Agentic AI in Healthcare Training Course
Agentic AI refers to a methodology where artificial intelligence systems autonomously plan, reason, and utilize tools to achieve specific objectives within established boundaries.
This guided, live training session (available online or in-person) is designed for intermediate-level healthcare and data professionals looking to design, assess, and manage agentic AI solutions for both clinical and operational scenarios.
Upon completing this training, participants will be able to:
- Articulate the core concepts and limitations of agentic AI within healthcare environments.
- Create secure agent workflows incorporating planning, memory capabilities, and tool integration.
- Develop retrieval-augmented agents that leverage clinical documents and knowledge bases.
- Assess, monitor, and govern agent behavior using safety guardrails and human-in-the-loop controls.
Course Format
- Interactive lectures combined with facilitated group discussions.
- Guided laboratory exercises and code walkthroughs conducted in a sandbox environment.
- Scenario-based activities focusing on safety protocols, evaluation methods, and governance frameworks.
Customization Options
- To request a tailored training version of this course, please contact us to make arrangements.
Course Outline
Foundations of Agentic AI for Healthcare
- Distinguishing agentic systems from tool-only LLM applications
- Defining autonomy boundaries, policies, and requirements for human oversight
- Overview of the healthcare data landscape and constraints (EHR, FHIR, PHI)
Designing Agent Workflows
- Implementing planning, memory, tool usage, and reflection loops
- Techniques for prompt engineering, function/tool invocation, and action selection
- Patterns for state management and orchestration
Retrieval-Augmented Agents
- Processing medical documents through ingestion and chunking strategies
- Utilizing embeddings, vector stores, and methods for relevance evaluation
- Strategies for grounding responses and citation
Healthcare Integrations and Interoperability
- Basics of FHIR and SMART for agent connectivity
- Managing structured and unstructured clinical data
- Handling eventing, APIs, and maintaining audit trails
Safety, Risk, and Governance
- Implementing guardrails, conducting red-teaming, and designing fail-safe mechanisms
- Handling PHI, de-identification techniques, and access control measures
- Facilitating human-in-the-loop reviews and establishing escalation paths
Evaluation and Monitoring
- Conducting offline evaluations, utilizing golden datasets, and defining KPIs
- Techniques for hallucination detection and factuality verification
- Ensuring observability, logging, and managing cost/latency
Deployment Patterns and Hands-on Lab
- Comparing API-based versus on-premises model options
- Building a retrieval-augmented agent using LangChain, FastAPI, and ChromaDB
- Simulating incident response and practicing rollback procedures
Summary and Next Steps
Requirements
- Foundational knowledge of Python programming
- Practical experience with data analysis or machine learning workflows
- Familiarity with healthcare data standards (e.g., EHR, FHIR)
Target Audience
- Data scientists and ML engineers in the healthcare sector
- Clinical informatics and digital health product teams
- IT leaders and innovation managers within healthcare organizations
Open Training Courses require 5+ participants.
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