'SHAP' stands for 'Shapley Additive Explanations'. It is a method from cooperative game theory applied to machine learning to explain how much each input feature contributes to a model’s output.

Originally derived from Shapley values in game theory, SHAP calculates the average marginal contribution of each feature to the prediction, across all possible combinations of features.

• Model-agnostic: Can be applied to any machine learning model.
• Additive: The sum of SHAP values equals the model output.
• Interpretable: Assigns an importance score to each variable for a specific prediction.

SHAP is increasingly used in medical AI to:

• Understand which variables drive risk predictions.
• Improve transparency in black-box models.
• Support clinician trust in algorithmic decision tools.

• SHAP explains the behavior of the model, not the underlying physiology.
• It does not imply causality — a high SHAP value does not mean a variable causes disease.
• Computationally expensive for complex models and large datasets.

'In summary:' SHAP helps interpret machine learning outputs, but must be used with caution in clinical settings to avoid overinterpreting spurious correlations.