## 3 Results
### Security and Intelligent Platforms: AI as a Driver or Change in Healthcare
For example, in China, a big data-based decision-making model for prostate cancer has shown improvement in diagnostic efficiency, offering a concrete example of how these technologies can be applied in real clinical contexts [82]. However, challenges such as the need to ensure data privacy and the integration of these technologies into existing systems also emerge. Additionally, the use of big data and AI in rheumatic and musculoskeletal diseases (RMDs) in the European Union [84] has highlighted the ability to identify relevant data sources and analysis methods, essential for improving diagnosis and treatment of these conditions. In the field of disease diagnosis, a joint study between Saudi Arabia and South Korea [87] proposed a deep-learning model for the Internet of Medical Things (IoMT), enhancing privacy and efficacy in disease diagnosis. This approach represents a significant step forward in ensuring the security of sensitive data in connected environments. In Indonesia, the development of a smart healthcare system based on a cyber-physical system and deep federated learning architectures [85] has demonstrated how the integration of advanced technologies can improve the efficiency and security of healthcare systems, addressing challenges of both a technical and data privacy nature. In terms of data security, the implementation of blockchain technology, as illustrated in a global study [83], has significantly improved the security and interoperability of \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ref.** & **Year** & **Country** & **Focus** & **Methodology** & **Results** & **Challenges** \\ \hline
[85] & 2022 & Indonesia & Cyber-physical system-based smart healthcare system with federated deep-learning architectures with data analytics & Smart healthcare system with federated deep learning & Cyber-physical system development & Improved healthcare system efficiency and security \\ \hline
[86] & 2022 & Italy & Decentralized federated learning for healthcare networks: a case study on tumor segmentation & Decentralized learning in healthcare for tumor segmentation & Decentralized learning in healthcare for tumor segmentation & Improved brain architecture & Improved brain tumor segmentation \\ \hline
[87] & 2022 & Saudi Arabia/ South Korea & Deep-learning-enabled disease diagnosis for secure internet on medical things & Disease diagnosis in IoMT using deep learning & Privacy-preserving deep-learning model for IoMT & Enhanced privacy and effective disease diagnosis \\ \hline
[88] & 2021 & South Korea & DeepBlockShield: blockchain agent-based secured clinical data management model from the deep web environment & Securing clinical data in deep web & Blockchain-based model for data security & Enhanced security for clinical data \\ \hline
[89] & 2023 & Saudi Arabia & Managing security of healthcare data for a modern healthcare system using encryption & Healthcare data & Hybrid metaheuristic optimization with encryption & Efficient encryption-decryption process \\ \hline
[90] & 2022 & China & Research on application of meticulous nursing scheduling management based on data-driven intelligent optimization technology & Nursing scheduling optimization & Data analytics and intelligent optimization & Improved efficiency in nursing schedules \\ \hline
[91] & 2019 & India & Transforming healthcare with big data analytics and artificial intelligence: a systematic mapping study & Review of big data analytics and AI in healthcare & Systematic mapping of existing research & Evolution of research, focus, and techniques in healthcare AI \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._healthcare data. This emphasizes the need for specific technological innovations to address security challenges in an increasingly digitalized environment. These innovations also require updates in healthcare policies and medical training, to adapt to technological changes and ensure effective use of these technologies.