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Here, techniques which could be used to add explainable AI in predictive healthcare led models include SHAP (SHapley Additive exPlanations) values, LIME (Local Interpretable Model-agnostic Explanations), as well as rule-based models. It improves interpretability by getting insights into the model decisions. Therefore, hybrid models which balance predictive performance for transparency should be considered. Working with healthcare professionals to align the explanations with clinical contexts may also be beneficial. This iterative process, coupled with continuous validation, contributes to the seamless integration of explainable AI in predictive healthcare models.