Digital twins in Oncology: AI-based Multimodal Data Framework for Cancer Patient Care
Abstract
Digital Twins (DT) have the possible to develop healthcare by allowing personalized medicine, particularly in cancer treatment. This paper proposes a novel framework for Cancer Patient Digital Twins (CPDT), aiming to enhance predictive analytics, treatment optimization, clinical decision-making, and oncology outcomes through the integration of AI-driven data fusion and real-time monitoring. The CPDT framework combines advanced technologies such as deep learning, federated learning, complex biological simulations, and quantum-assisted computing to model cancer patients as dynamic systems. By capturing and integrating multi-modal data using artificial intelligence, the framework generates continuous insights that support more precise and personalized clinical interventions. This allows clinicians to simulate treatment outcomes, evaluate risks, and make data-driven decisions tailored to individual patients, thus advancing the goals of precision oncology. The framework also facilitates diagnostic monitoring and predictive modeling at multiple biological scales, ensuring adaptability to diverse clinical scenarios. Despite its transformative possible, the CPDT approach faces important challenges, including data privacy, ethical anxieties, and computational resource demands. Addressing these issues will require ongoing innovation in methodology, robust clinical validation, and interdisciplinary collaboration among AI researchers, clinicians, and policymakers. With continued…
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