Objective:

To explore the creation and application of Human Digital Twins (HDTs) in predictive medicine, focusing on technological foundations, clinical applications, and implementation challenges.

Approach:

Key Findings:

• HDTs can enhance personalized prediction of treatment responses and optimize clinical trial designs.
• Current challenges include the lack of model standards, interoperability, and ethical governance mechanisms.
• Key research directions include developing common-validation techniques and incorporating multi-omics data into physiological models.

Interpretation:

The review synthesizes existing knowledge on HDTs, highlighting their potential in precision medicine while addressing significant barriers to clinical implementation.

Limitations:

• Insufficient predictive modeling of individual responses to therapies.
• Heterogeneous methodologies for validating predictive models.
• Lack of systematic governance frameworks for ethical deployment.

Conclusion:

The review provides a comprehensive framework integrating technical, clinical, regulatory, and ethical factors related to HDTs, offering practical suggestions for future research and implementation.

Attribution Notice

This content is an AI-generated, fully rewritten summary based on a published scholarly article. It does not reproduce the original text and is not a substitute for the original publication. Readers are encouraged to consult the source for full context, data, and methodology.