AI in Healthcare Agenda

Healthcare Data – From Clinical Trials to Real-World Data #

• AI in Healthcare – Clinical Data, Evaluations of AI Applications (Coursera – Stanford)
• Clinical Data Science – Hands-on training in structured clinical data (EHR, OMOP, CDM), computational phenotyping, predictive modeling, and clinical NLP (Coursera – UColorado)
• Hands-On Healthcare Data – Healthcare Informatics & Knowledge Graph (Book – Andrew Nguyen)

Causal Reasoning for Healthcare Decision #

• A Crash Course in Causality – Inferring Causal Effects from Observational Data (Coursera – PennMed)
• Causal AI – Structural Causal Models (SCMs) using DoWhy, enabling automated causal discovery, counterfactual reasoning, and intervention-based AI systems (altdeep.ai)

Knowledge-Driven Healthcare Decision #

• Knowledge Graphs – Enabling structured reasoning for clinical decision support (Neo4j).
• Knowledge Graph-Enhanced RAG – Combining structured medical knowledge with AI-driven retrieval to deliver context-aware, accurate, and explainable clinical insights (Neo4j with LangChain).

Regulatory Drivers of AI in Healthcare #

• 21st Century Cures Act (2016)
• FDA RWE Framework (2018)
• CMS & ONC Interoperability Rule (2020)
• FDA AI/ML SaMD Framework (2021)
• FDA AI-Generated Synthetic Control Arms (2021)
• HIPAA Updates for AI (2023)