Data: The Foundation of Medical AI
Building effective AI models begins with data. This section covers everything a clinical researcher needs to know about assembling, preparing, and safeguarding datasets.
Why Data Quality Matters More Than Model Complexitysoon
The single most important lesson in applied AI: a simple model on excellent data will outperform a complex model on poor data. What "data quality" means in practice.
How Much Data Do You Need?soon
Rules of thumb and formal approaches for estimating dataset size requirements. Why there is no universal answer, and how transfer learning changes the equation.
Data Annotation and Labeling for Clinical AIsoon
Best practices for creating labeled datasets: annotation protocols, adjudication of disagreements, inter-annotator variability as a performance ceiling, and tools for efficient labeling.
Class Imbalance: When Rare Conditions Dominate Your Research Questionsoon
Why most clinical datasets have far more normal cases than pathological ones, and strategies for handling this imbalance without introducing bias.
Data Preprocessing and Augmentationsoon
Normalization, standardization, cropping, and augmentation techniques. When augmentation helps, when it introduces artifacts, and domain-specific considerations for medical imaging.
Multi-Site and Multi-Device Data: The Generalization Challengesoon
Why models trained on data from one scanner, hospital, or population often fail on another. Strategies for building generalizable datasets, including domain adaptation and harmonization.
De-Identification, Privacy, and Regulatory Compliance for Research Datasoon
HIPAA Safe Harbor and Expert Determination methods, GDPR requirements, institutional review board (IRB) considerations, and how to build datasets that are both useful and compliant.
Synthetic Data and Data Simulationsoon
When and how synthetic data can supplement real clinical data. Generative approaches, simulation-based data, and the limitations of synthetic datasets for regulatory submissions.