## 5 Discussion
### Advantages of the Proposed Approach  
The data collection methods described in Section 2 provide the foundation for the comparative analysis presented in Sections 3 and 4, but the relationship between them requires further explanation. Section 2 presents the tools, sampling strategies, and criteria used to gather information on digital twin architectures and performance metrics, and Sections 3 and 4 explain how these specific datasets impacted the categorization of \"simple\" and \"complex AI-based\" digital twins. Section 3 discusses architectural differences and device integrations, and Section 4 discusses resilience and sustainability metrics. The proposed novel approaches to AI-based DTs for Industry 4.0/5.0 offer numerous advantages across various industrial applications. First, they enhance real-time decision making by integrating AI-driven predictive analytics, enabling proactive responses to operational challenges. The use of advanced machine learning models allows DTs to continuously improve by learning from new data, increasing accuracy in fault detection and process optimization. Cloud-edge hybrid computing architectures ensure low-latency processing, making DTs more responsive in time-sensitive industrial environments. The integration of IoT devices enables seamless data collection and synchronization between physical and virtual systems, improving operational efficiency. AI-driven DTs support predictive maintenance by identifying equipment failures before they occur, reducing down-time and maintenance costs. In smart manufacturing, DTs optimize production scheduling \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature/Area** & **Emerging Trends** & **Research Gaps** & **Dominant Themes** \\ \hline IoT integration & Seamless data flow between physical assets and digital twins via connected devices & Data heterogeneity, latency issues, and lack of interoperability among IoT platforms & Continuous monitoring, remote diagnostics, and asset connectivity \\ \hline Edge computing & Local (close-to-the-source) processing for latency-sensitive applications and real-time personalization & Scalability of models across distributed edge nodes & Real-time inference, privacy-preserving analytics, decentralized intelligence \\ \hline AI/ML & Adaptive AI models that learn user/asset-specific behavior over time & Explainability, transfer learning for limited-data environments & Predictive maintenance, anomaly detection, intelligent decision support \\ \hline Human-in-the-loop & Interactive interfaces for user feedback and real-time customization & Modeling human behavior and integrating user intent into system logic & User-centric design, collaborative control, intuitive interfaces \\ \hline Cybersecurity and privacy & Personalized access control and encrypted communication protocols & Robustness against adversarial attacks in real-time AI-based control systems & Trustworthiness, secure data exchange, compliance with General Data Protection Regulation (GDPR)/industry standards \\ \hline Standardization and Interoperability & Push for open architectures and common modeling frameworks & Absence of widely adopted standards for AI-driven personalization components & Model sharing, cross-platform compatibility, modular twin architecture \\ \hline Domain-specific DTs & Customization for aerospace, manufacturing, healthcare, etc. & Lack of domain-specific datasets and validation benchmarks & Application-tailored modeling, fine-tuned simulations, task-specific optimization \\ \hline Simulation and feedback & Bi-directional learning between real and virtual systems & Closed-loop validation of adaptive models remains underexplored & Continuous learning, simulation-informed control strategies \\ \hline Sustainability and energy efficiency & Green AI techniques and energy-aware modeling & Balancing personalization complexity with energy/resource constraints & Low-power design, eco-conscious computation, sustainable system integration \\ \hline \hline \end{tabular}
\end{table}