Agentic AI in Healthcare Training Course
Agentic AI represents a methodology where artificial intelligence systems are capable of planning, reasoning, and utilizing tools to achieve specific objectives within established boundaries.
This guided, live training session—available either online or in-person—is designed for mid-level healthcare and data professionals looking to design, assess, and oversee agentic AI solutions for both clinical and operational scenarios.
Upon completing this course, participants will be equipped to:
- Clarify the principles and limitations of agentic AI within healthcare environments.
- Construct secure agent workflows that incorporate planning, memory retention, and tool integration.
- Develop retrieval-augmented agents leveraging clinical records and knowledge repositories.
- Assess, monitor, and manage agent conduct using guardrails and human-supervised controls.
Training Format
- Engaging lectures complemented by guided discussions.
- Supervised hands-on labs and code demonstrations within a sandbox setting.
- Practical exercises focusing on safety, evaluation metrics, and governance frameworks.
Customization Options
- For tailored training solutions, please reach out to us to discuss your specific needs.
Course Outline
Foundations of Agentic AI for Healthcare
- Distinguishing agentic systems from tool-only LLM applications
- Defining autonomy limits, policies, and human oversight mechanisms
- Navigating the healthcare data ecosystem and its constraints (EHR, FHIR, PHI)
Designing Agent Workflows
- Incorporating planning, memory, tool interaction, and reflection cycles
- Utilizing prompt engineering, functions/tools, and action selection techniques
- Managing state and applying orchestration patterns
Retrieval-Augmented Agents
- Ingesting and chunking medical documents
- Working with embeddings, vector databases, and relevance assessment
- Ensuring grounded responses and effective citation strategies
Healthcare Integrations and Interoperability
- Understanding FHIR/SMART basics for agent connectivity
- Processing structured and unstructured clinical data
- Implementing event handling, APIs, and maintaining audit trails
Safety, Risk, and Governance
- Establishing guardrails, conducting red-teaming, and designing fail-safe mechanisms
- Managing PHI, ensuring de-identification, and controlling access
- Facilitating human-in-the-loop reviews and defining escalation pathways
Evaluation and Monitoring
- Conducting offline evaluations, utilizing golden sets, and defining KPIs
- Detecting hallucinations and verifying factuality
- Enhancing observability, logging, and managing costs/latency
Deployment Patterns and Hands-on Lab
- Comparing API-based vs. on-premises model options
- Constructing a retrieval-augmented agent using LangChain, FastAPI, and ChromaDB
- Simulating incident response and executing rollback procedures
Summary and Next Steps
Requirements
- Foundational knowledge of Python programming
- Prior experience in data analysis or machine learning workflows
- Awareness of healthcare data standards (e.g., EHR, FHIR)
Target Audience
- Data scientists and machine learning engineers in the healthcare sector
- Teams focused on clinical informatics and digital health product development
- IT executives and innovation directors within healthcare organizations
Open Training Courses require 5+ participants.
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