Healthcare AI/ML Cookbook
A practical guide to AI and machine learning patterns in healthcare. Architecture-first: the emphasis is on how systems fit together and where they fail, not on shipping 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
A note on code
Each recipe has an architecture companion covering AWS services, prerequisites, and a pseudocode walkthrough. Some also have a Python companion. Those Python pages are illustrative sketches, not a deployable asset: they are not exercised by any test suite, they pin no dependency versions, and cloud APIs and model identifiers move faster than a book does. They exist to make the architecture concrete, and they are deliberately kept out of the site navigation so nobody mistakes them for a starting point for production work. Read them to understand the shape of a solution; build from current vendor documentation.
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
Chapters
| Ch | Chapter | What it covers |
|---|---|---|
| 1 | Document Intelligence | Paper digitization and extraction, including optical character recognition (OCR) |
| 2 | Clinical Text Generation | Large language models and generative AI applied to clinical text |
| 3 | Anomaly & Outlier Detection | Finding outliers and unusual patterns |
| 4 | Recommendation & Personalization | Tailoring experiences and recommendations |
| 5 | Entity Resolution & Record Linkage | Matching and linking records |
| 6 | Clustering & Patient Segmentation | Patient similarity and grouping |
| 7 | Predictive Risk Modeling | Risk scoring and prediction |
| 8 | Clinical NLP & Information Extraction | Traditional, non-LLM text processing and information extraction |
| 9 | Medical Imaging & Computer Vision | Medical imaging and visual analysis |
| 10 | Speech & Voice AI | Audio processing and voice interfaces |
| 11 | Conversational AI & Virtual Agents | Chatbots and virtual agents |
| 12 | Forecasting & Time-Series Analysis | Temporal patterns, trends and forecasting |
| 13 | Knowledge Graphs & Clinical Reasoning | Ontologies and relationship modeling |
| 14 | Optimization & Resource Allocation | Resource allocation and scheduling |
| 15 | Sequential Decision-Making & Reinforcement Learning | Adaptive decision-making and reinforcement learning (RL) |
Healthcare Context
All patterns assume HIPAA compliance, PHI handling requirements, and enterprise-scale concerns. Regulatory considerations (FDA, state laws) are noted where relevant.
Disclaimers
The patterns, architectures, and guidance here are provided for educational purposes. Healthcare AI systems must be validated for your own clinical, regulatory, and compliance context before production use.
Nothing here is medical advice. The clinical scenarios, patient vignettes, drug names, interaction severities, dosing figures, and treatment thresholds in these recipes are illustrative material, chosen to make an architecture concrete. They are not clinical guidance, they are not maintained as a clinical reference, and they are no substitute for the judgement of a qualified clinician who knows the patient. Reading this creates no clinician-patient relationship. Nothing here is legal or compliance advice either, and regulatory questions belong with your own legal and compliance teams.
The views and opinions expressed are those of the author alone. They do not represent the views, positions, or policies of any current or former employer of the author, or of any organization with which the author is or has been affiliated.
Many of the product and service names used here are claimed as trademarks by their owners. Amazon Web Services, AWS, Amazon Bedrock, Amazon SageMaker, Amazon Textract, Amazon Comprehend Medical, Amazon Transcribe Medical, AWS HealthLake, Amazon Connect, Amazon DynamoDB, Amazon S3, AWS Lambda, and AWS Step Functions are trademarks of Amazon.com, Inc. or its affiliates. All other trademarks are the property of their respective owners. Where those names appear they are used in an editorial fashion only, with no intention of infringement and no implication of affiliation with, sponsorship by, or endorsement from the trademark owner.