AI in Healthcare Apps Why Privacy Is Not Optional | Episode 12 | Agentic Enterprise

AI is becoming central to modern healthcare and wellbeing apps.
But when applications handle sensitive health data, privacy is not optional.

In Episode 12 of the Agentic AI series, we explain why private LLMs combined with RAG architecture are essential for healthcare and wellbeing applications — and why public AI models are not suitable for this category.

Healthcare apps handle some of the most sensitive data possible, including menstrual cycles, sleep patterns, stress levels, vitals, medication history, mood journals, and long-term symptoms. This data falls under PHI — Protected Health Information.

This episode covers:
• Why healthcare data requires stricter AI boundaries
• Why redaction and anonymization break personalization
• Why public LLMs with wrappers are still unsafe for PHI
• The risks of data retention logging and leakage
• Why compliance with HIPAA and GDPR matters
• What private LLMs actually mean in healthcare apps
• How RAG enables personalization without data exposure
• How embeddings represent user health history securely
• The role of vector databases inside a private VPC
• Validation layers to prevent hallucinations and unsafe advice

We walk through a real example showing how AI can respond to sensitive health questions using historical context — without ever sending user data outside the application’s secure environment.

In healthcare, trust is everything.
And trust starts with privacy by design.

Watch Episode 12 to understand how AI must be built for healthcare applications.


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