## 4 Discussion
### Directions of Further Research  
Complementing the overcoming of the fundamental limitations described above are the most promising directions for further research on GenAI in AI-based DTs for fault diagnosis for predictive maintenance in Industry 4.0/5.0. Advances in generating high-fidelity synthetic data that closely reflect real-world conditions can address data scarcity and improve model performance [78]. Research into GenAI techniques that enable real-time up \begin{table}
\begin{tabular}{c c} \hline \hline
**Limitation** & **Description** \\ \hline Dependence on data quality & GenAI models rely heavily on the quality and diversity of training data, so incomplete, uncertain, or biased data can lead to inaccurate simulations and fault diagnoses. \\ \hline Computational complexity & The computational power required to train and deploy GenAI models can be significantly higher, making it challenging for real-time applications in resource-constrained environments and cost- and energy-intensive. Regular updates and retraining of GenAI models are necessary to keep them current, which increases operational costs. \\ \hline Scenarios validity & Generated data or scenarios may not always reflect realistic or physically plausible conditions, which can lead to misleading conclusions. This often requires consulting experts. \\ \hline Model interpretability/explainability & GenAI models, especially those using DL, are often black boxes, making it difficult to understand or undermining the trust in the decisions they generate. \\ \hline Integration challenges & Integrating GenAI into existing DT frameworks can be complex and require significant AI and domain-specific expertise. \\ \hline Risk of overfitting & Generative models can overfit to specific patterns in training data, reducing their ability to generalize to unseen error conditions. \\ \hline Lack of domain-specific context & Without sufficient domain expertise incorporated into the AI model, generative AI may not account for the nuances of operational behavior of specific industrial systems. \\ \hline Dependence on AI expertise & Successful implementation requires skilled AI practitioners who understand generative models and digital twin technologies, which can be a limiting factor in many industries. \\ \hline Ethics and security concerns & Data generated by GenAI could potentially raise ethical issues or be used maliciously, such as by creating misleading error scenarios. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Limitations of AI in AI-based DTs for fault diagnostics within Industry 4.0/5.0 paradigm (own version). dates and learning can make DTs more dynamic and responsive to changing system conditions. Developing hybrid models that combine GenAI with physics/chemistry/mechanics-based simulations can increase the realism and reliability of DTs for fault diagnosis [79]. Research into interpretable/explainable GenAI (XAI) models can help build trust and make it easier to understand the insights provided by AI-based DTs. Adaptively tailoring GenAI architectures to specific industries or assets can improve their accuracy and relevance in predictive maintenance applications [80]. Furthermore, research into lightweight GenAI models can enable scalability and real-time processing in resource-constrained industrial environments. Exploring how GenAI can seamlessly integrate with IoT sensors and edge computing can enhance data collection and error detection capabilities [81]. Collaborative interdisciplinary research involving AI experts and industry practitioners can ensure that GenAI solutions are practical and tailored to real-world needs [82]. Focusing on the secure implementation of GenAI in DTs can prevent vulnerabilities related to data manipulation and model exploitation [83]. Exploring how GenAI can optimize resource utilization and minimize energy consumption aligns with Industry 5.0's emphasis on sustainability and human-centric approaches [84; 85].