# Digital twins: The New Frontier for Personalized Medicine?
## 4 Disease Modeling and Clinical Decision Support  
Organs, tissues, and metabolic interactions can be virtually recreated and modeled to study the progression of the disease and predict how it will affect the patient over time and their response to therapy. This computer-generated simulation of the disease state can be run within the DHT and used to monitor and predict clinical outcomes and optimize treatment plans in a truly 'holistic' approach [8].  
Data from historical medical records, genetic, histological, imaging, behavioral, and social data which are integrated to create the virtual replica of the patient or the affected organs, can be analyzed by AI-driven algorithms, and associated with similar cases to predict the progression and efficacy of therapies. Real-time supervision tools can actually help healthcare workers and organizations improve patient management. DHTs can be used to implement an alerting system in case of critical abnormalities, unexpected changes, or lack of treatment-beneficial effects, allowing a timely intervention or a proper treatment modification.  
These concepts seem particularly suitable for highly complex chronic diseases, such as multiple sclerosis, characterized by a heterogeneous course and multiple treatment strategies [21]. So far, DT systems are mostly confined to the research field and the daily clinical use is still limited, even if some organ replicas have already been developed and can be potentially used for remote monitoring of the organ functionality and to test new treatments before the actual use on patients [8].  
Some heart models have been developed combining electrophysiological ECG signals with morphological CT scan data (ECVUE(tm), CardioInsight Inc., Cleveland), yielding a round virtual representation of the patient's heart functionality, including blood flow dynamics, contracting mechanics, and electrical impulses, generating a simultaneous 3D cardiac map [24].  
These systems could be used to better characterize the disease, plan interventional procedures, and simulate the effect of a drug on cardiac dynamics and the effects of a pacemaker before actual implantation, predicting the efficacy of the treatment on the patient [8]. Similar maps are already being used to continuously remotely monitor a patient's heart activity and alert doctors when a dangerous arrhythmia occurs [25].  
Another emblematic example is provided by the elderly with type 2 diabetes, who need continuous glucose monitoring and a precise personalized drug balance in order to avoid hypo and hyperglycemia in the short term and to prevent complications in the long term. In a recent paper, [PERSON] et al. presented a DHT framework tested on 15 elderly patients, based on complex mathematical and deep-learning models able to mimic each diabetic patient, updating the data at the same time from glycemia measurements. The results show an improvement of the time-in-range from 3-75% to 86-97%, a reduction in hyper and hypo-glycemia events, and a decrease in insulin infusions by 14-29% [26].  
Furthermore, older people would particularly benefit from these technologies, as they often live alone, are not fully aware of their debilitating condition, and are unable to adequately communicate their symptoms [26].