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Duration 14 hours
Course Outline
Foundations of Agentic AI in Healthcare
- Distinguishing agentic systems from standard tool-only LLM applications
- Defining autonomy limits, policy frameworks, and human oversight requirements
- Navigating the healthcare data ecosystem and its constraints (EHR, FHIR, PHI)
Architecting Agent Workflows
- Integrating planning mechanisms, memory systems, tool integration, and reflection cycles
- Applying prompt engineering, function/tool invocation, and strategic action selection
- Managing state and implementing effective orchestration patterns
Retrieval-Augmented Agent Development
- Ingesting and segmenting medical documentation for optimal processing
- Leveraging embeddings, vector databases, and assessing retrieval relevance
- Ensuring response accuracy through grounding techniques and citation methodologies
Healthcare Integration and Interoperability
- Utilizing FHIR/SMART standards for seamless agent connectivity
- Processing both structured and unstructured clinical data streams
- Implementing event handling, API interactions, and comprehensive audit trails
Safety, Risk Management, and Governance
- Deploying guardrails, conducting red-teaming exercises, and designing fail-safe mechanisms
- Managing PHI, applying de-identification techniques, and enforcing access controls
- Establishing human-in-the-loop review processes and clear escalation pathways
Evaluation and Continuous Monitoring
- Conducting offline assessments, curating golden datasets, and defining key performance indicators
- Detecting hallucinations and validating factual accuracy
- Enhancing observability, logging, and optimizing cost and latency metrics
Deployment Strategies and Practical Laboratory
- Selecting between API-based and on-premise model deployment options
- Constructing a retrieval-augmented agent using LangChain, FastAPI, and ChromaDB
- Simulating incident response scenarios and executing rollback procedures
Conclusion and Future Roadmap
Requirements
- Foundational proficiency in Python programming.
- Practical experience with data analysis pipelines or machine learning workflows.
- Familiarity with key healthcare data standards and concepts, such as EHR and FHIR.
Target Audience
- Data scientists and ML engineers specializing in healthcare.
- Teams involved in clinical informatics and digital health product development.
- IT executives and innovation managers within the healthcare sector.
