Readiness checklist for safer health AI evaluation.
Toggle the controls your prototype already has. Healthy AI calculates a deterministic readiness score, shows gaps, and creates a review plan without calling any external AI or scoring API.
Move from scattered concerns to a clear review-ready path.
A focused sequence helps teams see what is safe to check now, what needs evidence, and what must stay blocked.
- Clinical caution
- Team alignment
- Action plan
Limitation: this is not medical advice or safety certification.
Healthy AI helps builders organize readiness evidence before controlled evaluation. It does not diagnose, treat, triage, clear a product for clinical use, certify model safety, or replace legal, regulatory, clinical, security, or privacy review.
Do not start external evaluation until the critical controls are closed.
- Controls
- 2/10
- Critical gaps
- 5
- AI calls
- 0
Keep this in an internal sandbox until governance, clinical risk, privacy, and escalation gaps are closed.
Use toggles to model current readiness.
10 controls required before health AI evaluation.
Health builders must keep users from interpreting prototype output as clinician-grade guidance.
Health AI prototypes need an accountable human gate before testing moves beyond internal review.
High-risk health interactions need deterministic handoff language instead of generated improvisation.
Readiness decisions need reproducible evidence, not impressions from a few successful demos.
Health AI teams need explicit accountability before a prototype moves from demo to controlled evaluation.
Different health workflows require different review depth, escalation language, evidence, and deployment restrictions.
Averages can hide unsafe gaps for small cohorts, uncommon scenarios, or users with different access needs.
Health AI risk changes after deployment when real users, fresh data, and operational pressure appear.
Health prototypes can collect sensitive data before the product is mature enough to protect it.
Even internal pilots need a way to stop, triage, and learn from unsafe or confusing output.
8 readiness gaps
Close these first because they define who can approve the prototype, what clinical risk it carries, what evidence supports it, and how rollout can be stopped.
Name the reviewer, define their sign-off checklist, and require approval before any external pilot.
Define escalation triggers, emergency copy, support ownership, and a hard stop for out-of-scope scenarios.
Create a governance record with owners, intended-use boundaries, excluded uses, approval cadence, and change-control rules.
Classify the workflow, document excluded clinical scenarios, and route higher-risk use cases to qualified clinical and regulatory review.
Define validation slices, sample minimums, acceptance thresholds, and reviewer notes for failures and borderline cases.
Define staged rollout gates, production monitors, alert owners, rollback triggers, and post-launch review cadence.
Document what data is allowed, what is prohibited, how it is stored, who can access it, and when it is deleted.
Assign an incident owner, define severity levels, and connect feedback reports to the release-blocking review queue.
Required next actions
- 1Name the reviewer, define their sign-off checklist, and require approval before any external pilot.
- 2Define escalation triggers, emergency copy, support ownership, and a hard stop for out-of-scope scenarios.
- 3Create a governance record with owners, intended-use boundaries, excluded uses, approval cadence, and change-control rules.
- 4Classify the workflow, document excluded clinical scenarios, and route higher-risk use cases to qualified clinical and regulatory review.
- 5Define validation slices, sample minimums, acceptance thresholds, and reviewer notes for failures and borderline cases.
- 6Define staged rollout gates, production monitors, alert owners, rollback triggers, and post-launch review cadence.
- 7Document what data is allowed, what is prohibited, how it is stored, who can access it, and when it is deleted.
- 8Assign an incident owner, define severity levels, and connect feedback reports to the release-blocking review queue.
- No named reviewer means there is no accountable human gate for risky model behavior.
- Missing escalation creates a high-risk path where urgent users may stay inside the prototype.
- Without governance records, teams can expand scope faster than reviewers can assess patient, privacy, or compliance risk.
- Unclassified clinical risk makes a low-stakes prototype look equivalent to a patient-impacting workflow.
- Do not use real health data until privacy, retention, and access controls are explicitly documented.
- This score is builder readiness only. It is not medical advice, regulatory clearance, clinical safety validation, or a certification.