Performance metrics tell us whether a model predicts well. Interpretability asks a different question: can researchers, clinicians, and patients understand why the model behaves as it does, where it may fail, and whether its reasoning is clinically plausible?
The portal already includes safe splitting, leakage prevention, metrics, calibration, thresholds, bias, and fairness. A missing next step is explainability: after a health ML model looks accurate, students need to inspect which variables drive predictions and whether those drivers make clinical and epidemiological sense.
Clinicians often ask for reasons, not only probabilities. Useful explanations make model review easier without promising certainty.
Feature effects can reveal leakage proxies, site effects, or variables that encode healthcare access instead of biology.
Interpretability outputs should be reported with uncertainty, cohort context, and a warning that association is not causation.
Click a method to see what it answers, when it is useful, and which healthcare caveat to remember.
Ranks variables by their overall contribution to model performance or predictions.
Shows how predictions change as one feature varies while other features are averaged out.
Decomposes one prediction into feature contributions above or below a baseline risk.
Asks what small changes would move a prediction across a decision threshold.
Adjust a simplified patient profile. The bars show a local explanation: red features increase predicted risk and green features decrease predicted risk relative to the cohort baseline.
Answers: "What drives the model across the study population?" Useful for model cards, manuscript reporting, and sanity checks.
Answers: "Why did this specific patient receive this prediction?" Useful for case review and threshold discussions.
Answers: "Does this explanation make sense in the care context?" Useful before deployment or publication.
Interpretability is most useful when it is part of the evaluation plan, not a decorative figure added at the end.
Decide whether the explanation is for researchers, clinicians, patients, auditors, or model developers.
Use global explanations for model understanding and local explanations for individual prediction review.
Compare explanations across folds, bootstrap samples, sites, demographic groups, and time windows.
Flag proxies for missingness, hospital site, future information, billing behavior, or clinical workflow shortcuts.
State that explanations describe model behavior. They do not prove causal effects or treatment benefit.
Pair explanations with calibration, threshold choice, and a clear clinical response plan.
Compute explanations on validation or test data that reflects the intended use population.
A feature can be important because it is a proxy for care access, site, or measurement frequency.
Do not call a feature "causal" just because it has a large model contribution.
Correlated features can split or swap importance, making ranked lists unstable.
Report explanations with model type, dataset, preprocessing, and uncertainty or sensitivity checks.
Do not use interpretability to compensate for poor discrimination, calibration, or external validation.
Explainable AI is not a single chart. It is a set of questions about model behavior, clinical plausibility, subgroup robustness, and decision consequences. In healthcare, explanations should support responsible review and communication, not replace validation or clinical judgment.