"Explainable AI in Medical Imaging: Improving Clinical Trust in Deep Learning Model."

Abstract

The integration of deep learning (DL) into medical imaging has demonstrated remarkable potential in enhancing diagnostic accuracy and efficiency. However, the "black box" nature of these models often hinders clinical adoption due to a lack of transparency and trust. Explainable Artificial Intelligence (XAI) addresses this challenge by providing interpretable and transparent outputs, enabling clinicians to understand, verify, and trust model predictions. This paper explores the role of XAI in medical imaging, focusing on how it enhances clinical trust and supports informed decision-making. We discuss state-of-the-art XAI techniques, including saliency maps, Layer-wise Relevance Propagation (LRP), and SHAP values, and evaluate their application in imaging modalities such as MRI, CT, and X-rays. Furthermore, we assess the impact of XAI on clinician engagement, diagnostic confidence, and the regulatory landscape. Through a comprehensive review and case studies, the paper emphasizes the necessity of balancing performance with interpretability to ensure reliable and ethically responsible AI deployment in healthcare. By improving model transparency, XAI has the potential to bridge the gap between artificial intelligence and clinical practice, fostering greater collaboration and trust in AI-assisted diagnostics.