Due to the availability of extensive real-world data (RWD), such as electronic health records (EHR), it has emerged as a crucial cornerstone for advanced machine learning algorithms. This allows for effective decision making and applications by addressing confounding issues present in observational data. In recent years, Digital Twins (DTs) have emerged as digital counterparts that mirror physical components, processes, or systems. They provide virtual representations that can be used for simulations, evaluations, optimisations, and virtual testing. In the context of healthcare, this technology has gained increasing significance, offering transformative potential to reshape patient care and facilitate more personalized, intelligent, and proactive approaches to patient well-being. This transformative potential extends to various facets of healthcare, from prediction, diagnostics, treatment, prognostics strategies and resource allocation. Integration of digital twin technology emerges as a promising solution to meet these evolving demands.

Digital twins is particularly useful in medicine as it allows for the development of virtual models to simulate interactions between molecules, simulate response models to physicochemical stimuli of cells, tissues, and organs, up to the simulation of macroscopic clinical effects on the entire individual. Digital twins also have an even broader application when the strict similarity to their biological counterpart is relaxed; in this case, an interesting variation related to the theme is the production of entirely synthetic patients, without a real counterpart, but still sufficiently plausible to be representative of the same statistical characteristics concerning variables of interest. In this perspective, systems for generating synthetic datasets represent an interesting evolution of the digital twin paradigm, offering valuable solutions to issues such as privacy protection or data protection, for instance, in the management of multicenter studies. The purpose of this call is to collect high-quality articles or reviews on how the concept of digital twins can be interpreted and applied practically applied in the fields of genetics or clinical practice, from both the perspectives of health sciences and mathematical methodology.

Subtopics of interest include, but are not limited to, the following:

•Digital Twins in genetics
•Digital Twins in -omics sciences
•Digital Twins in genetics medicine
•Generation of synthetic digital patient cohorts.
•Data fusion and interoperability augmenting AI applications to personalized medicine
•Digital Twins platforms for real-time monitoring and chronic disease management
•Digital biomarkers from wearables and mobile devices for disease prediction
•Digital Twins applicable to telehealth and remote patient monitoring
•Transformative AI technologies in mental health care
•AI-driven fusion of pathology imaging and genetic data for cancer diagnosis
•Personalized medication management through AI analysis of EHR and pharmacy data
•Ethical and regulatory considerations for Digital Twins in Healthcare
•Data privacy and security in synthetic patient cohorts
•Multimodal data integration for comprehensive health profiles and digital twins

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Intelligent Digital Twins in Medicine