Module 2: AI in Healthcare
Healthcare AI operates under constraints that other domains do not have. A model predicting credit scores can be wrong 5% of the time and the business absorbs it. A model reading a chest X-ray that misses 5% of pneumonia cases causes harm to real patients. The stakes change everything - how you label data, how you evaluate models, how you deploy them, and how you handle the inevitable errors.
This module is for engineers building ML systems in healthcare or adjacent domains where accuracy, explainability, and regulatory compliance are non-negotiable.
Why Healthcare AI Is Different
Labels are expensive and noisy. A single radiologist annotation costs $5-50 per image. Annotation disagreement rates between experienced clinicians run 15-30% on difficult cases. You cannot crowdsource labels. Your training data is always smaller than you want, and noisier than you expect.
Regulatory constraints are real. FDA Class II medical devices require 510(k) clearance. HIPAA governs any system that touches Protected Health Information (PHI). The EU MDR adds another layer for European deployment. These are not suggestions - they determine your architecture.
Distribution shift is dangerous. A model trained on data from one hospital system will degrade at another. Demographics differ, scanning protocols differ, equipment models differ. A chest X-ray from a 2015 GE scanner looks different to a model than one from a 2023 Siemens scanner, even if the clinical finding is identical.
Explainability is a clinical requirement. Clinicians will not use a black-box model. They need to understand why the model flagged something. Attention maps, Grad-CAM, and saliency methods are not nice-to-haves - they are prerequisites for clinical adoption.
Module Architecture
Lessons in This Module
| # | Lesson | Key Concept |
|---|---|---|
| 1 | Medical Imaging AI | CNNs for radiology, DICOM processing, Grad-CAM |
| 2 | Clinical NLP and EHR Systems | Named entity recognition, ICD coding, MIMIC dataset |
| 3 | Drug Discovery with AI | Molecular property prediction, AlphaFold, generative chemistry |
| 4 | Patient Outcome Prediction | Survival models, readmission prediction, EHR features |
| 5 | Radiology AI in Production | PACS integration, worklist prioritization, FDA clearance |
| 6 | Genomics and Protein Folding | Sequence models, AlphaFold 2 architecture, variant calling |
| 7 | Federated Learning in Healthcare | Why federated learning, differential privacy, PySyft |
| 8 | AI Regulation and FDA Compliance | 510(k) pathway, predetermined change control, SaMD |
Key Concepts You Will Master
- DICOM and medical imaging pipelines - how radiology data is stored and how to process it for ML
- Class imbalance in medical data - rare diseases mean 0.1% positive rates; standard accuracy is useless
- Multi-reader agreement - handling label noise from clinician disagreement
- Federated learning - training across hospital systems without sharing patient data
- Model cards and datasheets - the documentation standards healthcare AI demands
- FDA SaMD framework - Software as a Medical Device classification and what it means for your architecture
Prerequisites
- Neural Networks
- Computer Vision
- Basic understanding of Python and PyTorch