As patients increasingly use generative AI to discover providers, services, and health information, healthcare search is shifting from ranked links toward AI-generated recommendations. But healthcare discovery requires more than fluent answers—it requires trustworthy evidence, source transparency, and traceability.
This session presents an evidence-grounded agentic AI framework that decomposes patient questions into targeted searches, retrieves healthcare provider data and biomedical literature from authoritative sources, ranks evidence, and generates responses grounded in verifiable citations. Using a working healthcare discovery prototype, the session demonstrates how specialized AI agents can separate planning, retrieval, evidence ranking, and response generation. The goal is to explore how agentic AI can support a more transparent and trustworthy future patient journey while reducing the risks of hallucination and unsupported recommendations.