Explainable and Multimodal Artificial Intelligence for Clinical Oncology: Decision Support Systems, Clinical Deployment, and Regulatory Perspectives examines how interpretable AI and multimodal data fusion transform cancer care. It positions AI as a clinically grounded decision-support tool that integrates imaging, pathology, genomics, and EHR data within oncology workflows, with emphasis on transparency, validation, and governance. The book connects cancer biology, diagnostic and treatment decision-making, and outcome prediction through a cohesive framework that addresses regulatory readiness and ethical considerations. It blends theory, methodological rigor, and practical deployment guidance to bridge research advances with routine clinical use. The content spans foundations in clinical oncology, machine learning methods, multimodal data integration, deployment workflows, and regulatory considerations, illustrated by real-world case studies and deployment narratives. It highlights interpretability, bias mitigation, data governance, and validation strategies as core requirements for trustworthy systems in oncology. The result is a comprehensive resource that guides scholars and practitioners from model development to safe, scalable integration in diverse oncology settings. The book benefits researchers and clinicians by providing a clear, action-oriented roadmap for developing, validating, and deploying AI in cancer care. It equips academic audiences with frameworks that harmonize computational advances with oncological practice, supports regulatory and ethical compliance, and fosters cross-disciplinary collaboration to translate AI innovations into tangible patient benefits.

• Integrates multimodal data to support transparent, clinically meaningful oncology decisions
• Ensures explainable AI with validation and regulatory-ready deployment frameworks
• Demonstrates real-world deployment through case studies and practical workflows