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. Prakash Ghosh
Dr. Prakash Ghosh, Professor,Department of Surgical Oncology, Bharati Vidyapeeth Medical College, Pune, India
Published Online : 2024-12-30
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ABSTRACT
Digital oncology is undergoing a transformative evolution through the convergence of artificial intelligence (AI), digital twin technologies, foundation models, and precision medicine into intelligent computational ecosystems capable of supporting personalized cancer care throughout the entire clinical continuum. Traditional oncology frequently relies on fragmented diagnostic modalities, episodic clinical assessments, and static therapeutic strategies that inadequately capture the dynamic biological evolution of cancer. Recent advances in foundation AI models, multimodal transformers, graph neural networks, self-supervised learning, and generative artificial intelligence have enabled comprehensive integration of radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, wearable technologies, electronic health records, patient-reported outcomes, and longitudinal clinical data into unified computational frameworks. Digital twin technologies further extend these capabilities by generating continuously evolving virtual patient models capable of simulating tumor progression, therapeutic response, toxicity, recurrence, and survivorship. These intelligent digital oncology platforms facilitate biomarker discovery, predictive therapeutics, adaptive treatment planning, precision immunotherapy, intelligent clinical decision support, preventive oncology, and continuous disease monitoring. Emerging innovations including multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further strengthen digital oncology ecosystems by enabling collaborative, privacy-preserving, and continuously adaptive computational medicine. Despite remarkable technological advances, 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 digital oncology intelligence platforms, emphasizing integration of AI, digital twins, and precision medicine as the future computational infrastructure for cancer care.[1]
Keywords:Digital oncology, Artificial intelligence, Foundation models, Digital twins, Precision oncology, Computational oncology, Clinical intelligence, Personalized medicine, Clinical decision support, Multimodal learning.
