The fairness layer for clinical AI
Sit underneath every AI-assisted care decision. Audit in minutes via HL7 FHIR R4. Catch bias at the point of care before it becomes a patient outcome.
Algorithmic bias determines the clinical outcome.
Two Tracks: What We Do Today, and What We Work to Prevent
Track A: Available Today • Healthcare AI Governance
A hospital data analyst executes DDFA on de-identified clinical datasets and model logs. DDFA identifies that training data systematically under-represents Black, Indigenous, and older patients, issuing an actionable representation-gap audit report before point-of-care deployment.
Track B: The Scenario We Work to Prevent • The Alice & Amara Disparity
Same hospital • Same acute appendicitis • Same AI triage tool:
- Alice (White, 7): Pain score 7, receives timely emergency analgesia, seen and discharged safely within 3–4 hours.
- Amara (Black, 7): Pain score 7, sent home with an over-the-counter script due to algorithm undertriage, readmitted within 6 hours for unmanaged acute pain.
The 3-Layer Governance Framework
Governance-First Entry
Assess your baseline without requiring IT integration. Our 15-minute Ethical AI Maturity Assessment (EAMA) reveals institutional governance gaps and maps them to concrete mitigation strategies.
Sidecar Retrospective Wedge
Audit your existing tools on historical data securely. We analyze your de-identified model logs in 30 days to expose demographic performance variations before deploying at the point of care.
HAIQ Governance Platform
Embed accountability into the clinical workflow. The Data Diversity & Fairness Auditor (DDFA) integrates directly via HL7 FHIR R4 to assess AI recommendations in minutes.
Purpose-Built for Vendors & Hospital Networks
Healthcare AI Vendors
Build trust and pass hospital procurement ethics reviews.
- ✔ Validate algorithms against our proprietary Composite Fairness Index (CFI).
- ✔ Earn the Synod Certified Fairness (SCF) Trust Standard to differentiate your software.
- ✔ Generate automated audit documentation for ACA Section 1557 and EU AI Act compliance.
Hospital Networks & Systems
Protect patient safety and satisfy regulatory oversight.
- ✔ Detect demographic performance variations across installed AI data and models.
- ✔ Automate compliance reporting for hospital AI steering committees and risk managers.
- ✔ Prevent adverse events and capture cost avoidance in readmissions.
Stay in the Know: Latest Publications & Events
AC:Health - Healthcare Innovation Program
Participant in AC:Health - Healthcare Innovation Program. AC:Health supports founders building AI, data, and software-enabled solutions that improve healthcare delivery, patient outcomes, and system efficiency.
Learn More →DaRMoD - Vector Institute for Artificial Intelligence
Participant in Darmod. Vector’s Data Readiness, Model Development, and Model Deployment (DaRMoD) program guides startups and small businesses from an AI idea to prototype in four months.
Learn More →Sheridan CAAI Institutional Research Collaboration
Our latest milestone in institutional validation, research collaboration, and algorithm benchmarking in partnership with Sheridan CAAI.
Learn More →The Invisible Patient: What Healthcare Has Always Known About Bias and What AI Must Learn
August 28, 2026 • By Lorna McKenzie, Retired RN
A retired primary care nurse manager on what twenty years in community health taught her about systemic bias, and why clinical AI without fairness auditing risks formalizing the inequities healthcare has always known.
Build the governance layer for your clinical AI.
Connect with our team to audit your AI tools and ensure equitable, safe care for every patient population.
Schedule a 30-Minute Call →