Digital Twin Technology: The Future of Predicting Neurological Complications of Pediatric Cancers and Their Treatment

Abstract

Healthcare technologies have seen a surge in utilization during the COVID 19 pandemic. Remote patient care, virtual follow-up and other forms of futurism will likely see further adaptation both as a preparational strategy for future pandemics and due to the inevitable evolution of artificial intelligence. This manuscript theorizes the healthcare applications of digital twin technology. Digital twin is a triune concept that involves a physical model, a virtual counterpart, and the interplay between the two constructs. This interface between computer science and medicine is a new frontier with broad potential applications. We propose that digital twin technology can exhaustively and methodologically analyze the associations between a physical cancer patient and a corresponding digital counterpart with the goal of isolating predictors of neurological sequalae of disease. This proposition stems from the premise that data science can complement clinical acumen to scientifically inform the diagnostics, treatment planning and prognostication of cancer care. Specifically, digital twin could predict neurological complications through its utilization in precision medicine, modelling cancer care and treatment, predictive analytics and machine learning, and in consolidating various spectra of clinician opinions.