Challenges and opportunities for digital twins in precision medicine: a complex systems perspective

TL;DR

This paper discusses the potential of digital twins in precision medicine, emphasizing the need for hypothesis-driven models like multiscale modeling to enhance clinical accuracy and decision-making.

Contribution

It advocates for integrating generative models with big data and complex systems theory to improve digital twin applications in healthcare.

Findings

Multiscale modeling enhances clinical relevance of digital twins.

Scenario-based modeling supports personalized therapeutic strategies.

Integration of complex systems improves disease simulation accuracy.

Abstract

The adoption of digital twins (DTs) in precision medicine is increasingly viable, propelled by extensive data collection and advancements in artificial intelligence (AI), alongside traditional biomedical methodologies. However, the reliance on black-box predictive models, which utilize large datasets, presents limitations that could impede the broader application of DTs in clinical settings. We argue that hypothesis-driven generative models, particularly multiscale modeling, are essential for boosting the clinical accuracy and relevance of DTs, thereby making a significant impact on healthcare innovation. This paper explores the transformative potential of DTs in healthcare, emphasizing their capability to simulate complex, interdependent biological processes across multiple scales. By integrating generative models with extensive datasets, we propose a scenario-based modeling approach that enables the exploration of diverse therapeutic strategies, thus supporting dynamic clinical decision-making. This method not only leverages advancements in data science and big data for improving disease treatment and prevention but also incorporates insights from complex systems and network science, quantitative biology, and digital medicine, promising substantial advancements in patient care.