## 4 SSHies: An Era of Research
### AI and Blockchain-Based Techniques in HIE Systems: A Background
Blockchain also allows for effective communication between healthcare professionals and patients, maintaining qualitative control over patient records. The study also integrates IoT and AI into blockchain technology to enhance its capabilities in the medicine field. The study emphasizes the importance of integrating EHRs and EMRs with blockchain technology, particularly in diabetes, due to its significant impact in the current world. Although implementing EHRs across the country can be challenging, this paper aims to address this issue in diabetes. The authors of [109] discussed the potential for combining machine learning (ML) and blockchain technologies in healthcare. They highlight the benefits of digital certificates, which allow users to authenticate and access data. Blockchain provides a shared ledger for authenticated users, reducing data acquisition issues. Patients can receive lifestyle advice from doctors, which is stored in the blockchain. Natural language processing can identify diseases and provide treatment suggestions, and the training model can predict outbreaks. However, the blockchain keeps a history of transactions, increasing database size. The dominance of central authority could be eliminated, and machine learning models can be directly fed with data, increasing their accuracy and efficiency. In [56], the authors conducted a meta-synthesis study aimed at analyzing the public's comprehension of the utilization of AI in healthcare and offering suggestions for its future application and administration. Employing a meta-aggregation method, the study synthesized findings from qualitative research focusing on the public's perspectives regarding AI in healthcare. The synthesized findings identified advantages of medical AI, such as huge data, efficiency, and real-time health monitoring. However, ethical and legal concerns were identified, including reliability, data security, privacy, responsibility, rights, communication, over-reliance, and economic implications. Public suggestions for medical AI application included meeting individual needs, improving transparency and credibility, and using AI as an auxiliary tool in medical practice. The study [110] aimed to improve the privacy and security of EHR by utilizing blockchain technology and AI-based multi-agent systems. The framework consists of two intelligent agents, a user interface and a DLT-based authentication agent, which validate users and generate digital certificates. Blockchain-based intelligent agents restrict access to the system. The system communicates between users and the EHR server via a network. The data set used in the experiment is available as a supplemental file. The proposed framework ensures privacy, integrity, and security metrics for electronic health records, ensuring that copies are immutable and distributed. The authors of [111] proposed a framework combining blockchain and AI algorithms to enhance security in medical healthcare. The framework consists of four main states: healthcare state, cutting-edge state, blockchain state, and data formation state. The healthcare state includes biosensors, health monitoring, and emergency observation, while the cutting-edge state collects and pre-processes patient data. The blockchain state serves as a critical gateway for reliable analysis of multiple resources across levels. The data formation state includes cloud computing, digital storage, and AI algorithms. Both states benefit from excellent authentication and authentication protocols. The research suggests that blockchain-integrated cyber-security based on artificial intelligence (BICS-AI) can help preserve medical-related information and identify severe mistakes in the medical field. The study [112] aimed to compare machine learning (ML) methods for security in electronic health record (EHR) logs. The simulation involves five main hospital modules and 19 departments, with 283.678 logs created. The data are classified according to 12 roles,and ML models are compared.