Given a trained Convolutional Neural Network (CNN) model designed for medical image diagnosis, perform a 'black box' interpretability analysis. Describe how you would apply techniques such as SHAP (SHapley Additive exPlanations) or LIME (Local Interpretable Model-agnostic Explanations) to understand which features contribute most to a specific classification. Discuss the implications of false positives in this context and how interpretability affects trust in clinical deployment.