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Course Outline

Foundations of Agentic AI for Healthcare

  • Distinguishing agentic systems from tool-only LLM applications
  • Defining autonomy boundaries, policies, and human oversight
  • Understanding the healthcare data landscape and constraints (EHR, FHIR, PHI)

Designing Agent Workflows

  • Integrating planning, memory, tool use, and reflection loops
  • Leveraging prompt engineering, functions/tools, and action selection
  • Implementing state management and orchestration patterns

Retrieval-Augmented Agents

  • Ingesting and chunking medical documents
  • Utilizing embeddings, vector stores, and relevance evaluation
  • Establishing grounding for responses and citation strategies

Healthcare Integrations and Interoperability

  • Basics of FHIR/SMART for agent connectivity
  • Managing structured and unstructured clinical data
  • Handling eventing, APIs, and audit trails

Safety, Risk, and Governance

  • Implementing guardrails, red-teaming, and fail-safe designs
  • Managing PHI, de-identification, and access controls
  • Facilitating human-in-the-loop review and escalation paths

Evaluation and Monitoring

  • Conducting offline evaluations, establishing golden sets, and defining KPIs
  • Detecting hallucinations and performing factuality checks
  • Ensuring observability, logging, and managing cost/latency

Deployment Patterns and Hands-on Lab

  • Comparing API-based vs. on-prem model choices
  • Building a retrieval-augmented agent using LangChain, FastAPI, and ChromaDB
  • Executing simulated incident response and rollback procedures

Summary and Next Steps

Requirements

  • A foundational understanding of basic Python programming
  • Prior experience with data analysis or machine learning workflows
  • Familiarity with healthcare data concepts (e.g., EHR, FHIR)

Target Audience

  • Healthcare data scientists and ML engineers
  • Clinical informatics and digital health product teams
  • IT leaders and innovation managers in the healthcare sector
 14 Hours

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