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. Sanil Joseph
Dr. Sanil Joseph, Assistant Professor, Department of Community Medicine, SDM Medical College, Dharwad, India
Published Online : 2024-12-30
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ABSTRACT
Computational oncology is entering a new era characterized by the convergence of foundation artificial intelligence (AI) models, multimodal biomedical intelligence, digital twins, federated learning, and precision medicine. This next-generation paradigm, referred to as Computational Oncology 5.0, extends beyond conventional machine learning by creating continuously adaptive computational ecosystems capable of integrating heterogeneous biomedical information across the entire cancer care continuum. Foundation AI models trained through self-supervised learning provide generalized biomedical representations that unify radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, wearable technologies, electronic health records, and longitudinal clinical outcomes into comprehensive patient-specific computational frameworks. These intelligent ecosystems support precision diagnosis, biomarker discovery, prognostic prediction, therapeutic optimization, immunotherapy selection, digital twin simulation, adaptive disease monitoring, and evidence-based clinical decision support. Emerging innovations including multimodal large language models, graph neural networks, reinforcement learning, federated learning, retrieval-augmented generation, explainable artificial intelligence, agentic AI, and generative models further strengthen Computational Oncology 5.0 by enabling collaborative, privacy-preserving, predictive, preventive, and continuously learning precision medicine. Despite remarkable technological advances, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interpretability, interoperability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of Computational Oncology 5.0, emphasizing foundation AI models, intelligent computational ecosystems, clinical applications, implementation challenges, and future perspectives for predictive, preventive, and personalized cancer care.[1].
Keywords: Computational oncology, Foundation models, Artificial intelligence, Precision oncology, Predictive medicine, Personalized medicine, Digital twins, Multimodal learning, Clinical decision support, Cancer intelligence.
