Predictive Analytics · Evidence methods
Model Evaluation and Validation
A practical approach to evaluating healthcare AI models across outcomes, populations, and clinical workflows.
Start with the question
Evaluation begins with a clear outcome, intended user, and decision context. These define what the model needs to do and which measures are relevant to assessing its performance.
Data and evaluation design
Data selection, separation of development and evaluation sets, missing information, and comparator methods are important parts of the design. Clear documentation makes the evaluation easier to review and reproduce.
Look beyond a single measure
Accuracy, sensitivity, specificity, calibration, and predictive values describe different aspects of performance. Together, they help explain how a model behaves within its intended setting.
Evaluate across settings
Population differences, data quality, and changing workflows can affect model behaviour. External evaluation and ongoing monitoring address these questions as systems and settings evolve.
Work across disciplines
Clinical specialists, data scientists, and statisticians bring complementary expertise to evaluation. We welcome teams with a specific model, dataset, or clinical question to discuss.