Healthcare professionals increasingly interact with AI systems that retrieve evidence, call external tools, synthesize information, and recommend next steps. Effective AI education therefore requires more than teaching prompt engineering. Clinicians, researchers, and students must learn how to evaluate where an AI answer came from, whether its citations support its claims, how external tools were used, and when human verification is required.
This session presents a practical, case-based framework for building AI evidence literacy in the healthcare workforce. Using an evidence-grounded healthcare agent as a teaching environment, participants learn to inspect retrieval, provenance, evidence quality, citations, uncertainty, and agent behavior. The session proposes a competency model for preparing healthcare professionals to use agentic AI critically, safely, and effectively.