Comparative Analysis of LIME and Shape Analysis Techniques in Prostate Cancer MRI Interpretation

Advancements in artificial intelligence (AI) are enabling the integration of explainable AI (XAI) models in Magnetic Resonance Imaging (MRI) to improve diagnostic accuracy and personalize prostate cancer treatment. This paper explores the effectiveness of two XAI techniques, Local Interpretable Model-agnostic Explanations (LIME) and shape analysis, in interpreting prostate cancer MRI datasets. A central focus is the necessary validation to ensure these models provide reliable and actionable diagnostic insights. However, the persistent “black-box problem,” which refers to the lack of transparency and interpretability in AI decision-making, has led to clinical skepticism regarding deep learning applications. Since most AI models produce results without clearly explaining their internal processes, radiologists may need help understanding the rationale behind predictions, limiting AI's role as a decision-support tool. In this study, we stress the need for validation to ensure that XAI-generated explanations are reliable and clinically useful. Their findings are intended to help integrate XAI into clinical practice and aid in transforming AI from an opaquer to a more transparent and crucial aid to radiologists in diagnosing and managing prostate cancer.