Course Outline

Foundations of Agentic AI for Healthcare

  • Agentic vs. tool-only LLM applications
  • Autonomy boundaries, policies, and human oversight
  • Healthcare data landscape and constraints (EHR, FHIR, PHI)

Designing Agent Workflows

  • Planning, memory, tool use, and reflection loops
  • Prompt engineering, functions/tools, and action selection
  • State management and orchestration patterns

Retrieval-Augmented Agents

  • Medical document ingestion and chunking
  • Embeddings, vector stores, and relevance evaluation
  • Grounding responses and citation strategies

Healthcare Integrations and Interoperability

  • FHIR/SMART basics for agent connectivity
  • Working with structured and unstructured clinical data
  • Eventing, APIs, and audit trails

Safety, Risk, and Governance

  • Guardrails, red-teaming, and fail-safe design
  • PHI handling, de-identification, and access controls
  • Human-in-the-loop review and escalation paths

Evaluation and Monitoring

  • Offline evaluations, golden sets, and KPI definition
  • Hallucination detection and factuality checks
  • Observability, logging, and cost/latency management

Deployment Patterns and Hands-on Lab

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

Summary and Next Steps

Requirements

  • An understanding of basic Python programming
  • Experience with data analysis or ML workflows
  • Familiarity with healthcare data concepts (e.g., EHR, FHIR)

Audience

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

Number of participants


Price per participant

Provisional Upcoming Courses (Require 5+ participants)

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