# Digital Twins Generated by Artificial Intelligence in Personalized Healthcare
## 3 Results
### AI-Based DT Building  
The proposed steps for building AI-generated DTs in personalized healthcare are as follows (Table 2). For the purposes of this study, it was assumed that DTs can be applied to both a sick patient and a healthy person (e.g., an athlete) in the context of preventive medicine [33,34]. Therefore, DTs must reflect both physiological and pathological processes/states. The integrated digital twin model in personalised healthcare, generated by AI, is a dynamic, virtual representation of a patient's physiological and medical state. It is constantly evolving, reflecting real-time input from wearables, EHRs, and genetic information. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Stage** & **Stage Name** & **Tasks** \\ \hline
1 & Data collection & Collecting comprehensive and diverse patient data, including electronic health records, genetic information, lifestyle data, and real-time data from wearable devices, to create a detailed foundation for the digital twin. \\ \hline
2 & Data integration and preprocessing & The collected data undergoes integration and pre-processing, where data from different sources is standardized, cleaned, and organized (e.g., normalized, balanced) to ensure consistency and readiness for modelling. \\ \hline
3 & Model/algorithm selection and development & AI algorithms and ML models are selected and developed to represent the patient’s physiological systems, health status, and potential responses to treatment. This can be carried out based on pre-prepared initial templates for easier initialization. \\ \hline
4 & Simulation and calibration & The DT is simulated using the processed data, and the model is calibrated to reflect the patient’s actual current health status by adjusting parameters to ensure that the DT accurately reflects the patient’s unique characteristics important for clinical diagnosis. Also, the directions and degree of their changes should be the same as those of the patient. \\ \hline
5 & Validation and testing & The constructed DT undergoes rigorous validation and testing against real-world outcomes to ensure that it accurately predicts the patient’s health trajectory and response to various interventions. \\ \hline
6 & Personalization and adaptation & The DT is then personalized by incorporating patient-specific factors and continually updating the model with new data to reflect any changes in the patient’s health over time and to keep the DT current (concurrent with the physical original). \\ \hline
7 & Integration into clinical processes & The DT is integrated with clinical processes, providing healthcare providers with a tool for real-time decision support, treatment planning, and monitoring patient progress. Initially, this may be carried out using the infrastructure of medical simulation centres. \\ \hline
8 & Feedback and refinement & As a DT is used in clinical practice, continuous feedback is collected from medical professionals and healthcare providers and results are monitored, leading to the continuous refinement and improvement of the model (both the technology itself, its engine and templates, and the specific DT of a healthy patient or one with specific conditions). \\ \hline
9 & Ethical and regulatory compliance & Ensuring DT compliance with ethical standards and regulatory requirements is key, with attention paid to patient consent, data privacy, and legal issues. \\ \hline
10 & Implementation and monitoring & Finally, the DT is implemented for use in personalized healthcare, with continuous monitoring to track its performance and effectiveness and to make further changes if necessary. \\ \hline
11 & Continuous improvement & The DT is never finished; it constantly evolves with changes in technology and the patient’s health, and after the patient dies and is anonymized, it serves as a basis for the teaching, learning, simulation, and development of the next generation of DTs. \\ \hline \hline \end{tabular}
\end{table}