Agentic AI in Healthcare Training Course
Agentic AI represents a paradigm where artificial intelligence systems engage in planning, reasoning, and executing tool-based actions to achieve specific objectives within set boundaries.
This instructor-led, live training (available online or onsite) is designed for intermediate-level healthcare and data professionals aiming to design, evaluate, and govern agentic AI solutions for clinical and operational scenarios.
Upon completing this training, participants will be equipped to:
- Articulate the principles and constraints of agentic AI within healthcare environments.
- Architect secure agent workflows incorporating planning, memory, and tool utilization.
- Develop retrieval-augmented agents capable of processing clinical documents and knowledge bases.
- Assess, monitor, and regulate agent behavior through guardrails and human-in-the-loop oversight mechanisms.
Course Format
- Interactive lectures coupled with facilitated discussions.
- Guided laboratory exercises and code walkthroughs within a sandbox environment.
- Scenario-based activities focusing on safety, evaluation, and governance.
Customization Options
- For tailored training requests, please reach out to us to arrange.
Course Outline
Foundations of Agentic AI for Healthcare
- Distinctions between agentic systems and tool-only LLM applications
- Defining autonomy boundaries, policies, and human oversight
- Understanding the healthcare data landscape and its constraints (EHR, FHIR, PHI)
Designing Agent Workflows
- Integrating planning, memory, tool use, and reflection loops
- Prompt engineering, function/tool integration, and action selection
- Managing state and applying orchestration patterns
Retrieval-Augmented Agents
- Ingesting and chunking medical documents
- Utilizing embeddings, vector stores, and relevance evaluation
- Grounding responses and implementing citation strategies
Healthcare Integrations and Interoperability
- Basics of FHIR/SMART for agent connectivity
- Working with structured and unstructured clinical data
- Eventing, APIs, and maintaining audit trails
Safety, Risk, and Governance
- Implementing guardrails, red-teaming, and fail-safe design
- Handling PHI, de-identification, and access controls
- Human-in-the-loop review and escalation paths
Evaluation and Monitoring
- Conducting offline evaluations, using golden sets, and defining KPIs
- Detecting hallucinations and performing factuality checks
- Ensuring observability, logging, and managing cost/latency
Deployment Patterns and Hands-on Lab
- Comparing 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
- Proficiency in basic Python programming
- Experience with data analysis or machine learning workflows
- Familiarity with healthcare data concepts (e.g., EHR, FHIR)
Target Audience
- Healthcare data scientists and ML engineers
- Clinical informatics and digital health product teams
- IT leaders and innovation managers in the healthcare sector
Open Training Courses require 5+ participants.
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