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 instructor-led training session, available either online or at your location, is designed for intermediate-level healthcare professionals and data teams looking to create, assess, and manage agentic AI solutions for both clinical and operational scenarios.
Upon completing this training, participants will be equipped to:
- Clarify the principles and limitations of agentic AI within healthcare settings.
- Construct secure agent workflows that incorporate planning, memory retention, and tool integration.
- Develop retrieval-augmented agents capable of processing clinical documents and knowledge repositories.
- Assess, supervise, and regulate agent activities using safety guardrails and human oversight mechanisms.
Course Format
- Interactive lectures paired with guided discussions.
- Hands-on labs and code walkthroughs conducted in a sandbox environment.
- Practical exercises focused on safety, evaluation, and governance scenarios.
Customization Options
- For tailored training arrangements, please get in touch with us.
Course Outline
Foundations of Agentic AI for Healthcare
- Distinctions between agentic and tool-only LLM applications.
- Defining autonomy boundaries, policies, and human oversight requirements.
- Overview of the healthcare data landscape and constraints (including EHR, FHIR, and PHI).
Designing Agent Workflows
- Implementing planning, memory, tool use, and reflection loops.
- Techniques for prompt engineering, functions/tools, and action selection.
- Patterns for state management and orchestration.
Retrieval-Augmented Agents
- Ingesting medical documents and chunking strategies.
- Utilizing embeddings, vector stores, and evaluating relevance.
- Grounding responses and managing citation strategies.
Healthcare Integrations and Interoperability
- Basics of FHIR and SMART for agent connectivity.
- Working effectively with structured and unstructured clinical data.
- Managing 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 controls.
- Establishing human-in-the-loop review processes and escalation paths.
Evaluation and Monitoring
- Conducting offline evaluations, creating golden sets, and defining KPIs.
- Detecting hallucinations and performing factuality checks.
- Ensuring observability, logging, and managing costs and latency.
Deployment Patterns and Hands-on Lab
- Comparing API-based versus on-prem model options.
- Building a retrieval-augmented agent using LangChain, FastAPI, and ChromaDB.
- Simulating incident response and executing rollback procedures.
Summary and Next Steps
Requirements
- Basic proficiency in Python programming.
- Prior experience with data analysis or machine learning workflows.
- Understanding of healthcare data concepts, such as EHR and FHIR.
Target Audience
- Healthcare data scientists and machine learning engineers.
- Teams specializing in clinical informatics and digital health products.
- IT leaders and innovation managers within the healthcare sector.
Open Training Courses require 5+ participants.
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