## 2 Literature Review
### Artificial Intelligence (AI)-Based Digital Twins (DTs): Benefits and Challenges
#### 2.3.1 Artificial Intelligence (AI)-Based Digital Twins (DTs): Benefits  
Regarding applications, artificial intelligence aims to mimic the basis of intelligence to create a new intelligence system that can respond similarly to human intelligence [10]. With the assistance of neural networks, machine learning, deep learning, and expert systems [47], AI can support DTs by offering an advanced analytical tool that can automatically analyze the collected data and produce insightful information, forecast results, and offer advice on how to prevent possible issues [24]. For more clarification, with the rapid advancement of digitalization, artificial intelligence (AI) has become a core enabler of digital twin systems. Key AI technologies such as deep learning (DL), machine learning (ML), natural language processing (NLP), and computer vision all enhance the intelligence, adaptability, and automation capabilities of digital twins. To illustrate this, ML supports real-time decision making and predictive modeling [48]. Deep learning, especially convolutional neural networks, aids image-based infrastructure monitoring [49]. NLP enables human-machine interactions by interpreting textual data [50], while computer vision enhances situational awareness in fields such as autonomous driving [51]. Together, these technologies drive \begin{table}
\begin{tabular}{c c c} \hline
**No.** & **Challenges** & **References** \\ \hline
1 & High implementation costs & [10; 28; 43; 44; 45] \\
2 & Lack of data standardization/lack of a standardized & [10; 46; 10] \\
3 & Lack of data management & [10; 20; 46] \\
4 & Challenges of connectivity privacy & [10; 46] \\
5 & Increased demand for power and storage & [10; 43] \\
6 & Storage integration difficulties with existing systems & [10; 43] \\
7 & The complexity of its architecture & [10; 28; 43] \\
8 & Lack of IT infrastructure & [10; 44; 46] \\
9 & Data quality issues, e.g., poor and missing data & [45; 46] \\
10 & Sensitive data privacy and security & [10; 44; 46] \\
11 & Lack of trust & [46] \\
12 & Lack of positive and negative expectations of & [28; 46] \\
13 & digital twins’ needs & [19; 28; 44] \\
14 & Resistance to change to the new system & [14; 44; 45] \\
15 & Issue of interoperability with various sensors & [20; 28] \\
16 & Lack of national guidelines/codes of practices & [44; 45] \\ \hline \end{tabular}
\end{table} 
Table 2: Challenges of using DTs. the evolution of digital twins across various sectors, including healthcare, energy, and smart cities. In this context, and according to the literature review, AI-driven DT technology offers three main benefits: (1) AI enhances DTs' modeling of complex real-world systems, (2) it supports strategic decision-making processes, and (3) AI capabilities improve data analytics and insights. Numerous examples in the literature showcase the current research on smart cities in relation to DTs. Key use cases include the monitoring and comparison of energy consumption, renewable energy integration (e.g., wind turbines), smart grids, intelligent transportation systems, and connected vehicles. For instance, to monitor and compare energy consumption based on environmental factors and human activities, [PERSON] et al. [52] propose the \"mySMARTlife\" framework, which leverages IoT advancements across cities to develop a smart city digital twin. Integrating renewable energy sources into the energy grid is a significant area of implementation for digital twins, particularly in developing smart cities where reliable and efficient energy delivery is essential. In this context, [PERSON] et al. [53] introduced a cloud-based digital twin monitoring system designed to support farms' development and real-time supervision, enabling continuous monitoring, analysis, and optimized energy distribution. Similarly, [PERSON] et al. [54] present a case study investigating the deployment of wind farms and energy consumption within smart grid environments. Furthermore, digital twin technologies extend into traffic management.