'Interpretability' refers to the degree to which a human can understand the reasoning or internal mechanics of a model, algorithm, or system.

Interpretability means that:

• The influence of each input variable on the model’s output can be understood.
• Clinicians or researchers can trace how a decision or prediction was made.
• The model’s behavior is transparent, explainable, and trustworthy.

• 'Interpretability' often refers to simpler models (e.g., linear regression, decision trees) that are inherently transparent.
• 'Explainability' refers to methods (e.g., SHAP, LIME) that help clarify complex or black-box models like deep learning systems.

High interpretability is critical when:

• Decisions affect patient safety
• Regulatory approval or ethical accountability is required
• Clinicians must trust and verify model outputs

• More interpretable models may be less accurate.
• Highly accurate models (e.g., neural networks) may lack interpretability, requiring post hoc explanation tools.

'In summary:' interpretability is essential for responsible use of data-driven tools in healthcare, as it connects model predictions to human understanding.