Healthcare middleware analyzing diagnostic AI performance across demographic groups using explainable AI. Provides real-time disparity alerts, root-cause analysis, and compliance reporting for hospitals, public health agencies, and medical AI developers.
EquiScreen AI addresses algorithmic bias in medical diagnostics by offering real-time monitoring of AI performance across patient demographics. Designed for large hospital networks and public health regulators, this DICOM-compatible layer integrates between PACS systems and diagnostic AI, providing actionable insights while maintaining HIPAA/GDPR compliance.
Core Functionality
Acts as middleware between PACS and diagnostic AI systems, using explainable AI techniques to:
- Monitor performance disparities across age, gender, and ethnicity
- Generate real-time alerts for statistically significant deviations
- Provide root-cause analysis tools with SHAP values
- Automate compliance reporting for EU AI Act and FDA standards
Target User and Segment
- Primary: 500+ bed hospitals using third-party diagnostic AI
- Secondary: Public health agencies monitoring screening equity
- Developers: Medical AI teams requiring bias validation
Recommended Tech Stack
- Python ML stack (TensorFlow Extended + SHAP)
- FHIR/DICOM API gateways
- AWS HealthLake for HIPAA-compliant storage
- React-based clinician dashboard
- OAuth2/OpenID Connect integration
Estimated MVP Costs
550 development hours (€55,000-€65,000) covering:
- Backend development: 300h
- Compliance engineering: 150h
- DICOM integration testing: 100h
SWOT Analysis
- Strengths: First regulatory-ready bias monitoring solution
- Weaknesses: Dependency on hospital data-sharing agreements
- Opportunities: CMS equity incentives expansion
- Threats: PACS vendors developing competing features
First 1000 Customers Strategy
€240 target CPA through:
- 10 hospital pilot programs (€0 CAC)
- Medical AI conference demos (€15k/event)
- Co-branded webinars with radiology associations
Monetization
- Model: Per-study SaaS pricing + certification fees
- Pricing: €0.12/imaging study (€9,600 avg hospital/year)
- Break-even: 42 hospital contracts at €20k ARPA
- Team: 5 FTEs (clinical ML engineers + regulatory specialists)
Market Positioning
- TAM: €680M in EU/US hospital AI monitoring
- Competitors: Aidence QA Suite (€45M funded)
- Edge: Granular disparity tracking in workflows
- Strategy: Launch in Germany/France first
