Digital Twins in Personalized Medicine: Bridging Innovation and Clinical Reality

Area of Science:

• Biomedical Informatics
• Computational Biology
• Digital Health

Background:

• Digital Twins (DTs) offer real-time, multiscale simulations for personalized medicine by integrating diverse patient data.
• Potential applications span cardiology, oncology, pharmacogenomics, and neurology, yet clinical adoption is limited.
• A significant translational gap exists between digital innovation and current healthcare delivery.

Purpose of the Study:

• To review the scientific maturity and clinical use cases of DTs.
• To critically analyze barriers hindering widespread adoption, including systemic, regulatory, ethical, and infrastructural challenges.
• To propose a translational roadmap for responsible implementation in precision medicine.

Main Methods:

• Literature review and critical analysis of existing research on Digital Twins in medicine.
• Exploration of systemic, regulatory, ethical, and infrastructural barriers to clinical adoption.
• Development of a translational roadmap focusing on validation, co-development, data equity, and harmonization.

Main Results:

• DTs show significant potential for predictive, adaptive, and patient-centered decision-making in various clinical domains.
• Key barriers identified include the need for dynamic model validation, clinician involvement, equitable data, and regulatory clarity.
• Reframing DTs as cognitive tools for clinical reasoning can facilitate their integration.

Conclusions:

• Bridging the translational gap requires addressing identified barriers through a structured roadmap.
• Responsible, inclusive, and interdisciplinary implementation is crucial for establishing DTs in 21st-century precision medicine.
• DTs can become foundational elements of future healthcare by enhancing clinical reasoning and decision support.