO MAKE
Medcase builds clinician evaluation programs around the decision your team needs to make: whether a feature is ready to progress, what failure modes remain, and which changes matter most to the people who will use it.
Synthetic benchmarks are useful and incomplete. Practicing clinicians ask questions with shorthand, missing context and workflow-driven intent. Real-user evaluation exposes ambiguity, poor prioritization, missing detail and usability problems that benchmark prompts do not reach.
A list of bad answers is not a deliverable. A reproducible issue set is.
Real-world queries surface different failures
WHAT WE EVALUATE
STRUCTURED EVALUATION
RATINGS + REASONING
Conversational clinical decision support, documentation tools, diagnostic aids, imaging AI, medication-support systems, clinical search, patient-facing medical content and other healthcare AI workflows.
↓ What you get back
Structured ratings and qualitative explanation together — measurable results and the clinical reasoning behind them.
CLINICAL RED-TEAMING
ADVERSARIAL CLINICAL TESTING
Healthcare AI fails differently from general software. A response can be fluent, plausible and still omit a red flag, mishandle a medication, overstate certainty or give the wrong escalation advice. Security-focused red-teaming does not catch this; clinicians do.
↓ What you get back
Each accepted issue with model context, failure category, severity, clinician explanation and enough provenance to reproduce it.
MEDICAL CONTENT VERIFICATION
ACCURACY · RELEVANCE · LOCALIZATION
Where the output is medical information rather than a clinical recommendation, the same network reviews it for factual accuracy, clinical relevance, missing context, inappropriate certainty, outdated guidance and localization.
↓ What you get back
A rubric that distinguishes objective error from reasonable difference in clinical judgment — without it, review produces noise.
WORK WE HAVE RUN
Proof at the scale
you are probably planning for.
CLINICAL PUBLISHER —
DECISION SUPPORT
The program:
450+ healthcare professionals across the US, Latin America and Europe, evaluating 10,000+ clinical queries in five languages.
↓ How it ran
Clinicians used an AI-powered decision-support tool in real work and gave structured quantitative and qualitative feedback on relevance, completeness and accuracy.
GLOBAL SEARCH ENGINE — MISINFORMATION
The program:
Large-scale multi-language medical content auditing, run to counter medical misinformation.
↓ How it ran
An on-call physician workflow operating to defined service levels, alongside the standing audit.
COHORT DESIGN
IS THE PROTOCOL
The program:
A generic physician panel rarely answers the question. Cohorts are defined by provider role, specialty, sub-specialty, geography, language and active clinical context.
↓ How it ran
The people evaluating the product resemble the people expected to use it. Where seniority, market or specialty is likely to change performance, the study compares those groups rather than averaging across them.
How a program runs.
02
Define the reviewer profile and calibration standard.
04
Run structured evaluation against the agreed protocol.
06
Deliver quantitative results, clinician commentary and an audit-ready record of the work performed.
01
Scope the clinical use case and the decision the evaluation must support.
03
Recruit and brief the cohort.
05
Apply quality review and resolve inconsistent judgments.
Most teams start with a focused pilot to test the protocol and cohort assumptions, then expand across regions, languages and provider types on the same measurement framework.
Built for
regulated work
HIPAA-compliant workflows, ISO 27001 &
FDA & EU AI Act aligned
HIPAA-compliant workflows, ISO 27001 information-security controls, every project scoped before launch, and a documented record of who performed the review under which protocol. Validation built for FDA and EU AI Act requirements.
We generate clinician-led validation evidence and documented review outputs. We do not provide regulatory approval — regulatory strategy remains with you and the relevant authorities.
Frequently asked questions
Yes. That is exactly what the 450-clinician publisher program was: 10,000+ clinical queries evaluated across three regions in five languages.
Yes. The reference program covered the US, Latin America and Europe in five languages, across 140+ countries of network coverage.
Yes, and most teams should. A focused pilot refines reviewer criteria, rubrics and operating assumptions before scaling.
The publisher program was real-workflow evaluation. The use setting has to be approved for your specific product and study.
Where you hold the necessary rights, a validated issue set can inform internal regression testing or benchmark development.
Yes. Structured ratings and detailed clinician commentary come back together, categorized by failure type and severity.
We generate clinician-led validation evidence and documented review records built for those requirements. We do not provide regulatory approval — regulatory strategy stays with you and the relevant authorities.
Benchmarks tell you how the model performs against fixed prompts. Practicing clinicians tell you how it performs against real clinical intent, and which failures would matter at the point of care.
Ask the eleven who know,
not the five hundred who answered.
Tell us what you're building — we'll tell you exactly how we can help. Bring one AI feature, one target user group and one quality question you need answered, and we'll translate that into a clinician evaluation pilot and a path to scaled validation.
Other services
Managed end to end — recruitment, credentialing, onboarding, training, scheduling, payments and QA. One accountable team.
Medical data annotation & labeling
Specialty-matched annotation of medical imaging, clinical text, EHR, video, waveform and audio data for AI training. Every label is placed by a licensed HCP matched
to the data.
Expert network access (interviews, surveys)
On-demand access to HCPs for interviews, surveys, advisory input, protocol review and KOL engagement. Any specialty, any geography, any language.