Explainable AI: Enhancing Clinician Trust and Accuracy in Prostate Cancer Diagnosis

Abstract

Prostate cancer remains one of the most prevalent malignancies among men worldwide and achieving an accurate and timely diagnosis is essential for guiding appropriate treatment decisions and improving patient outcomes. In recent years, Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL) techniques, has been increasingly applied to clinical, imaging, and histopathological data to enhance diagnostic accuracy. These approaches have shown strong potential in detecting complex patterns within medical datasets that may not be easily captured using traditional statistical methods. Nevertheless, despite the growing number of AI-based diagnostic models proposed in the literature, their adoption in routine clinical practice remains limited. This is largely due to concerns related to transparency, interpretability, reliability, and the extent to which clinicians can understand and trust automated decision-making systems. To address these limitations, Explainable Artificial Intelligence (XAI) has emerged as a promising direction aimed at improving the interpretability of predictive models without compromising performance. In this study, an XAI-based framework for prostate cancer diagnosis is developed by integrating ensemble learning with post-hoc explanation techniques. Multiple classifiers, including decision trees, support vector machines, and artificial neural networks, were combined within an ensemble strategy to leverage their complementary strengths and enhance overall predictive capability. To ensure model transparency, explanation methods such as SHAP (Shapley Additive Explanations) and feature importance analysis were employed to quantify and illustrate the contribution of each variable to the final prediction. The experimental results demonstrate that the proposed ensemble XAI framework achieves superior diagnostic accuracy compared with individual classifiers while also providing clear and clinically meaningful explanations of its decisions. These findings highlight the value of incorporating explainability into machine learning models to strengthen clinician trust, support responsible AI deployment, and enable more informed and reliable decision-making in prostate cancer management.