Digital Twins: From Personalised Medicine to Precision Public Health

Abstract

A digital twin is a virtual model of a physical entity, with dynamic, bi-directional links between the physical entity and its corresponding twin in the digital domain. Digital twins are increasingly used today in different industry sectors. Applied to medicine and public health, digital twin technology can drive a much-needed radical transformation of traditional electronic health/medical records (focusing on individuals) and their aggregates (covering populations) to make them ready for a new era of precision (and accuracy) medicine and public health. Digital twins enable learning and discovering new knowledge, new hypothesis generation and testing, and in silico experiments and comparisons. They are poised to play a key role in formulating highly personalised treatments and interventions in the future. This paper provides an overview of the technology's history and main concepts. A number of application examples of digital twins for personalised medicine, public health, and smart healthy cities are presented, followed by a brief discussion of the key technical and other challenges involved in such applications, including ethical issues that arise when digital twins are applied to model humans.

Keywords: digital twins; human digital twins; personalised medicine; precision medicine; precision public health.

Figure 1

Composition of a digital twin. Note the bidirectional link between the real world (human body and its component parts) and the corresponding digital twin.

Figure 2

Key concepts and examples of human digital twins: digital twin types and aggregates. From left to right: Different types of digital twins can be conceived, covering the whole human body, one body system or body function (e.g., digestive system), one body organ (e.g., stomach or liver), one cell of a given type, or even simply some specific subcellular (organelle/sub-organelle) or molecular level of interest within a cell. Digital twins can equally cover healthy/normal and diseased entities; for example, a diseased cell (e.g., a cancerous cell of some given type), a diseased organ (e.g., a fatty liver in type 2 diabetes), or a disease or syndrome affecting the whole body or multiple parts of it (e.g., Parkinson’s disease). Healthcare institutions (e.g., a hospital) can also have their corresponding ‘digital twins of organisations’. Left inset: Aggregates of the corresponding digital twins belonging to different individuals can be used to represent one family, population group, or whole population.

Figure 3

Digital twin instances of the same person or patient can be used for in silico testing and comparison of different treatment or preventive intervention possibilities to find out which option will work best for that particular individual. Note the data flow from clinical sensors, DNA (deoxyribonucleic acid) sequencing, and other data sources and streams to the digital domain.