Review of healthcare industry 4.0 application-based blockchain in terms of security and privacy development attributes: Comprehensive taxonomy, open issues and challenges and recommended solution

Qahtan, Sarah; Yatim, Khaironi; Zulzalil, Hazura; Osman, Mohd Hafeez; Zaidan, A. A.; Alsattar, Hassan A.

doi:10.1016/j.jnca.2022.103529

Cited by 31 publications

(8 citation statements)

References 181 publications

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“…4) Datasets: Reviewing the datasets used to develop transparent and interpretable AI models in healthcare reveals a diverse range of data sources. The studies cover various medical domains, including cancer, medical imaging, clinical and physiological data, and mobility and activity data [146]. This diversity reflects the growing interest and efforts in applying AI techniques to various healthcare challenges.…”

Section: B Discussionmentioning

confidence: 99%

A Systematic Literature Review on Transparencyand Interpretability of AI models in Healthcare:Taxonomies, Tools, Techniques, Datasets, OpenResearch Challenges, and Future Trends

Shafik,

Hidayatullah,

Kalinaki

et al. 2024

Preprint

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The increased utilization of disruptive health and biomedical informatics technologies, such as artificial intelligence (AI), has accelerated medical operations from patient-centered medical experience data management to simplified medical procedures in this generative era. As these technologies integrate into traditional approaches, they raise critical medical concerns, entailing transparency and interpretability of these AI models. This study conducts a systematic literature review (SLR) and presents an exhaustive review of the studies using data collection procedures and publicly available academic databases. 1837 articles published between 2014 and 2024 were obtained from eight popular academic databases: PubMed, ACM Library, Springer, Scopus, IEEE Xplore, ScienceDirect, Google Scholar, and Web of Science. A comprehensive screening process was used, and 148 articles were considered based on the relevance of the AI method to healthcare and biomedical. The studied studies demonstrate that the majority of medical people still find it complex to effectively explain the reasoning behind the decisions AI models make during biomedical experiments, leading to limited trust, biased decision-making, and unknown patient data safety. Model-agnostic strategies and explainable AI (XAI) frameworks are inspected, together with crucial datasets for training and assessment. The main challenges are AI model intricacy and regulatory compliance, while future trends highlight fairness and predisposition mitigation. Limited studies are focusing on improving AI openness, trust, and interpretability. Towards the end, it reveals that there is still a big research gap in descriptive explainable AI models in healthcare when integrating AI into clinical practice while maintaining ethical standards and patient-centric care.

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Section: B Discussionmentioning

confidence: 99%

A Systematic Literature Review on Transparencyand Interpretability of AI models in Healthcare:Taxonomies, Tools, Techniques, Datasets, OpenResearch Challenges, and Future Trends

Shafik,

Hidayatullah,

Kalinaki

et al. 2024

Preprint

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show abstract

“…In addition, a review proposed by [16] has segmented E-healthcare Industry 4.0 systems that utilise blockchain technology into two distinct categories: telecare-medical-information-systems and E-health systems. The literature review by [17] demonstrates that previous research efforts have incorporated privacy and security features creating E-healthcare Industry 4.0 systems dependent on blockchain technology. Nevertheless, no study utilised these properties as evaluation criteria to establish a standard of comparison for the existing systems.…”

Section: Related Workmentioning

confidence: 99%

“…The telecare-medical-information systems in this study were based on utilising Internet of Things (IoT) technology in conjunction with blockchain. To date, no scholarly investigation has evaluated cloud-based E-health systems incorporating blockchain technology and employing the seven fundamental privacy and security properties [17], representing a notable research gap in the existing literature.…”

Section: Related Workmentioning

confidence: 99%

“…The development of E-health systems involved the utilisation of seven privacy and security properties, namely user-authentication (UA), access control (AC), privacy-preservation (PP), secure-search (SS), integrity (Int), availability (Ava), and anonymity (Ano), as stated in [17]. To date, no research has utilised them to rank the existing systems.…”

Section: Decision Matrices Formulationmentioning

confidence: 99%

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A Decision Cloud Ranking Approach Based on Privacy and Security in Blockchain E-Health Industry 4.0 Systems

