## 5 Conclusions  
DT technologies, including those based on GenAI, enable early detection and correct diagnosis of faults, which will facilitate corrective actions to replace predicted damaged components before failures occur. The number of publications on GenAI-based DTs is not large in relation to the needs, nor does it cover all the observed research gaps, which is why there should be more emphasis on interdisciplinary scientific and economic cooperation in this area. This applies to both collected and generated data, as well as entire environments for their processing. This will not only allow for maintaining control over the development of this group of solutions, but also for their standardization and synchronization of development with the consideration of Explainable AI (XAI). With respect to the previously observed knowledge and experience gaps, it can be said that Generative AI plays a key role in AI-based DTs for fault diagnosis and predictive maintenance in Industry 4.0 and 5.0. By creating realistic simulations, it enables accurate modeling of machine behavior under different conditions. These AI-driven DTs continuously learn from real-time data, enhancing the ability to detect and predict faults. Generative AI helps in synthesizing missing or sparse failure data, improving diagnostic accuracy. It also enables adaptive maintenance strategies by predicting potential failures before they occur. This reduces downtime, minimizes maintenance costs, and optimizes resource utilization. Moreover, the integration of Generative AI with IoT and edge computing improves real-time monitoring and decision-making. The synergy between AI and digital twins facilitates a proactive, data-driven approach to maintenance in smart industries. As Industry 5.0 emphasizes human-AI collaboration, Generative AI increases interpretability and decision support for human operators. Using Generative AI in DTs transforms predictive maintenance, ensuring reliability, efficiency, and sustainability in industrial operations. In predictive maintenance, GenAI DTs enable realistic operational profiles, identifying potential failure modes that traditional methods may miss. New opportunities in GenAI-based DTs include:  
* Incorporating XAI to enhance decision-making clarity and improve reliability in key industries such as manufacturing, energy, and even employee healthcare as part of preventive medicine;
* Emphasizing a human-centric approach, so GenAI-based DTs can better integrate with human operators to support collaboration and decision-making;
* Implementation of edge AI and distributed computing further increases the scalability and real-time capabilities of DT, and federated learning ensures data privacy. In this way, increasingly effective DT technologies will increasingly cooperate with operators within the Industry 5.0 paradigm. Challenges remain in managing computational complexity, ensuring data security, and addressing ethical issues during implementation. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15063166/s1](https://www.mdpi.com/article/10.3390/app15063166/s1)]([https://www.mdpi.com/article/10.3390/app15063166/s1](https://www.mdpi.com/article/10.3390/app15063166/s1)), Partial PRISMA 2020 Checklist. Conceptualization, E.M., D.M., T.M. and T.P. ; methodology, E.M., D.M., T.M. and T.P. ; software, E.M., D.M., T.M. and T.P. ; validation, E.M., D.M., T.M. and T.P. ; formal analysis, E.M., D.M., T.M. and T.P. ; investigation, E.M., D.M., T.M. and T.P. ; resources, E.M., D.M., T.M. and T.P. ; data curation, E.M., D.M., T.M. and T.P. ; writing--original draft preparation, E.M., D.M., T.M. and T.P. ; writing--review and editing, E.M., D.M., T.M. and T.P. ; visualization, E.M., D.M., T.M. and T.P. ; supervision, T.M. ; project administration, T.M. ; funding acquisition, T.M. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. No new data set was generated.