Agentic AI in Healthcare Training Course
Agentic AI represents a methodology where artificial intelligence systems autonomously plan, reason, and execute actions using various tools to achieve specific objectives within established boundaries.
This instructor-led training session, available both online and on-site, is designed for healthcare and data teams at an intermediate level. It focuses on helping participants design, assess, and govern agentic AI solutions tailored for clinical and operational scenarios.
Upon completing this training, participants will be equipped to:
- Articulate the core concepts and limitations of agentic AI within the healthcare sector.
- Create secure agent workflows incorporating planning, memory management, and tool integration.
- Develop retrieval-augmented agents capable of processing clinical documents and knowledge bases.
- Assess, monitor, and govern agent behavior through the implementation of guardrails and human-in-the-loop oversight.
Course Format
- Interactive lectures accompanied by facilitated discussions.
- Guided laboratory exercises and code walkthroughs conducted in a sandbox environment.
- Scenario-based activities focusing on safety, evaluation, and governance.
Course Customization Options
- For organizations seeking a customized version of this training, please reach out to us to arrange a tailored experience.
Course Outline
Foundations of Agentic AI for Healthcare
- Distinguishing agentic systems from tool-only LLM applications
- Defining autonomy boundaries, policies, and the role of human oversight
- Understanding the healthcare data landscape and its constraints (EHR, FHIR, PHI)
Designing Agent Workflows
- Implementing planning, memory, tool use, and reflection loops
- Applying prompt engineering, function/tool invocation, and action selection techniques
- Managing state and utilizing orchestration patterns
Retrieval-Augmented Agents
- Ingesting and chunking medical documents
- Utilizing embeddings, vector stores, and evaluating relevance
- Grounding responses and implementing citation strategies
Healthcare Integrations and Interoperability
- Basics of FHIR and SMART for agent connectivity
- Handling structured and unstructured clinical data
- Managing eventing, APIs, and audit trails
Safety, Risk, and Governance
- Implementing guardrails, conducting red-teaming, and designing fail-safe mechanisms
- Managing PHI, de-identification processes, and access controls
- Establishing human-in-the-loop reviews and escalation paths
Evaluation and Monitoring
- Conducting offline evaluations, utilizing golden sets, and defining KPIs
- Detecting hallucinations and verifying factuality
- Ensuring observability, logging, and managing costs and latency
Deployment Patterns and Hands-on Lab
- Comparing API-based versus on-premises model options
- Constructing a retrieval-augmented agent using LangChain, FastAPI, and ChromaDB
- Practicing simulated incident response and rollback procedures
Summary and Next Steps
Requirements
- A foundational understanding of Python programming
- Prior experience with data analysis or machine learning workflows
- Familiarity with healthcare data standards and concepts (such as EHR and FHIR)
Target Audience
- Healthcare data scientists and machine learning engineers
- Clinical informatics specialists and digital health product teams
- IT leaders and innovation managers within the healthcare industry
Open Training Courses require 5+ participants.
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