A perspective on the use of health digital twins in computational pathology
This is an incremental perspective on applying digital twins to computational pathology, addressing privacy concerns for patients and researchers.
The paper discusses the potential of health digital twins to enhance computational pathology by integrating patient data into virtual models, which could transform clinical trials and real-world evidence generation.
A digital health twin can be defined as a virtual model of a physical person, in this specific case, a patient. This virtual model is constituted by multidimensional data that can host from clinical, molecular and therapeutic parameters to sensor data and living conditions. Given that in computational pathology, it is very important to have the information from image donors to create computational models, the integration of digital twins in this field could be crucial. However, since these virtual entities collect sensitive data from physical people, privacy safeguards must also be considered and implemented. With these data safeguards in place, health digital twins could integrate digital clinical trials and be necessary participants in the generation of real-world evidence, which could positively change both fields.