Agentic AI in Healthcare Training Course
Agentic AI refers to a methodology where AI systems are capable of planning, reasoning, and utilizing tools to achieve specific objectives within established boundaries.
This instructor-led live training, available both online and onsite, is designed for intermediate-level healthcare and data professionals looking to design, evaluate, and govern agentic AI solutions for clinical and operational scenarios.
Upon completion of this training, participants will be equipped to:
- Articulate the concepts and constraints of agentic AI within healthcare environments.
- Design safe agent workflows incorporating planning, memory capabilities, and tool usage.
- Develop retrieval-augmented agents using clinical documents and knowledge bases.
- Evaluate, monitor, and govern agent behaviour through the implementation of guardrails and human-in-the-loop controls.
Course Format
- Interactive lectures coupled with facilitated discussions.
- Guided labs and code walkthroughs conducted in a sandbox environment.
- Scenario-based exercises focusing on safety, evaluation, and governance.
Customization Options
- To request customized training for this course, please reach out to us to make arrangements.
Course Outline
Foundations of Agentic AI for Healthcare
- Differences between agentic applications and tool-only LLM applications.
- Autonomy boundaries, policies, and human oversight mechanisms.
- The healthcare data landscape and its constraints, including EHR, FHIR, and PHI.
Designing Agent Workflows
- Planning, memory, tool use, and reflection loops.
- Prompt engineering, functions/tools, and action selection strategies.
- State management and orchestration patterns.
Retrieval-Augmented Agents
- Ingestion and chunking of medical documents.
- Embeddings, vector stores, and relevance evaluation techniques.
- Grounding responses and citation strategies.
Healthcare Integrations and Interoperability
- Basics of FHIR/SMART 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 versus on-prem model choices.
- Building a retrieval-augmented agent using LangChain, FastAPI, and ChromaDB.
- Simulated incident response and rollback procedures.
Summary and Next Steps
Requirements
- Basic understanding of Python programming.
- Experience with data analysis or machine learning (ML) workflows.
- Familiarity with healthcare data concepts, such as EHR and FHIR.
Target Audience
- Healthcare data scientists and ML engineers.
- Clinical informatics and digital health product teams.
- IT leaders and innovation managers within the healthcare sector.
Need help picking the right course?
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Agentic AI in Healthcare Training Course - Enquiry
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