Published Paper Details:

Kidney stone disease presents a significant healthcare challenge globally, necessitating precise and timely detection for effective management and treatment. Recently, the integration of artificial intelligence (AI) techniques has shown promise in enhancing diagnostic accuracy and efficiency. However, the lack of interpretability in conventional AI models often impedes their adoption in critical medical tasks. This study advocates for the use of Explainable Artificial Intelligence (XAI) methodologies to improve the detection of kidney stones. By utilizing XAI techniques such as rule-based systems, decision trees, and model-agnostic approaches like LIME (Local Interpretable Model-agnostic Explanations) and SHAP (Shapley Additive Explanations), the goal is to provide clinicians with transparent insights into the AI models' decision-making processes. The proposed XAI framework aims to deepen the understanding of the features and patterns that drive the classification of kidney stone presence, thereby increasing trust and acceptance among medical practitioners. Additionally, elucidating the rationale behind AI predictions allows clinicians to validate and refine the model's performance, leading to improved diagnostic accuracy and patient care. Through comprehensive evaluation using realworld kidney stone datasets, this study demonstrates the efficacy and interpretability of the XAI-driven approach in detecting kidney stones. The findings highlight the importance of transparency and interpretability in AI-based medical systems, fostering trust and collaboration between AI technology and healthcare professionals. Ultimately, integrating explainable AI in kidney stone detection promises to advance diagnostic capabilities and enhance patient outcomes in urology.