## 3 Results
### Technological Innovation and AI in Enhancing Healthcare
\begin{tabular}{} \end{tabular}  } \\  & & & & & & \\  & & & & & & \\ \cline{1-1} \cline{3-3} \cline{5-5} \cline{7-7}  & & & & & & & \\ \cline{1-1} \cline{3-3} \cline{5-5} \cline{7-7}  & & & & & & & & \\ \hline \multirow{3}{*}{[46]} & \multirow{3}{*}{2020} & \multirow{3}{*}{Saudi Arabia} & Enhancement of privacy \(\left(\text{and security of electronic health records (EHR) through }\right.\) & Use of distributed \(\left(\text{DLT}\right)\) & Proposal of a platform to ensure the integrity, privacy, and security of \(\left.\text{EHR data}\right.\) distributed \\  & & & blockchain technology & & EHR data & digital environment \\ \hline \multirow{3}{*}{[47]} & \multirow{3}{*}{2022} & \multirow{3}{*}{Italy} & Automatic classification & \multirow{3}{*}{ \begin{tabular}{} \end{tabular}  } & Development of a \(\left(\text{model for efficient}\right.\) & Challenges related to the accuracy of \(\left.\text{automatic classification}\right.\) & Challenges related to \(\left.\text{automatic classification}\right.\) \\  & & of hospital environments & & & & \\ \cline{1-1} \cline{3-5} \cline{7-7}  & & & & & & \\ \cline{1-1} \cline{3-5} \cline{7-7}  & & &  
\begin{table}
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**Ref. No. ** & **Year** & **Country** & **Focus** & **Methodology** & **Results** & **Challenges** \\ \hline
[48] & 2021 & Saudi Arabia, United States and China & Role of blockchain technology in healthcare & Systematic literature review & Identification of applications and design frameworks of blockchain in healthcare & Integration of blockchain technology into complex healthcare \\  &  
The explosive growth in adopting innovative healthcare technologies is evident in research exploring various aspects of the sector. Virtual reality, for instance, is being used innovatively to enhance doctors' skills in critical situations, showing how technology can transform the medical field [38]. The revolution in the sector is further fueled by the integration of AI, machine learning, and the Internet of Things, leading to epochal transformations [40]. A study in 2020 developed a model that improved clinical decision-making efficiency and computational costs [51], highlighting AI's potential. However, significant challenges remain in integrating these methodologies in clinical contexts and balancing accuracy, interpretability, and computational costs. Efficient health data management, aimed at maximizing efficiency and privacy, is a key element in this evolving landscape [41]. The adoption of innovative healthcare technologies utilizes AI to improve disease diagnosis and management, increasing efficiency and reducing costs. A 2019 study in Canada developed a framework to evaluate doctors' skills through simulations, offering a robust approach to assessing medical competencies [39]. Yet, integrating this framework into medical institutions and its validation remain open issues. Simultaneously, AI-based decision support systems are becoming crucial in the clinical environment, integrating into workflows and enhancing treatment efficacy and care quality [42; 43]. AI can be leveraged to efficiently utilize untapped clinical information from electronic health records (EHRs), improving the diagnosis and management of diseases such as chronic kidney disease (CKD), thereby increasing efficiency and reducing costs [52]. The importance of standardization and safety in managing health data is emphasized, reflecting the need to adapt to modern challenges [44; 45]. The interoperability and integration of health systems mark another frontier of innovation, merging technology with healthcare in revolutionary ways [46]. Further studies show how a comprehensive approach to health data management can transform care, positively affecting both patients and healthcare professionals [47]. In this evolving context, the influence of AI on clinical pathways has become particularly evident in several therapeutic areas.