Agentic AI in Healthcare Training Course
Agentic AI refers to a methodology where artificial intelligence systems are capable of planning, reasoning, and utilizing tools to achieve specific objectives within established boundaries.
This instructor-led live training, available online or in-person, is designed for intermediate-level healthcare and data professionals seeking to design, assess, and manage agentic AI solutions for both clinical and operational scenarios.
Upon completion of this course, participants will be equipped to:
- Articulate the core 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 that leverage clinical documentation and knowledge repositories.
- Assess, oversee, and govern agent conduct using safety guardrails and human-in-the-loop mechanisms.
Course Format
- Interactive lectures coupled with guided discussions.
- Directed laboratory exercises and code walkthroughs conducted in a sandbox setting.
- Scenario-based activities focusing on safety, assessment, and governance.
Customization Options
- To arrange a tailored training program for this course, please reach out to us.
Course Outline
Foundations of Agentic AI for Healthcare
- Distinguishing agentic systems from tool-only LLM applications
- Defining autonomy boundaries, policies, and human oversight requirements
- Understanding the healthcare data landscape and constraints (EHR, FHIR, PHI)
Designing Agent Workflows
- Implementing planning, memory, tool usage, and reflection loops
- Prompt engineering, function/tool integration, and action selection
- Managing state and orchestration patterns
Retrieval-Augmented Agents
- Ingesting and chunking medical documents
- Utilizing embeddings, vector stores, and relevance evaluation techniques
- Ensuring grounded responses and implementing citation strategies
Healthcare Integrations and Interoperability
- Mastering FHIR/SMART basics for agent connectivity
- Processing structured and unstructured clinical data
- Managing events, APIs, and audit trails
Safety, Risk, and Governance
- Implementing guardrails, conducting red-teaming, and designing fail-safes
- Handling PHI, de-identification processes, and access controls
- Facilitating 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 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 executing rollback procedures
Summary and Next Steps
Requirements
- Foundational knowledge of Python programming
- Practical experience with data analysis or machine learning workflows
- Awareness of healthcare data standards (e.g., EHR, FHIR)
Target Audience
- Healthcare data scientists and ML engineers
- Clinical informatics and digital health product teams
- IT leadership and innovation managers within the healthcare sector
Open Training Courses require 5+ participants.
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