Eval directory
Evals for Aidoc
6 evaluation packs covering adversarial robustness, safety gates, workflow quality, and operator-level checks for Aidoc AI products.
About Aidoc
Aidoc is a clinical AI company whose aiOS platform analyzes and aggregates medical data to help care teams operationalize clinical workflows. Its solutions began in radiology and now support broader health-system care delivery.
Industry
Clinical AI
Website
www.aidoc.comHow 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 Aidoc
6 packs ready to run.
Ai Detection Multi Condition Inference
54 graded scenarios covering edge cases, failure modes, and quality checks.
Image Ingestion Dicom Pipeline
48 graded scenarios covering edge cases, failure modes, and quality checks.
Notification Alerting Escalation Delivery
56 graded scenarios covering edge cases, failure modes, and quality checks.
Result Output Pacs Ris Write Back
60 graded scenarios covering edge cases, failure modes, and quality checks.
Study Eligibility Indication Gating Routing
63 graded scenarios covering edge cases, failure modes, and quality checks.
Triage Worklist Prioritization
67 graded scenarios covering edge cases, failure modes, and quality checks.
Why eval Aidoc AI
Aidoc'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 Aidoc 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 Aidoc's public product surface and runnable in Corsac with your own data.