Healthcare AI/ML Cookbook
A practical guide to AI and machine learning patterns in healthcare — architecture-focused, no code.
What This Is
An O'Reilly-style cookbook covering AI/ML applications in healthcare. Each section presents:
- Use cases ordered from simple to complex
- Architecture patterns for real-world implementation
- Hidden challenges that aren't obvious until you're in production
- Limitations and assumptions to understand before you start
Who This Is For
- Solution architects designing healthcare AI systems
- Technical leaders evaluating AI opportunities
- Engineers building healthcare ML pipelines
- Product managers scoping AI features
How to Use This Book
- Browse categories to find relevant AI/ML capabilities
- Start simple — each category begins with quick-win use cases
- Understand complexity — use the ordering to gauge implementation effort
- Reference architecture patterns when designing systems
Categories Covered
| Category | Description |
|---|---|
| Document Intelligence / OCR | Paper digitization and extraction |
| LLM / Generative AI | Text generation and synthesis |
| Anomaly Detection | Finding outliers and unusual patterns |
| Personalization | Tailoring experiences and recommendations |
| Entity Resolution | Matching and linking records |
| Cohort Analysis / Clustering | Patient similarity and grouping |
| Predictive Analytics | Risk scoring and forecasting |
| NLP (Non-LLM) | Traditional text processing |
| Computer Vision | Medical imaging and visual analysis |
| Speech / Voice AI | Audio processing and voice interfaces |
| Conversational AI | Chatbots and virtual assistants |
| Time Series Analysis | Temporal patterns and trends |
| Knowledge Graphs | Ontologies and relationship modeling |
| Optimization | Resource allocation and scheduling |
| Reinforcement Learning | Adaptive decision-making |
Healthcare Context
All patterns assume HIPAA compliance, PHI handling requirements, and enterprise-scale concerns. Regulatory considerations (FDA, state laws) are noted where relevant.