Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Duration 14 hours
Course Outline
Foundations of Agentic AI in Healthcare
- Distinguishing agentic systems from simple tool-using LLM applications
- Defining autonomy boundaries, policies, and human oversight roles
- Understanding the healthcare data environment, including EHR, FHIR, and PHI constraints
Architecting Agent Workflows
- Implementing planning, memory, tool use, and reflection cycles
- Prompt engineering, function/tool integration, and action selection strategies
- Managing state and orchestration patterns effectively
Retrieval-Augmented Agents
- Processing medical documents through ingestion and chunking
- Utilizing embeddings, vector stores, and assessing relevance
- Grounding responses and applying citation strategies
Healthcare Integration and Interoperability
- Fundamentals of FHIR/SMART for enabling agent connectivity
- Handling structured and unstructured clinical data streams
- Managing eventing, APIs, and maintaining audit trails
Safety, Risk Management, and Governance
- Implementing guardrails, red-teaming, and fail-safe designs
- Managing PHI, de-identification, and access control protocols
- Establishing human-in-the-loop reviews and escalation pathways
Evaluation and Monitoring
- Conducting offline evaluations, defining golden sets, and establishing KPIs
- Detecting hallucinations and performing factuality checks
- Ensuring observability, logging, and managing cost/latency
Deployment Strategies and Practical Lab
- Choosing between API-based and on-premises model deployment
- Developing a retrieval-augmented agent using LangChain, FastAPI, and ChromaDB
- Simulating incident response and executing rollback procedures
Summary and Future Directions
Requirements
- Proficiency in basic Python programming
- Practical experience with data analysis or machine learning workflows
- Knowledge of healthcare data concepts, such as EHR and FHIR
Target Audience
- Healthcare data scientists and machine learning engineers
- Clinical informatics and digital health product teams
- IT leaders and innovation managers within the healthcare sector