AI symptom checker for healthcare: demo
Watch how to build a fully guarded AI symptom checker in minutes, with real-time PII detection that blocks patient data before it reaches the model.
What's in this video
- Build an AI symptom checker from a healthcare workflow.
- Add guardrails that inspect requests for listed personal data.
- Watch the system stop a request before patient data reaches the model.
Frequently asked questions
Topics from this video and why governed AI matters.
- How does the guarded symptom checker work?
- Patients describe how they feel and add age and gender; in seconds they get evidence-based guidance (emergency, urgent, routine, or self-care). Sentinel replaces email addresses and phone numbers; Social Security, passport, driver's license, tax and national ID numbers; credit card, IBAN, and bank account numbers; IP and MAC addresses; VINs; API keys and other secrets; and dates of birth and medical record numbers when they are labeled. It does not cover names, street addresses, other dates, or unlabeled record numbers. For requests routed through Sentinel, the audit log records request metadata.
- Why must zero PHI touch the model in healthcare AI?
- HIPAA and trust require that PHI never reaches the LLM. Best practice is to detect and block PII at the prompt level before data leaves your control. SUPERWISE guardrails strip or block identifiable data before inference, and every decision is recorded, giving health systems a defensible audit trail.
- What does the clinical dashboard show?
- Every assessment is saved with full context, timestamps, and red flags highlighted. Analytics cover distribution, trends, and export for compliance and care improvement. Governed AI needs this visibility so clinicians and compliance teams see what the system did and why.
- Why is transparency non-negotiable for symptom checkers?
- Patients and clinicians need to understand why a triage decision was made. SUPERWISE keeps every decision auditable and blocks PII by default. That combination is what makes AI safe for telehealth and population health.
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