AI and Digital Twin Transforms in the Construction of Precision Medical Model: Healthcare Management in Smart Cities

Abstract

In recent years, the integration of artificial intelligence (AI) and digital twin (DT) is driving a new revolution in the healthcare field. Precision medical methods can utilize the complex computing techniques and models of AI, combined with various genetic and non-genetic data, to enable the system to reason and learn under the drive of data and algorithms, assisting clinical doctors and researchers in making more accurate related decisions. Research has shown that AI and DT has shown enormous technological application space in genomics, clinical cancer treatment, molecular imaging, and other fields, but it also faces potential challenges such as system bias, correlation limitations, algorithm black boxes, and unfairness. This requires the use of AI and DT transformations to build a precision medical intelligent system, which can update, capture, and study real-world data in real-time and simulate in DT. This study proposes that real-world data should be constructed from information system data and medical knowledge data from various hospitals, combined with the roles of real-world evidence (RWE), randomized clinical trial (RCT), genetic research, and AI technology in precision medicine, to innovatively design a precision medical smart system in the social 5.0 smart city. This work also proposes the structure and operating standards of the smart system, providing innovative ideas and contributions for the future construction of precision medical smart systems in society.