The Digital Twin in Medicine: A Key to the Future of Healthcare?

TLDR

The growing demand for precise diagnosis and personalized treatment has positioned the digital twin—an engineering concept linking physical entities to digital models—as a promising, though still developing, platform for healthcare, despite challenges in data collection, fusion, and accurate simulation. This paper introduces digital twin technology and examines its potential benefits and limitations for medical applications. By integrating Big Data, the Internet of Things, and artificial intelligence, the authors propose that digital twins can construct high‑resolution patient models to enable precise diagnosis and personalized treatment.

Abstract

There is a growing need for precise diagnosis and personalized treatment of disease in recent years. Providing treatment tailored to each patient and maximizing efficacy and efficiency are broad goals of the healthcare system. As an engineering concept that connects the physical entity and digital space, the digital twin (DT) entered our lives at the beginning of Industry 4.0. It is evaluated as a revolution in many industrial fields and has shown the potential to be widely used in the field of medicine. This technology can offer innovative solutions for precise diagnosis and personalized treatment processes. Although there are difficulties in data collection, data fusion, and accurate simulation at this stage, we speculated that the DT may have an increasing use in the future and will become a new platform for personal health management and healthcare services. We introduced the DT technology and discussed the advantages and limitations of its applications in the medical field. This article aims to provide a perspective that combining Big Data, the Internet of Things (IoT), and artificial intelligence (AI) technology; the DT will help establish high-resolution models of patients to achieve precise diagnosis and personalized treatment.