A perspective on the use of health digital twins in computational pathology

TL;DR

This paper discusses the potential of health digital twins in computational pathology, emphasizing their ability to integrate diverse patient data for improved clinical insights while highlighting privacy considerations.

Contribution

It provides a perspective on how digital twins can enhance computational pathology and discusses privacy safeguards necessary for their implementation.

Findings

Digital twins can integrate multidimensional patient data.

They could facilitate digital clinical trials and real-world evidence generation.

Privacy safeguards are essential for their adoption.

Abstract

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.