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    1. Naturvetenskap och teknik
    2. Matematik och naturvetenskap
    3. Biologi

    Explainable and Multimodal Artificial Intelligence for Clinical Oncology

    Decision Support Systems, Clinical Deployment, and Regulatory Perspectives

    AvKailas Patil,Sachi Nandan Mohanty

    Häftad, Engelska, 2027

    2 144 kr

    Kommande

    Beskrivning

    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

    Produktinformation

    • Utgivningsdatum:2027-08-01
    • Mått:191 x 235 x undefined mm
    • Format:Häftad
    • Språk:Engelska
    • Antal sidor:450
    • Förlag:Elsevier Science
    • ISBN:9780443527531

    Utforska kategorier

    • Biologi inom Naturvetenskap och teknik

    Mer om författaren

    Prof.(Dr.) Kailas R. Patil is Dean of the Faculty of Science and Technology and Chief Information Security Officer at Vishwakarma University, Pune, India. He earned his PhD in Computer Science from the National University of Singapore and conducted postdoctoral research at Kasetsart University, Thailand. With substantial experience in academia and research, his work spans cybersecurity, web and network security, Internet of Things, and machine learning. He has a robust record of peer-reviewed publications, along with books, patents, and innovative outputs in emerging technology domains. His research has earned national recognition as a leading cybersecurity scholar and he has received multiple awards for research and innovation. He has led funded projects with major industry players and government agencies. He serves as an editor and reviewer for international journals and contributes to global research collaborations and conference leadership. His focus includes secure systems, IoT security, and ML-driven security solutions.Dr. Sachi Nandan Mohanty is a globally recognized researcher, repeatedly highlighted in prestigious global rankings by Stanford University and Elsevier. He earned his PhD from IIT Kharagpur and completed a postdoctoral fellowship at IIT Kanpur, supported by the MHRD scholarship. A prolific scholar, he has authored and edited books with leading publishers and has published extensively in esteemed international journals. His research spans data mining, big data analysis, cognitive science, fuzzy decision making, brain–computer interfaces, cognition, and computational intelligence. He has received numerous national and international honors, including fellowships from the Government of India’s Department of Science and Technology, and holds fellowships and memberships across leading professional bodies. He serves as Editor-in-Chief of the International Journal on Intelligent Systems and Machine Learning Applications and as General Chair of ICISML and AIHC conferences, delivering keynote talks worldwide. Dr. Jayant S. Goda (MD, DNB, MRes) is a Clinical Scientist and Professor in Radiation Oncology and In-charge of Radiobiology at ACTREC, Tata Memorial Centre, Mumbai, India. He completed his MD in Radiation Oncology from PGIMER, Chandigarh, followed by advanced translational research fellowships at Princess Margaret Hospital, Toronto, and King’s College London.His research focuses on translational oncology, including the development of novel radiosensitizers and radioprotectors, nanotechnology-based drug delivery systems, and the application of radiomics and machine learning for tumor classification and prognosis. He is actively involved in Phase II and Phase III clinical trials in hemato-oncology. Dr. Goda has authored over 100 peer-reviewed publications in high-impact journals, contributing significantly to clinical and translational cancer research.Dr. Fernando Moreira is a Full Professor at the University of Porto. He earns a BSc in Computer Science, an MSc in Electronic Engineering, and a PhD in Electronic Engineering from the University of Porto, and has completed habilitation. A long-standing member of the university, he holds the rank of Full Professor and also serves as a visiting professor at several other institutions. His teaching spans undergraduate and graduate programs, and he supervises doctoral and Master’s students. He maintains an active research portfolio with peer‑reviewed publications in international journals and conference proceedings and serves on editorial boards for journals and books. He has organized special issues and has chaired conferences, delivered keynote talks, and contributed to program committees. He coordinates a computation program, participates in the REMIT center, and remains engaged with professional societies. His research interests include mobile computing, artificial intelligence in higher education, and machine learning, areas in which he has received scholarly recognition. Prof. Sital Dash is an Assistant Professor in the Department of Computer Engineering, Faculty of Science and Technology, Vishwakarma University, Pune, India. She holds an M.Tech in Information Security from the National Institute of Technology (NIT), Durgapur, and Ph.D. in the area of privacy preservation for static and dynamic healthcare environments.She has over a decade of academic and research experience, with prior affiliations at KIIT Deemed to be University and MIT World Peace University, Pune. Her research interests include privacy-preserving systems, cloud computing, Internet of Things), mobile ad hoc networks (MANET), and data security.Prof. Dash has contributed to 10+ peer-reviewed journal articles, book chapters, and international conference publications in areas of artificial intelligence, IoT, and secure computing. Her work focuses on developing secure and privacy-aware intelligent systems for real-world applications.

    Innehållsförteckning

    • 1. Artificial Intelligence in Modern Clinical Oncology2. Clinical Perspectives on AI Adoption in Oncology3. Cancer Biology and Oncology Decision-Making for AI Researchers4. Clinical Oncology Workflows and Decision Support Systems5. Oncology Data Ecosystems, Data Traceability and Quality Challenges6. Data Preprocessing, Annotation and Clinical Data Validation7. Machine Learning Foundations for Oncology Applications8. Deep Learning for Radiological Cancer Imaging9. Reinforcement and Weakly Supervised Learning in Medical Imaging10. Computational Pathology and Whole-Slide Image Analysis11. Multimodal AI for Integrated Cancer Diagnosis12. AI for Cancer Screening and Early Detection13. AI in Precision Oncology and Patient Stratification14. Artificial Intelligence in Radiation Oncology15. AI-Assisted Surgical Oncology and Interventional Systems16. Machine Learning for Chemotherapy and Targeted Therapy Optimization17. Prognosis, Survival Analysis, and Outcome Prediction18. AI in Immuno-Oncology and Biomarker Discovery19. AI-Driven Drug Discovery and Repurposing in Oncology20. Explainable AI and Trustworthy Decision Support in Oncology21. Bias, Fairness, and Robustness in Oncology AI Systems22. Data Privacy, Security and Governance in Oncology AI23. Regulatory Approval and Clinical Validation of AI Systems24. Human–AI Interaction and Trust in Clinical Oncology25. Clinical Deployment of AI in Oncology: Integration with Hospital Systems26. Real-World AI Case Studies in Oncology Practice27. Emerging Frontiers in AI-Driven Oncology