# Digital Twins Generated by Artificial Intelligence in Personalized Healthcare
## 3 Results
### AI-Based DT Architecture  
An AI-powered patient DT is a virtual replica that dynamically reflects a patient's physiological, psychological, and behavioural states in real time. The architecture typically integrates data from multiple sources, such as EHRs, wearable devices, and imaging systems, to create a comprehensive patient model. The system uses advanced machine learning algorithms to continuously update and refine the twin, ensuring it accurately reflects the patient's current health status. A key component is a predictive analytics engine that can simulate potential outcomes based on different treatment scenarios, helping clinicians make informed decisions. The architecture often includes a feedback loop in which the AI analyses real-time data, predicts changes, and recommends interventions that are then validated and implemented in the real world. Interoperability is key, enabling the digital twin to integrate with various healthcare systems and devices, ensuring a seamless flow of data. Privacy and security measures, such as encryption and anonymization, are integral to protecting sensitive patient data. DT models also include a user interface that provides visualizations and insights for medical professionals, enabling them to more effectively monitor and manage patient health. In advanced implementations, the twin can incorporate natural language processing (NLP) to interpret unstructured data from clinical notes and patient communications, further enriching the model. Finally, continuous learning capabilities ensure that the DT model evolves over time, adapting to new medical knowledge and the changing the health profile of an individual patient [22; 23; 24; 25; 26; 27]. DTs represent a virtual model of a physical system, object, or process in real time, which is why their accuracy and functionality rely heavily on advanced AI models and ML methods and the accuracy of the data learned and used later for model honing. At the core of DTs are AI-based models such as deep learning, reinforcement learning, and generative models, which are used to predict and optimise the performance of physical/physiological entities. In healthcare applications, neural networks, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), enable DTs to process sensor data, identify patterns, and predict future patient behaviour. These models are trained using large datasets that record historical performance, often relying on supervised learning methods to map inputs to specific outputs, such as predicting failures/illnesses or optimising operations. Unsupervised learning techniques such as clustering and anomaly detection are key in identifying patterns in unlabelled data, enabling DTs to detect unusual body system behaviour or emerging faults in real time. As a particularly effective method for decision-making in dynamic environments, reinforcement learning (RL) enables DTs to autonomously optimise processes such as autonomous systems, where the DT continuously improves its performance by interacting with its environment. Physics-based neural networks (PINNs) are also being used to embed physical laws directly into AI models, ensuring that predictions are consistent with known physical constraints, which is crucial in areas such as healthcare, aerospace, or energy systems, where deviations from physical laws can lead to dangerous consequences. The integration of generative models, such as variational autoencoders (VAEs) and generative adversarial networks (GANs), enables the creation of virtual simulations that mimic the performance of a physical twin under different conditions, helping to proactively maintain or test new configurations without affecting actual operations. In addition, transfer learning allows DTs to quickly adapt to new environments or conditions, using knowledge from one model or system to optimise another.