Translational barriers to digital twins in radiation oncology
Abstract
Digital twin research in radiation oncology has expanded rapidly across multiple domains, yet the field lacks definitional consensus and validated translational frameworks. A systematic search (PubMed, Scopus and Web of Science - September 2025) identified 903 records and six original studies met inclusion criteria. Appraisal of the available original studies revealed three recurring translational barriers: misuse of the term "digital twin" for virtual humans or patient-specific predictive models; overreliance on internal or in-silico validation; and limited benchmarking against clinically established alternatives. Progress toward clinical translation requires disciplined nomenclature, real-patient external validation, head-to-head benchmarking, explicit attention to data-pipeline and regulatory pathways.
Fields of Study & Subjects
Radiomics and Machine Learning in Medical Imaging
Effects of Radiation Exposure
Artificial Intelligence in Healthcare and Education
Publication details
- Journal / source
- Physics and Imaging in Radiation Oncology
- Publisher
- Elsevier BV
- Volume
- 40
- Pages
- 101024–101024
- Publication date
- 18 June 2026
- Language
- EN