## 7 Discussion
One must then find a reduced coupled model behaving exactly physically but with a reduced execution time. In summary, the physical complexity introduces a mathematical complexity, and, for a fast matching, the latter must be reduced but can still represent the former. Thus, reducing a model comprises hurrying its execution while degrading its precision as slim as possible [126]. The dilemma is then often to attain the boundary among saving time and deteriorating correctness. A reduced (surrogate) model is therefore substituted [92; 93] for the superior model to obtain a pre-sizing. In addition, one can exercise non-intrusive stochastic methods (e.g., kriging and polynomial chaos) [127; 128] that spend 3-D FEM computations with a contained set of realizations (training trials), thus offering efficient (reduction) metamodels. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Type of** & & & & & & \\
**Intervention** & **MI** & **Cut Trace** & **Healing Time** & **Visualization** & **Tool Control** & **Surgical Dexterity** & **Staff Ease and Ergonomics** \\ \hline Open & No & Large & Elongated & Directly & Hand & Moderate & Difficult \\ \hline Laparoscopic & Yes & Small & Short & Laparoscope & Hand & Moderate & Moderate \\ \hline Robotic & Yes & Small & Short &
\begin{tabular}{c} Pre-images \\ + Computer \\ \end{tabular} & Robot & Good & Good \\ \hline Image-guided & Yes & Small & Short & Scanner & Robot & Accurate & Comfortable \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summarized indicative comparison of the different types of interventions. * Regarding the potential and challenges of using DTs, in addition to studies indicating that their use in the healthcare sector is comprehensive and offers a wide variety of content, other analyses highlighted the challenges and difficulties related to technical fragility as well as data security and confidentiality, which are all obstacles to overcome. DTs in the healthcare sector illustrate a promising fusion of cutting-edge technologies such as AI, IoT, Big Data, and VR to design treatments that improve the efficiency of care. They have transformative potential at various levels of healthcare, from treatment planning to clinical trial design. Their intrinsic ability to provide personalized, prognostic, and dynamic models of specific patients could significantly improve health outcomes, and their ability to replicate complex organizations and leverage available data offers the potential to generate the performance sought by both healthcare providers and technology manufacturers. Nevertheless, DTs are not without challenges. From a technical perspective, the integration of composite technologies, initially not designed to work together, and the need for task synchronization pose a significant challenge. The quality of data shaping DT policy, including its likely direction, the boldness of models, and the diversity of data origins, are just some of the real weaknesses of DTs that need to be seriously assessed to ensure their accuracy and consistency. Moreover, the socio-ethical inferences of DTs are complex. Issues of security and privacy, trust in DT systems, and availability disparities require careful consideration and tailored policy responses [129; 130]. * The three different MRI fields B\({}_{0}\), B\({}_{1}\), and G are assumed to be protected and secure. In fact, the use of MRI scanners is normally safe for patients and medical personnel in the case of using common scanners with moderate efficiency (static field strength and gradient output). For recent high-performance scanners, some traumatic concerns could be observed. Actually, in such circumstances, the EMFs of the scanner can trigger possible uncomfortable side effects for patients or nearby personnel. The RF B\({}_{1}\) field reflects a trivial intensity for a negligible duration, and the corresponding tissues that induced currents are insignificant.