Clinical Report: Digital Twins of Humans in Tailored and Predictive Medicine

Overview

This review discusses the creation and application of Human Digital Twins (HDTs) in precision medicine, highlighting their potential to enhance predictive simulations of physiological behavior and treatment responses. It identifies key technologies and challenges in implementing HDTs in clinical practice.

Background

The shift towards individualized treatment in healthcare is driven by advancements in genomic sequencing, wearable technologies, and machine learning. Human Digital Twins represent a novel approach to simulate patient-specific biological processes. However, the translation of these technologies into clinical practice faces significant challenges, including model validation and ethical considerations.

Data Highlights

No numerical data or trial results were provided in the source material.

Key Findings

• Human Digital Twins (HDTs) can predict physiological behavior and treatment responses.
• Technologies supporting HDTs include physiological modeling and cloud-based computing.
• HDTs have shown potential in cardiology, oncology, genomics, and immunology.
• Challenges include the lack of model standards, interoperability, and ethical governance frameworks.
• Future research directions include developing common validation techniques and incorporating multi-omics data.

Clinical Implications

Addressing the challenges of model validation and ethical governance will be crucial for successful implementation.

Conclusion

Human Digital Twins face significant barriers that must be addressed to realize their potential in clinical settings.

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