BetterHealthBenchIndependent, continuous evaluation of clinical AI. Does the tool perform safely on real healthcare workflows? Has that performance changed? We measure it. We publish it.
Credit ratings. Drug trials. Financial audits.
Every regulated industry has independent evaluation.
Healthcare AI has none.
Models score at physician level on medical exams but fail on the workflow tasks deployment actually requires. Static benchmarks measure knowledge recall. Clinical safety depends on judgment, information gathering, and knowing when to escalate. The measurement problem is as important as the modeling problem.
How it works
Submit your model endpoint.
Centralized execution under identical conditions. Vendors never see the test prompts. No model gets special treatment. The evaluation suite is tailored to what your tool does — a scribe gets scribe benchmarks, a CDS tool gets triage and diagnosis.
Run 396 clinical scenarios.
Multi-turn triage, differential diagnosis, clinical summarization, ambient scribe evaluation, and adversarial safety cases, all scored against physician-defined rubric criteria.
Score every response.
Dual scoring: transparent regex for reproducibility, LLM-as-judge for nuance. Worst-of-K reliability because a single critical failure at 3am matters more than a high average.
Publish results monthly.
Signed, tamper-evident reports. Longitudinal tracking across model versions. Drift detection catches silent regressions before patients do.
Who needs this
Clinical AI Vendors
Earn the badge.
Independent evaluation closes enterprise deals that pilot programs cannot. Procurement committees need evidence for governance boards. A BetterHealthBench report is that evidence.
Health Systems
Deploy with evidence.
A tool that performs well on average may fail on your patient populations, your clinical protocols, your institutional policies. Evaluation that tests against your operational reality, not generic benchmarks.
Healthcare Payers
Price the risk.
Provider networks are deploying AI in care delivery. You carry the liability exposure. Continuous, independent performance measurement is the foundation for clinical AI coverage and risk pricing.
Regulators & Standards Bodies
Build the standard.
CTA, URAC, Health Canada, MLCommons are all building health AI evaluation frameworks. None have a concrete multi-turn methodology yet. We are filling that gap.
How we compare
| Capability | BetterHealthBench | HealthBenchOpenAI | MedHELMStanford |
|---|---|---|---|
| Multi-turn conversation evaluation | Yes | No | No |
| 10-run reliability testing (worst-of-K) | Yes | No | No |
| Contamination detection + embargo tiers | Yes | No | No |
| Longitudinal drift tracking | Yes | No | No |
| Structurally independent | Yes | No | Yes |
| Deployed clinical tool evaluation | Yes | No | No |
| Peer-reviewed methodology | No | Yes | Yes |
| 48K+ rubric criteria | No | Yes | No |
Neutrality is the product.
Structural independence by design, not intent. Vendors submit endpoints. They never see the prompts.
The database compounds.
Every evaluation deepens contamination detection and drift baselines. The 50th company benefits from the 49 before it.
The standard-setting flywheel.
Credibility earns inclusion, inclusion earns data, data earns authority. Authority earns more inclusion.
BetterHealthBench
Evaluation infrastructure for clinical AI, built to earn the trust it carries.