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How can the use of AI techniques enable incorporation of predictive healthcare models in clinical practice to make them trustworthy and interpretable?

11 months ago in Health Management , PhD Research Proposal By Shobha

AI Model Artificial Intelligence

Explanatory AI Techniques

While pursuing PhD in Predictive Healthcare Models, I got interested in explainable AI. Can you please elaborate with an illustrative explanation of explanatory AI techniques on how it can enhance interpretability, trustworthiness and clinical adoption of predictive healthcare models?

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By Rani Answered 11 months ago

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.

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