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Review
. 2025 Aug 4.
doi: 10.1007/s10278-025-01597-1. Online ahead of print.

Digital Twin Technology In Radiology

Sara Sadat Aghamiri  1 , Rada Amin  2 , Pouria Isavand  3 , Sanaz Vahdati  4 , Atefeh Zeinoddini  5 , Felipe C Kitamura  6   7 , Linda Moy  8 , Timothy Kline  9
Affiliations
Review

Digital Twin Technology In Radiology

Sara Sadat Aghamiri et al. J Imaging Inform Med. .

Abstract

A digital twin is a computational model that provides a virtual representation of a specific physical object, system, or process and predicts its behavior at future time points. These simulation models form computational profiles for new diagnosis and prevention models. The digital twin is a concept borrowed from engineering. However, the rapid evolution of this technology has extended its application across various industries. In recent years, digital twins in healthcare have gained significant traction due to their potential to revolutionize medicine and drug development. In the context of radiology, digital twin technology can be applied in various areas, including optimizing medical device design, improving system performance, facilitating personalized medicine, conducting virtual clinical trials, and educating radiology trainees. Also, radiologic image data is a critical source of patient-specific measures that play a role in generating advanced intelligent digital twins. Generating a practical digital twin faces several challenges, including data availability, computational techniques, validation frameworks, and uncertainty quantification, all of which require collaboration among engineers, healthcare providers, and stakeholders. This review focuses on recent trends in digital twin technology and its intersection with radiology by reviewing applications, technological advancements, and challenges that need to be addressed for successful implementation in the field.

Keywords: Artificial intelligence; Digital twins; Imaging informatics; Personalized medicine; Radiology.

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Conflict of interest statement

Declarations. Ethics Approval: This research does not involve human participants, their data, or biological material. Consent to Participate: Not applicable as this research does not involve human subjects. Consent for Publication: The manuscript does not contain any patient information in any form (including individual details, images, or videos). Competing Interests: SSA, LM, and TK are members of the Machine Learning Education Sub-committee of the Society for Imaging Informatics in Medicine (SIIM). SV is a member of the SIIM Hackathon Security Sub-committee. AZ is the Deputy Editor in training of Radiology. AZ has received a Schlaeger grant from Massachusetts General Hospital. AZ is a member of Integrating the Healthcare Enterprise Committee/RIC committee at RSNA. LM is the Editor of Radiology with salary support from RSNA and serves on the Editorial Board of JMRI. LM has received grant support from the Siemens Research Grant. LM has received personal fees from Lunit Insight, ICAD. LM also serves on the ACR Data Safety Monitoring Board. FK is the Vice-chair of the SIIM Machine Learning Committee, a member of the RIC at RSNA, a member of the AI committee at RSNA, an Early Career Consultant to the Editor of Radiology, and an Associate Editor for Radiology: Artificial Intelligence. FK is also a consultant for MD.ai, a consultant for GE Healthcare, and a speaker for Sharing Progress in Cancer Care. The rest of the authors declare no competing interests.

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