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  4. AI-Blockchain-Based E-Healthcare Records Management Systems: A Systematic Review
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Category: MTech Blockchain Projects
By MTech Projects
MTech Projects
11.Apr
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AI-Blockchain-Based E-Healthcare Records Management Systems: A Systematic Review

PROJECT TITLE :

Systematic Review on AI-Blockchain Based E-Healthcare Records Management Systems

ABSTRACT:

Electronic health records, also known as EHRs, are simply health records that have been saved digitally and provide information regarding a person's health. EHRs are typically shared among the various stakeholders in the healthcare industry; as a result, they are vulnerable to a variety of issues, including power outages, the inappropriate use of data, a lack of privacy and security, and the absence of an audit trail. On the other hand, blockchain is a ground-breaking technology that offers a distributed and decentralized environment. Within this environment, nodes in a list of networks are able to connect to each other without the need for a central authority to facilitate the connection. It has the potential to overcome the limitations of EHR management and to create an environment for the exchange of EHR data that is more secure, more decentralized, and safer. In addition, blockchain is a distributed ledger that allows for the storage and sharing of data in a manner that is cryptographically secure, validated, and mutually agreed upon across all mining nodes. This is achieved through the use of cryptography. Data is stored on the blockchain with an extremely high level of integrity and resiliency, and it cannot be changed in any way. The conclusions that can be drawn from using smart contracts to make decisions and carry out analytics using machine-learning algorithms can be relied on and should not be questioned. Blockchain, on the other hand, is not always indestructible and suffers from scalability and complexity issues, both of which could potentially render it inefficient. Combining artificial intelligence (AI) and blockchain technology is an effective way to handle some of the challenges presented by these two different technical ecosystems. For the purposes of learning, analyzing, and drawing conclusions, AI algorithms require data or information. The performance of AI algorithms can be improved by using data that was obtained either from a data repository or from a platform that is dependable, secure, trustworthy, and credible. Researchers have classified potential blockchain-based solutions for the management of electronic health records into three categories: conceptual, prototype, and implemented. This body of work will conduct a Systematic Literature Review (SLR) with the goal of locating and evaluating research articles that were either conceptualized or put into practice with the intention of managing EHRs through the utilization of blockchain technology. In this study, a comprehensive review of the existing research on blockchain technology and improved health record management systems that make use of artificial intelligence technologies is carried out. The research looked at 189 different research papers that were gathered from a variety of publication types. The accessibility, privacy, and scalability of publications are the primary focuses of the in-depth analysis. The SLR was able to demonstrate that blockchain technology has the potential to deliver decentralization, security, and privacy, all of which are frequently absent in traditional EHRs. In addition, the findings of the in-depth analysis provide prospective researchers with information regarding the type of blockchain that should be used in their investigations. In addition, the methods that are utilized in the healthcare industry are broken down according to their respective application areas, as well as their benefits and drawbacks. In conclusion, the highlighted taxonomy combines blockchain technology and artificial intelligence, which enables us to investigate the potential applications of blockchain technology and artificial intelligence in health records management systems. The last part of the article is a discussion on open questions for future research as well as future directions.

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