## 10 Conclusions  
The rapid development of new technologies is moving the health sector toward intelligent, efficient, and patient-centric methods of service delivery. Some of these technologies are AI, IoT, blockchain, and digital twins. Among others, digital twins are avatars of a physical system or a human, which can simulate, monitor, and analyze real-time data for advanced medical decision-making. While initially developed in industries like manufactur  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Work** & **Year** & **Taxonomy** & \
\begin{tabular}{c} **Security** \\ **Issues** \\ \end{tabular} & **Challenges** &
\begin{tabular}{c} **Future Research** \\ **Directions** \\ \end{tabular} & **Digital Twins** \\ \hline
[88] & 2021 &
\begin{tabular}{c} FL, Health \\ Informatics \\ \end{tabular} & ✗ & ✗ & ✗ & ✗ \\ \hline
[89] & 2020 & FL & ✗ & ✗ & ✗ & ✗ \\ \hline
[90] & 2021 &
\begin{tabular}{c} Federated \\ Learning, Edge \\ Computing \\ \end{tabular} & ✗ & ✗ & ✗ & ✗ \\ \hline
[91] & 2022 & FL, Healthcare & ✗ & ✗ & ✗ & ✗ \\ \hline
[92] & 2022 &
\begin{tabular}{c} FL, Big Data, \\ Healthcare \\ \end{tabular} & ✗ & ✗ & ✗ & ✗ \\ \hline
[73] & 2023 & FL, IoMT Security & ✓ & ✓ & ✗ & ✗ \\ \hline This review & 2025 &
\begin{tabular}{c} FL, Blockchain, \\ Digital Twins (DT), \\ Healthcare \\ \end{tabular} & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of characteristics of this study with previous surveys.  
ing and aerospace, their adaptation to the field of healthcare has opened new opportunities for predictive diagnostics, personalized treatment, and operational optimization. Within this context, the present paper aims to explore how digital twin technologies, in conjunction with federated learning and blockchain, can solve certain core challenges surrounding privacy, security, and interoperability in medical environments. Thus, this paper provides a thorough review of the current state of the art, a breakdown of DT architecture, implementation tools, applications in healthcare, and emerging trends driving DTs integration into clinical practice. Furthermore, there is an emphasis on the necessity of integrating intelligent and secure infrastructure to support scalable and adaptable healthcare solutions. As healthcare systems become increasingly data-driven, the ability of digital twins to bridge real-world entities and virtual simulations offers transformative potential.  
This review also emphasizes that federated learning facilitates the collaborative development of machine learning models across decentralized health datasets without the requirement to share sensitive patient information, thus maintaining privacy. Blockchain, concomitantly, establishes secure, transparent, and tamper-proof mechanisms for managing data, instilling trust in the digital healthcare system. With such integration, these technologies strengthen digital twin systems' capabilities and credibility while solving problems related to data protection, reliability, and ethical compliance. Additionally, the integration of these technologies supports real-time, adaptive healthcare interventions that can respond dynamically to patient conditions. This paper further explores implementations in real world scenarios and monitors future research directions that can be undertaken to realize the full implementation of digital twins in next-generation intelligent and secure healthcare systems.  
Writing--original draft preparation, E.E.-D.H. and A.S.; writing--review and editing, E.E.-D.H. and A.S. All authors have read and agreed to the published version of the manuscript.  
The authors would like to acknowledge Prince Sultan University for paying the article processing charges (APCs) of this publication.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors would like to thank Prince Sultan University for their support.  
The authors declare no conflict of interest.