What is AI design certification?

We certify outputs: discrete, versioned, viewable artifacts — screens, flows, diagrams, data visualizations. We are not certifying that "this AI is good," but that "this specific artifact is fit for purpose under these conditions."

What passes and what fails is defined by a shared standard: safety, data integrity, regulatory fit, usability, language, bias, and how the flow works together. This is the certifiable surface layer.

Product vision

We certify AI-generated design outputs — screens, flows, copy, and visuals — through non-negotiable human review for safety, accessibility, bias, provenance, and regulatory fit. The result is a trusted design health layer that turns fast AI output into production-ready work teams can ship, defend, and buy.

That requires four things: a shared standard, human judgment at the right moment, verifiable proof, and tight integration into existing workflows.

Design Health Schema

A machine-readable rule set (YAML/JSON) defining the non-negotiables:

Certify outputs, not models

Certification attaches to a specific artifact version (screen, flow, copy, visual), its intended use, and its risk profile — not to the AI system that produced it.

Human-in-the-loop tuning

Domain experts — hello Juhan and Goinvo Design — adjust and validate AI outputs; their changes and rationale are captured as structured signals that improve future generations and reviews.

Verifiable certification

Issue a versioned, auditable certificate bound to an artifact hash, rubric version, reviewer, scope, and expiration. Clear for procurement, legal, and regulators.

Workflow-native integration

"Request certification" lives where work happens: GitHub PRs, Figma, design systems, CMSs. Certification as a button, not a process.

Risk-based gates

Hard pass/fail thresholds scale with risk (e.g., marketing site vs clinical intake). Higher risk = stricter gates, named reviewers, shorter cert lifetimes.

Drift and change detection

Any meaningful artifact change invalidates the cert and triggers re-review, which can prevent silent regression after approval.

Public signal, private audit trail

A simple badge teams can show externally, backed by a detailed internal record that stands up to scrutiny. Example: Project Crucible that measured EHR vendor compliance with FHIR.

Why it matters now

AI tools are generating healthcare UX at unprecedented speed. Cursor, Claude, and similar systems can produce working screens in minutes. But speed without accountability is dangerous in healthcare — where a single poorly-worded insight could prompt a patient to ignore symptoms that need clinical attention.

Existing review processes weren’t designed for artifact-level AI output. They assume a human designer made intentional choices about language, logic, and display. When AI produces the artifact, those assumptions break down. Certification closes that gap.

Real-world example: Apple Health

Using Apple Health as a concrete summary.

1. What the AI does

The app generates short health insight cards from your data (sleep, heart rate, activity). It’s making clinical guidance from patient data.

Example: "Your resting heart rate is higher this week and your sleep is down. Consider resting today."

This is copy + logic + UI, not "the model." And, this is medical advice delivered by software.

2. What gets certified

Before shipping, the team submits the actual outputs:

• The text users will read
• The rules that trigger it
• The data sources used
• How it’s shown (color, urgency, notifications)

In this case, it’s the medical artifact being approved.

3. Non-negotiable checks

Each output is reviewed against a standard schema:

• Safety: No diagnosis, clear uncertainty, calm tone (or appropriate thresholds, false-positive control)
• Scope of Practice: What the software may and may not assert
• Bias: Works across age, sex, medications, and baselines
• Accessibility: Plain language, readable, screen-reader safe (clinician and patient-readable rationale)
• Data provenance: Users can see what data drove the insight
• Regulatory fit: Wellness guidance, not medical advice
• Usability: Actionable, not alarming
• Human escalation: When and how a clinician must intervene

Some checks are automated. All are human-verified.

4. Human review catches problems

If we're in the wellness world, this must be rewritten to a safe, non-diagnostic version before approval. If we're in the medical world, we'll need to:

• Narrow scope
• Add uncertainty
• Trigger clinical follow-up — not just self-action

5. What "Certified" means

Approval issues a verifiable medical certificate tied to:

• The exact wording
• The logic used
• The UI shown
• The regulatory claim made

Once approved, the insight gets a verifiable certificate tied to that exact copy, logic, and UI.

• It ships with the app
• It’s auditable later
• If something goes wrong, you know what was approved and why

6. What approval looks like

7. The point

Treat AI outputs as the medical devices at the UI layer.
You're not trusting an AI model.
You're certifying the health insight itself,
the thing a human actually sees,
so teams can move fast and stay safe.

How it works in your workflow

Certification lives where work happens. A "Request Certification" button embedded in GitHub pull requests, Figma, design systems, and CMSs. Certification as a button, not a process.

Risk-based gates scale thresholds with context: a marketing site and a clinical intake form are not the same artifact. Higher risk means stricter gates, named reviewers, and shorter certificate lifetimes.

We’re building this in the open:

If you’ve got a real or prototype healthcare product, let’s together run it through the cert so we can pressure-test it on reality.

Mark Begale for gently tapping the AI Certification idea into our skulls, and to Venus Wong for volunteering as our first certification partner.

About GoInvo

GoInvo is a healthcare design company that crafts innovative digital and physical solutions. Our deep expertise in Health IT, Genomics, and Open Source health has delivered results for the National Institutes of Health, Walgreens, Mount Sinai, and Partners Healthcare.

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