  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.
