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
Core Principles of Agentic AI in Healthcare
- Distinguishing agentic systems from standard tool-using LLM applications.
- Defining autonomy limits, policies, and the role of human oversight.
- Navigating the healthcare data environment and its constraints, including EHR, FHIR, and PHI.
Creating Agent Workflows
- Integrating planning, memory, tool usage, and reflective loops.
- Applying prompt engineering, function/tool definition, and action selection strategies.
- Managing state and implementing orchestration patterns.
Retrieval-Augmented Agents
- Processing and segmenting medical documents for ingestion.
- Utilizing embeddings, vector stores, and assessing relevance.
- Ensuring response accuracy and developing citation methodologies.
Healthcare Integration and Interoperability
- Fundamentals of FHIR and SMART for connecting agents.
- Handling both structured and unstructured clinical data.
- Implementing eventing, APIs, and maintaining audit trails.
Safety, Risk Management, and Governance
- Designing guardrails, conducting red-teaming, and establishing fail-safes.
- Managing PHI, ensuring de-identification, and enforcing access controls.
- Implementing human-in-the-loop reviews and defining escalation protocols.
Assessment and Monitoring
- Conducting offline evaluations, creating golden sets, and defining KPIs.
- Detecting hallucinations and verifying factual accuracy.
- Ensuring observability, logging, and managing cost and latency.
Deployment Strategies and Practical Lab
- Comparing API-based versus on-premises model deployment options.
- Developing a retrieval-augmented agent using LangChain, FastAPI, and ChromaDB.
- Simulating incident response scenarios and executing rollback procedures.
Recap and Future Directions
Requirements
- A foundational understanding of Python programming.
- Practical experience with data analysis or machine learning workflows.
- Knowledge of key healthcare data standards and concepts, such as EHR and FHIR.
Target Audience
- Healthcare data scientists and machine learning engineers.
- Clinical informatics specialists and digital health product teams.
- IT leadership and innovation managers within the healthcare sector.
