Eval directory
Evals for Glass Health
6 evaluation packs covering adversarial robustness, safety gates, workflow quality, and operator-level checks for Glass Health AI products.
About Glass Health
Glass Health is an AI ambient-scribing and clinical-decision-support platform. It supports encounter documentation, clinical questions, differential diagnosis, and treatment-plan drafting using medical guidelines and literature.
Industry
Healthcare AI / Clinical Decision Support
Website
glass.healthHow complete this published benchmark library is across datasets, metrics, rubrics, use-case maps, and pack context. This is library coverage, not an agent performance score.
Test datasets
6/6 packs
Scoring metrics
6/6 packs
Judge rubrics
6/6 packs
Use-case maps
0/6 packs
Pack context
6/6 packs
Available eval packs for Glass Health
6 packs ready to run.
Ambient Audio Capture Recording
54 graded scenarios covering edge cases, failure modes, and quality checks.
Case Intake Clinical Input Capture
75 graded scenarios covering edge cases, failure modes, and quality checks.
Clinical Note Structured Documentation Generation
63 graded scenarios covering edge cases, failure modes, and quality checks.
Diagnostic Reasoning Differential Generation
56 graded scenarios covering edge cases, failure modes, and quality checks.
Speaker Diarization Multi Speaker Attribution
58 graded scenarios covering edge cases, failure modes, and quality checks.
Speech Recognition Transcription
Transcription Accuracy72 graded scenarios covering edge cases, failure modes, and quality checks.
Why eval Glass Health AI
Glass Health's AI features ship behind brand promises about accuracy, safety, and reliability. Buyers and integrators need to know those promises hold up under adversarial prompts, edge-case workflows, and the long tail of real customer inputs — not just the demo path.
The Corsac eval library for Glass Health measures four dimensions teams care about most when deploying medical & clinical ai agents:
- Adversarial robustness — does the agent resist prompt injection, jailbreaks, and social-engineering attempts?
- Workflow quality— does it complete the task buyers were shown in the demo, on inputs that don't look like the demo?
- Safety gates — does it escalate or refuse when it should, and only then?
- Operator quality — does it preserve analyst trust by surfacing the right context at the right time?
Every eval pack above is hand-authored against Glass Health's public product surface and runnable in Corsac with your own data.