• Digital Twins in Medicine: Digital Simulation of Patients for Personalized Treatment
  • Nora Arabbaraghi,1,*
    1. Department of Biotechnology, Amol University of Special Modern Technologies, Amol, Iran


  • Introduction: Originally developed in the engineering sector to precisely model physical systems, the idea of a "Digital Twin" has just now made its way into the medical domain. A dynamic, individualized digital model of the patient is produced using this method, and it is updated on a regular basis with biological and clinical data. This model is one of the most promising instruments for achieving customized medicine since it can forecast how a disease will advance or how a patient will react to therapy (1,2).
  • Methods: Three major foundations form the foundation of digital twins in medicine. The first is input data, which includes signals from wearable technology, imaging data, omics data like genome and proteome, and electronic health records. The second is a computational engine, which can replicate the body's most intricate functions by combining mathematical physiological models with machine learning methods. Third, dynamic interaction enables the creation of a snapshot of the patient's condition and updates the digital twin with fresh information (3). This technology is currently being used in pharmacology to precisely adjust the dosage of high-risk medications, oncology to simulate tumor response to drugs, cardiovascular medicine to predict arrhythmias and choose appropriate interventions, and even hospital management and intensive care units to forecast workload and maximize resource utilization (4,5). Reviews and clinical pilots have shown that Digital Twins can help improve the accuracy of clinical decision-making, but there is still a lack of extensive evidence and sufficient randomized controlled trials (6).
  • Results: Major challenges in this area include the lack of a single definition and standardization, heterogeneity and variable quality of input data, difficulty in validating models in diverse populations, concerns about data privacy and security, high computational and infrastructure costs, and the lack of clear legal and ethical frameworks (2,7). These limitations have hindered the widespread adoption of this technology in the clinic.
  • Conclusion: Data standards, shared scientific and regulatory frameworks, large-scale trials to demonstrate clinical and economic benefit, and the integration of omics data for increased precision in customized therapy are all critical to the future of digital twins in medicine (5,6). Gaining the trust of physicians and patients also requires the development of clear and understandable models (7). Despite these challenges, Digital Twin appears to have the potential to emerge as a key component of precision medicine in the near future and revolutionize the prevention, diagnosis, and treatment of diseases (1,4)
  • Keywords: Digital Twin, Patient Digital Twin, Personalized Medicine, Computational Modeling, Digital Human