## 4 SSHies: An Era of Research
### AI and Blockchain-Based Techniques in HIE Systems: A Background  
The study [106] focused on constructing a secure health monitoring system in healthcare 5.0 using blockchain technology and federated learning. The proposed approach involves implementing a blockchain module, RTS-DELM module, federated learning module, data fusion module, and intrusion detection system (IDS) module. Federated learning combines training data from multiple sources to build a global model without directly sharing data sets, ensuring patient privacy. Data fusion techniques combine information from multiple sensors for more precise observations. The proposed approach is efficient in detecting intrusions, with the experiment using the publicly accessible [PERSON]'s disease data set. In [107], the study aims to improve access to health care data in community pharmacies through the adoption of blockchain technology and AI. The framework involves a scoping review of literature, using an AI algorithm to identify specific areas for implementation, and a process model to optimize clinical outcomes. Two areas for optimal use of blockchain technology in community pharmacies are assessed and implemented. The results show that blockchain technology can advance interoperability, improve continuity of care, patient safety, and medical safety. It also provides guidance on how pharmacies can pilot blockchain technology in practice, using a user-centered design approach to improve pharmacists' tasks and population health. \begin{table}
\begin{tabular}{c c c c c} \hline
**Ref.** & **Year** & **Method** & **Data Set** & **Performance** \\ \hline
[95] & 2020 & Action-HER & \
\begin{tabular}{c} StonyBrook \\ University \\ \end{tabular} &
\begin{tabular}{c} Security = 89.96\%, \\ Privacy = 91.78\% \\ \end{tabular} \\ \hline
[96] & 2019 & \begin{tabular}{c} Direct, look-up, \\ patient-centered, \\ blockchain \\ \end{tabular} & \
\begin{tabular}{c} 2013 participants \\ \end{tabular} &
\begin{tabular}{c} Ensures privacy \\ and trust \\ \end{tabular} \\ \hline
[47] & 2021 & \
\begin{tabular}{c} End-to-end \\ blockchain framework & NA &
\begin{tabular}{c} Scalability 89.8\%, \\ low latency \\ \end{tabular} \\ \hline
[97] & 2020 & \
\begin{tabular}{c} Encryption, mobile \\ application, \\ fragmentation \\ \end{tabular} & EHR from ONC &
\begin{tabular}{c} Latency \textless{} 100 ms/ \\ 220 MB EHR \\ \end{tabular} \\ \hline
[48] & 2020 & \
\begin{tabular}{c} Access control \\ framework \\ \end{tabular} & NA &
\begin{tabular}{c} Privacy = 91\%, \\ Security = 89.98\%, \\ Attack Resistant \\ = 91.27\% \\ \end{tabular} \\ \hline
[99] & 2020 & \
\begin{tabular}{c} Blockchain and smart \\ contract-based \\ framework \\ \end{tabular} & NA &
\begin{tabular}{c} High security, \\ and efficiency \\ \end{tabular} \\ \hline
[101] & 2019 & \
\begin{tabular}{c} Personal health \\ records sharing \\ scheme \\ \end{tabular} & NA &
\begin{tabular}{c} Privacy = 91.88\%, \\ Integrity = 88.97\% \\ \end{tabular} \\ \hline
[103] & 2020 & \
\begin{tabular}{c} Authentication and \\ authorization framework \\ \end{tabular} & NA &
\begin{tabular}{c} Low execution \\ time, high \\ throughput, \\ and scalability \\ \end{tabular} \\ \hline
[104] & 2018 & \
\begin{tabular}{c} Blockchain-based \\ information scheme \\ management system \\ \end{tabular} & NA &
\begin{tabular}{c} Latency \textless{} 1000 ms \\ /100 users \\ \end{tabular} \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._The study in [49] explores the theories, methodologies, and breakthroughs in AI-powered information fusion for smart health. It focuses on three questions: achieving human-level intelligence in smart health, achieving and benefiting smart health from a multidisciplinary balance, and utilizing the power of big data for smart health. The study of [108] focused on diabetes disease by integrating blockchain technology into EMRs and EHRs. The authors use consensus mechanisms like PoW, Pos, PoB, BFT, and smart contract to integrate EMRs and HERs. Data are encrypted after storage, ensuring security.