Abstract

Interpretable artificial intelligence (AI) is gaining prominence in radiology and radiation oncology, where the ability to understand and trust AI-driven decisions is crucial for clinical practice. This paper explores the role of interpretable AI in these fields, focusing on how it enhances the clarity and transparency of AI models used for diagnostic and therapeutic purposes. We examine various approaches to improving interpretability, including model-agnostic techniques, feature visualization, and algorithmic transparency. The paper also discusses the implications of interpretable AI for clinical decision-making, patient trust, and regulatory compliance. By analyzing current advancements and providing practical examples, the study aims to highlight the importance of interpretability in integrating AI into radiological and oncological workflows.