Arman Process Journal (APJ)

Abstract The rapid growth of big data, coupled with advancements in data science and artificial intelligence, has significantly accelerated the potential for developing digital twins. A digital twin is a continuously updated virtual copy that enables the analysis, simulation, and prediction of a real-world object or process. Recently, applications of digital twins have seen substantial expansion across both academic communities and diverse governmental and military industries, and the healthcare sector is no exception. The concept of the digital twin for health promises a transformation in medical systems, encompassing service management and delivery, disease treatment and prevention, health maintenance, and ultimately, the enhancement of human life. By harnessing the ability to aggregate and analyze vast datasets from multiple sources, digital twins can facilitate personalized treatment pathways tailored to individual patient characteristics, medical history, and physiological data. This enables predictive analytics, preventative interventions, and the early identification of health risks and diseases through machine learning algorithms. Furthermore, digital twins can optimize clinical operations by analyzing treatment processes and resource allocation, leading to simplified and expedited treatment protocols. This review outlines the current applications of digital twins within the healthcare sector, delineates their core components in medicine, and examines the present landscape of open research opportunities. We demonstrate how the integration of diverse enabling technologies and tools—such as artificial intelligence, large language models, and mechanistic modeling—paves the way for overcoming limitations and fostering broader clinical adoption and implementation of digital twins. This review also aims to assist data scientists, clinicians, and policymakers in developing future medical digital twins and bridging the gap between this emerging paradigm's theoretical promise and practical realization.

Abstract The convergence of emerging technologies, including the Internet of Bio-Nano Things (IoBNT), Artificial Intelligence (AI), Digital Twins, and Nanomedicine, has opened new opportunities for the development of next-generation intelligent systems for cancer diagnosis, monitoring, and treatment. Despite significant advances in each of these fields, a comprehensive framework integrating these technologies into a unified, self-regulating, and closed-loop therapeutic ecosystem has not yet been established. This review proposes a forward-looking conceptual framework in which molecular data acquired by in vivo nanosensors are transmitted through IoBNT to a Nano-Digital Twin, analyzed using artificial intelligence models, and subsequently utilized to guide intelligent nanodrug delivery systems and therapeutic decision-making.
The principal contribution of this study is the integration of four key technologies IoBNT, Artificial Intelligence, Digital Twins, and Nanomedicine within a multilayer closed-loop therapeutic architecture that enables continuous monitoring, real-time analysis, disease progression prediction, and dynamic personalized treatment optimization. From a qualitative perspective, the proposed framework has the potential to improve diagnostic accuracy, enhance the specificity of targeted drug delivery, reduce reactive clinical decision-making, and facilitate the transition toward predictive, adaptive, and personalized medicine. From a functional perspective, the proposed architecture integrates five essential capabilities real-time monitoring, intelligent data analytics, disease progression prediction, adaptive nanodrug delivery, and continuous therapeutic feedback within a unified platform, whereas these capabilities have largely been investigated independently in previous studies.
Finally, this review discusses the major scientific, technical, and translational challenges associated with the clinical implementation of the proposed framework and presents a roadmap for the development of next-generation intelligent nanomedicine and precision medicine. The proposed conceptual framework demonstrates that the convergence of these emerging technologies has the potential to transform conventional healthcare into autonomous, intelligent, and data-driven therapeutic systems for future cancer management.