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Use of Emerging Technologies in Public Health: Blockchain and AI
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Use of Emerging Technologies in Public Health: Blockchain and AI

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Digital Health".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 9539

Special Issue Editors

Dr. Fariza Sabrina

E-Mail Website
Guest Editor
School of Engineering and Technology, Central Queensland University, Sydney, NSW 2000, Australia
Interests: artificial intelligence; machine learning; security; QoS; Internet of Things; computer networks security; cybersecurity; blockchain; computer networks; computer security
Special Issues, Collections and Topics in MDPI journals
Dr. Shaleeza Sohail

E-Mail Website
Guest Editor
School of Information and Physical Sciences, The University of Newcastle, Callaghan, NSW 2308, Australia
Interests: machine learning; computer networks; blockchain; cyber security
Dr. Nan Li

E-Mail Website
Guest Editor
School of Information and Physical Sciences, The University of Newcastle, Callaghan NSW 2308, Australia
Interests: applied cryptography; IoT security; trust management system; privacy protection; authentication protocols
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Public health is an organized effort of society to prolong life by preventing diseases and promoting health, and requires the integration of emerging technologies to face complex and unforeseen challenges in recent times. Blockchain is a disruptive technology that provides a distributed ledger, decentralization and immutability—some characteristics that have huge applicability in different sectors of our lives, especially public health. Artificial intelligence (AI) has made extraordinary advances by providing means to analyze huge quantities of data and engage in evidence-informed decision-making. Public health has seen tremendous benefits from the use of AI technologies to improve the health of individuals and populations. Information security is one such area where blockchain and AI can provide new and revolutionary solutions for public health.

The aim of this Special Issue is to gather the latest research on methodologies, techniques, and new solutions for improving usability, securing information and optimizing the performance of public health applications by using emerging technologies.  In particular, this Issue will address the challenges, opportunities and trends of blockchain and AI integration in public health. Researchers, experts, and scholars from both industry and academia are encouraged to submit their research outcomes in these areas.

Topics of interest include, but are not limited to: 

  • Blockchain applications in medical/healthcare field;
  • AI applications in medical/healthcare field;
  • Techniques and methodologies for securing blockchain-based public health applications;
  • Challenges and opportunities of blockchain for ensuring security in public health;
  • Machine and deep learning for information security applications;
  • Explainability of AI-based public health solutions;
  • Trust and privacy challenges in smart public health;
  • Performance and scalability challenges in blockchain-based public health applications;
  • Use of blockchain for securing industrial IoT medical applications;
  • Economic, performance, scalability, and security analysis of AI-based public health applications;
  • Robustness and fault tolerance of emerging-technology-based public health applications;
  • Impact of precision public health;
  • Ethical issues in integrating AI in public health.

Dr. Fariza Sabrina
Dr. Shaleeza Sohail
Dr. Nan Li
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • public health
  • emerging technologies
  • blockchain
  • AI
  • machine learning
  • security
  • privacy

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Published Papers (3 papers)

