“Explainable AI” Disease Detection with Reasoning
Manoj Kumar, Kanishk Ramrakhiyani, Harsh Garg · 2024
Explainable AI (XAI) has gained significant attention globally due to its capability to provide transparency and understanding of complex machine learning models. We explore the potential benefits of XAI and integrate them into the domain of disease detection. Our objective is to leverage the power of large language models coupled with advanced explainability algorithms to enhance the accuracy and interpretability of disease predictions based on input symptoms. By doing so, we aim to not only achieve precise disease identification but also offer clear and comprehensible explanations for the predictions made. This approach holds the promise of bridging the gap between the intricate nature of AI-driven disease detection systems and the need for transparency and trustworthiness in medical diagnoses.