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With the increasing availability of structured and unstructured data and theswift progress of analytical techniques, Artificial Intelligence (AI) isbringing a revolution to the healthcare industry. With the increasinglyindispensable role of AI in healthcare, there are growing concerns over thelack of transparency and explainability in addition to potential biasencountered by predictions of the model. This is where Explainable ArtificialIntelligence (XAI) comes into the picture. XAI increases the trust placed in anAI system by medical practitioners as well as AI researchers, and thus,eventually, leads to an increasingly widespread deployment of AI in healthcare.  In this paper, we present different interpretability techniques. The aim isto enlighten practitioners on the understandability and interpretability ofexplainable AI systems using a variety of techniques available which can bevery advantageous in the health-care domain. Medical diagnosis model isresponsible for human life and we need to be confident enough to treat apatient as instructed by a black-box model. Our paper contains examples basedon the heart disease dataset and elucidates on how the explainabilitytechniques should be preferred to create trustworthiness while using AI systemsin healthcare.

Explainable AI meets Healthcare: A Study on Heart Disease Dataset