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Research

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14 pages, 483 KiB  
Article
BioChainReward: A Secure and Incentivised Blockchain Framework for Biomedical Data Sharing
by Mahmoud Elkhodr, Ergun Gide, Omar Darwish and Shorouq Al-Eidi
Int. J. Environ. Res. Public Health 2023, 20(19), 6825; https://doi.org/10.3390/ijerph20196825 - 25 Sep 2023
Cited by 3 | Viewed by 1671
Abstract
In the era of digital healthcare, biomedical data sharing is of paramount importance for the advancement of research and personalised healthcare. However, sharing such data while preserving user privacy and ensuring data security poses significant challenges. This paper introduces BioChainReward (BCR), a blockchain-based [...] Read more.
In the era of digital healthcare, biomedical data sharing is of paramount importance for the advancement of research and personalised healthcare. However, sharing such data while preserving user privacy and ensuring data security poses significant challenges. This paper introduces BioChainReward (BCR), a blockchain-based framework designed to address these concerns. BCR offers enhanced security, privacy, and incentivisation for data sharing in biomedical applications. Its architecture consists of four distinct layers: data, blockchain, smart contract, and application. The data layer handles the encryption and decryption of data, while the blockchain layer manages data hashing and retrieval. The smart contract layer includes an AI-enabled privacy-preservation sublayer that dynamically selects an appropriate privacy technique, tailored to the nature and purpose of each data request. This layer also features a feedback and incentive mechanism that incentivises patients to share their data by offering rewards. Lastly, the application layer serves as an interface for diverse applications, such as AI-enabled apps and data analysis tools, to access and utilise the shared data. Hence, BCR presents a robust, comprehensive approach to secure, privacy-aware, and incentivised data sharing in the biomedical domain. Full article
(This article belongs to the Special Issue Use of Emerging Technologies in Public Health: Blockchain and AI)
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22 pages, 809 KiB  
Article
Security Risks and User Perception towards Adopting Wearable Internet of Medical Things
by Sanjit Thapa, Abubakar Bello, Alana Maurushat and Farnaz Farid
Int. J. Environ. Res. Public Health 2023, 20(8), 5519; https://doi.org/10.3390/ijerph20085519 - 14 Apr 2023
Cited by 7 | Viewed by 3251
Abstract
The Wearable Internet of Medical Things (WIoMT) is a collective term for all wearable medical devices connected to the internet to facilitate the collection and sharing of health data such as blood pressure, heart rate, oxygen level, and more. Standard wearable devices include [...] Read more.
The Wearable Internet of Medical Things (WIoMT) is a collective term for all wearable medical devices connected to the internet to facilitate the collection and sharing of health data such as blood pressure, heart rate, oxygen level, and more. Standard wearable devices include smartwatches and fitness bands. This evolving phenomenon due to the IoT has become prevalent in managing health and poses severe security and privacy risks to personal information. For better implementation, performance, adoption, and secured wearable medical devices, observing users’ perception is crucial. This study examined users’ perspectives of trust in the WIoMT while also exploring the associated security risks. Data analysed from 189 participants indicated a significant variance (R2 = 0.553) on intention to use WIoMT devices, which was determined by the significant predictors (95% Confidence Interval; p < 0.05) perceived usefulness, perceived ease of use, and perceived security and privacy. These were found to have important consequences, with WIoMT users intending to use the devices based on the trust factors of usefulness, easy to use, and security and privacy features. Further outcomes of the study identified how users’ security matters while adopting the WIoMT and provided implications for the healthcare industry to ensure regulated devices that secure confidential data. Full article
(This article belongs to the Special Issue Use of Emerging Technologies in Public Health: Blockchain and AI)
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Review

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25 pages, 385 KiB  
Review
A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems
by Xin Gu, Fariza Sabrina, Zongwen Fan and Shaleeza Sohail
Int. J. Environ. Res. Public Health 2023, 20(15), 6539; https://doi.org/10.3390/ijerph20156539 - 7 Aug 2023
Cited by 25 | Viewed by 3873
Abstract
Federated learning (FL) provides a distributed machine learning system that enables participants to train using local data to create a shared model by eliminating the requirement of data sharing. In healthcare systems, FL allows Medical Internet of Things (MIoT) devices and electronic health [...] Read more.
Federated learning (FL) provides a distributed machine learning system that enables participants to train using local data to create a shared model by eliminating the requirement of data sharing. In healthcare systems, FL allows Medical Internet of Things (MIoT) devices and electronic health records (EHRs) to be trained locally without sending patients data to the central server. This allows healthcare decisions and diagnoses based on datasets from all participants, as well as streamlining other healthcare processes. In terms of user data privacy, this technology allows collaborative training without the need of sharing the local data with the central server. However, there are privacy challenges in FL arising from the fact that the model updates are shared between the client and the server which can be used for re-generating the client’s data, breaching privacy requirements of applications in domains like healthcare. In this paper, we have conducted a review of the literature to analyse the existing privacy and security enhancement methods proposed for FL in healthcare systems. It has been identified that the research in the domain focuses on seven techniques: Differential Privacy, Homomorphic Encryption, Blockchain, Hierarchical Approaches, Peer to Peer Sharing, Intelligence on the Edge Device, and Mixed, Hybrid and Miscellaneous Approaches. The strengths, limitations, and trade-offs of each technique were discussed, and the possible future for these seven privacy enhancement techniques for healthcare FL systems was identified. Full article
(This article belongs to the Special Issue Use of Emerging Technologies in Public Health: Blockchain and AI)
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