# Digital Twins Generated by Artificial Intelligence in Personalized Healthcare
## 3 Results
### AI-Based DT Clinical Use  
DTs can use AI models to predict future health outcomes for individual patients in the form of an AI-generated digital twin to support the rapid assessment of intervention strategies in silico. We gain the ability to update real-time data from our physical patients and connect to multiple diagnostic and therapeutic devices. To date, at least several methods have been defined for the use of AI-generated DTs in personalized healthcare. By analysing a patient's DT, physicians can predict the onset of diseases or complications before they manifest in various scenarios, enabling early intervention and potentially preventing adverse health events and reducing the invasiveness and cost of therapeutic interventions. DTs can be used to simulate a patient's response to different treatment options, allowing healthcare providers to tailor treatments based on DT predictions, thereby optimizing outcomes for the individual. DTs can be used to model a patient's response to different medications, helping to identify the most effective medications and doses while minimizing side effects, especially in cases where personalized drug therapy is crucial. Surgeons can use DTs to plan complex surgeries by simulating different procedural approaches and predicting outcomes, thereby reducing risk and improving surgical precision. DTs enable the continuous monitoring of a patient's health through real-time data analysis, allowing for timely adjustments to treatment plans and the management of chronic conditions. Interestingly, patients can interact with their DTs to better understand their health status, the impact of lifestyle choices, and the potential outcomes of different treatments, allowing them to make more informed decisions. With improved diagnostics and prognosis, AI has the potential to revolutionize various fields of medicine by supporting medical professionals for AI-cased DTs. The implementation of omni-channel engagement in digital health interventions and digital twins allows the flexibility of personalization that can increase and sustain patient engagement in digital health interventions and, ultimately, the quality of care and patient outcomes [40,41]. [PERSON] et al. proposed to supplement structured cancer patient data with clinical concepts extracted from unstructured text (e.g., hospital discharge notes) using NLP techniquesto extract disease and drug names and link them to medical concepts using medical dictionaries. These data included richer information on patient symptoms than tabulated diagnosis records and could be a useful supplement, further enhanced by a drug safety module that captures suspected adverse drug reactions caused by the concomitant use of anticancer drug pairs [42]. In the study by [PERSON] et al., DTs support the monitoring of respiratory failure, accelerating necessary interventions, helping to personalize treatment plans, and providing decision support to healthcare professionals. Respiratory data were collected using an ESP32 Wi-Fi Channel State Information (CSI) sensor and statistically augmented with time-series data to generate larger synthetic respiratory data with the comparative analysis of the experimental and synthetic datasets. These data were then denoised (smoothed and filtered) and dimensionality reduced using principal component analysis (PCA). The patient's breaths per minute (BPM) were estimated from the raw patient data with an accuracy of 92.3% from the raw respiratory data and an accuracy of 89.2% and 83.7% from the combined real and synthetic respiratory datasets with the larger synthetic dataset, respectively [43]. The study by [PERSON] et al. demonstrates an approach to the diagnosis and treatment of chest diseases known as Lung-DT, which is based on IoT sensors and AI algorithms and is used to establish a digital representation of a patient's respiratory health.