Agentic AI in Healthcare Training Course
Agentic AI is an approach where AI systems plan, reason, and take tool-using actions to accomplish goals within defined constraints.
This instructor-led, live training (online or onsite) is aimed at intermediate-level healthcare and data teams who wish to design, evaluate, and govern agentic AI solutions for clinical and operational use cases.
By the end of this training, participants will be able to:
- Explain agentic AI concepts and constraints in healthcare contexts.
- Design safe agent workflows with planning, memory, and tool usage.
- Build retrieval-augmented agents over clinical documents and knowledge bases.
- Evaluate, monitor, and govern agent behavior with guardrails and human-in-the-loop controls.
Format of the Course
- Interactive lecture and facilitated discussion.
- Guided labs and code walkthroughs in a sandbox environment.
- Scenario-based exercises on safety, evaluation, and governance.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
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
Open Training Courses require 5+ participants.
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