Developing AI Models
A practical guide to the model development lifecycle, written for clinical researchers who will collaborate with engineering teams rather than build models alone.
The AI Development Lifecycle: From Hypothesis to Deploymentsoon
An end-to-end map of the development process: problem formulation, data curation, model selection, training, validation, and iteration. Where clinical expertise is essential at each stage.
Training, Validation, and Test Sets: Why You Need All Threesoon
The logic behind data splitting, why the test set must remain untouched, and the consequences of data leakage — the most common and most damaging mistake in medical AI research.
Transfer Learning: Standing on the Shoulders of Large Modelssoon
How pre-trained models (ImageNet, medical foundation models) can be fine-tuned for your specific clinical task with far less data than training from scratch.
Hyperparameter Tuning and Model Selectionsoon
What hyperparameters are, how they differ from learned parameters, and practical approaches to tuning (grid search, random search, Bayesian optimization) without overfitting.
Overfitting, Underfitting, and Regularizationsoon
Recognizing when a model has memorized the training data versus learned generalizable patterns. Dropout, weight decay, early stopping, and cross-validation as defenses.
Working with an Engineering Team: The Clinician's Rolesoon
How to be an effective collaborator when you are the domain expert, not the developer. Defining clinical requirements, reviewing results critically, and avoiding the "throw it over the wall" failure mode.
Tools and Infrastructure: What You Need to Knowsoon
A non-technical overview of the computational landscape: GPUs and cloud computing, Python and common frameworks (PyTorch, TensorFlow), experiment tracking, and what these cost.