Qahtan,

Yatim,

Osman

et al. 2023

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E-health Industry 4.0 systems ranking based on Blockchain is a multi-criteria decision-making (MCDM) problem, considering the multiple evaluation properties, their significance, and data variety. The final closeness between the evaluation sample and ideal solutions also constitutes an optimisation problem. To the authors’ knowledge, no study has provided a multi-privacy and security ranking approach solution for E-health Industry 4.0 systems based on Blockchain. Consequently, this study proposes and discusses a multi-privacy and security ranking approach solution for E-health Industry 4.0 systems, utilising the SFS-FWZIC method to address the significance of properties issue and the Grey-TOPSIS method to tackle data variation, multiple evaluation properties, and the ideal solutions optimisation problem. The methodology of the proposed approach consists of three sections. First, three decision matrices are constructed based on the intersection of E-health systems with the mentioned properties, intersecting Electronic Health Records (EHRs), Electronic Medical Records (EMRs), and Personal Health Records (PHRs) with seven properties. Second, the weights of each privacy and security property are calculated using the SFS-FWZIC method. Finally, the weights determined by the SFS-FWZIC method and the three decision matrices are input into the Grey-TOPSIS method to rank E-health systems across the three categories. The findings reveal the following: (1) The SFS-FWZIC method effectively assigned weights to privacy and security properties, with access control achieving the highest significance weight (0.1934) and Secure-search receiving the lowest weight (0.0603). (2) The Grey-TOPSIS method efficiently ranked E-health systems across the three categories based on various parameters, including α=0.1,α=0.3,α=0.5,α=0.7, and α=0.9. Sensitivity and correlation analysis were conducted to evaluate the results, revealing high correlation results based on each α value across all discussed scenarios of changing property weights. The implications of this study can lead to better decision-making, improved security and privacy, increased competition, and widespread adoption, ultimately contributing to more efficient and effective healthcare delivery.

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“…Data management and connectivity through digitization are the key similarities between Industry 4.0 and blockchain technology. Recent literature combining Industry 4.0 and blockchain technology in general [58] and various applications, such as health care [59], supply chain management [60], automotive supply chain [61], manufacturing [62,63], and specifically combining IoT and blockchain technology prior to the application [64,65]. Earlier, Pan et al [66] suggested that blockchain technology might assist with integrating Industry 4.0 into EIPs.…”

Section: Combination Of Industry 40 and Blockchain Technologymentioning

confidence: 99%

Potential Application of Blockchain Technology in Eco-Industrial Park Development

et al. 2022

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The concept of an eco-industrial park (EIP) that promotes resource sharing, reutilization of waste, and exchange of materials among industries and communities brings significant benefits in terms of environmental, social, and economic aspects. EIP can be accomplished with the aid of blockchain technology to manage data and integration complexity and problem dimensionality. This work proposes the application of blockchain technology in combination with other Industry 4.0 (IR4.0) technologies as enablers for EIP development. We review the international frameworks for EIP and the IR4.0 principles and applications focusing on blockchain technology to evaluate the suitability of blockchain technology integration into the green industrial park concept. An illustrative model of a blockchain-based EIP data management platform has been proposed in this work as a starting point for exploring the full potential of this technology for future EIP applications.

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Review of healthcare industry 4.0 application-based blockchain in terms of security and privacy development attributes: Comprehensive taxonomy, open issues and challenges and recommended solution

Cited by 31 publications

References 181 publications

A Systematic Literature Review on Transparencyand Interpretability of AI models in Healthcare:Taxonomies, Tools, Techniques, Datasets, OpenResearch Challenges, and Future Trends

A Systematic Literature Review on Transparencyand Interpretability of AI models in Healthcare:Taxonomies, Tools, Techniques, Datasets, OpenResearch Challenges, and Future Trends

A Decision Cloud Ranking Approach Based on Privacy and Security in Blockchain E-Health Industry 4.0 Systems

Potential Application of Blockchain Technology in Eco-Industrial Park Development

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