International Journal of Advances in Engineering & Scientific Research
Print ISSN : 2349 –4824
Online ISSN : 2349 –3607
Frequency : Continuous
Current Issue : Volume 11 , Issue 2
2024
Dr. Sandeep Kulshreshtha, Dr. Yusuf Ali
Dr. Sandeep Kulshreshtha, Professor,Department of Surgical Oncology, Sri Devaraj Urs Medical College, Kolar, India
Dr. Yusuf Ali, Professor,Department of Radiology, Sri Devaraj Urs Medical College, Kolar, India
Published Online : 2024-12-30
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ABSTRACT
Cancer is a biologically heterogeneous disease characterized by dynamic interactions among molecular alterations, cellular architecture, tissue organization, immune responses, metabolic adaptation, and patient-specific clinical factors. Conventional computational approaches frequently analyze these biological domains independently, limiting comprehensive understanding of tumor evolution and therapeutic response. Recent advances in artificial intelligence (AI), particularly foundation models, multimodal transformers, graph neural networks, self-supervised learning, and generative AI, have enabled the development of multimodal cancer intelligence systems capable of integrating radiomics, pathomics, multi-omics, laboratory biomarkers, wearable technologies, electronic health records, and longitudinal clinical outcomes into unified computational frameworks. These intelligent ecosystems generate comprehensive patient-specific representations that support precision diagnosis, biomarker discovery, prognostic prediction, therapeutic optimization, immunotherapy selection, adaptive disease monitoring, digital twin simulation, and intelligent clinical decision support. Emerging technologies including multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further strengthen these computational ecosystems by enabling collaborative, privacy-preserving, and continuously adaptive precision oncology. Despite remarkable technological progress, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interoperability, interpretability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of multimodal cancer intelligence systems, emphasizing integration of radiomics, pathomics, multi-omics, and clinical data as the foundation of next-generation precision oncology.[1]
Keywords: Multimodal artificial intelligence, Precision oncology, Radiomics, Pathomics, Multi-omics, Foundation models, Computational oncology, Clinical decision support, Personalized medicine, Cancer intelligence.
