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Sensors (Basel, Switzerland) Jul 2023Data provenance means recording data origins and the history of data generation and processing. In healthcare, data provenance is one of the essential processes that... (Review)
Review
Data provenance means recording data origins and the history of data generation and processing. In healthcare, data provenance is one of the essential processes that make it possible to track the sources and reasons behind any problem with a user's data. With the emergence of the General Data Protection Regulation (GDPR), data provenance in healthcare systems should be implemented to give users more control over data. This SLR studies the impacts of data provenance in healthcare and GDPR-compliance-based data provenance through a systematic review of peer-reviewed articles. The SLR discusses the technologies used to achieve data provenance and various methodologies to achieve data provenance. We then explore different technologies that are applied in the healthcare domain and how they achieve data provenance. In the end, we have identified key research gaps followed by future research directions.
Topics: Biomedical Research; Delivery of Health Care
PubMed: 37514788
DOI: 10.3390/s23146495 -
NPJ Precision Oncology Aug 2023This study evaluates the quality of published research using artificial intelligence (AI) for ovarian cancer diagnosis or prognosis using histopathology data. A... (Review)
Review
This study evaluates the quality of published research using artificial intelligence (AI) for ovarian cancer diagnosis or prognosis using histopathology data. A systematic search of PubMed, Scopus, Web of Science, Cochrane CENTRAL, and WHO-ICTRP was conducted up to May 19, 2023. Inclusion criteria required that AI was used for prognostic or diagnostic inferences in human ovarian cancer histopathology images. Risk of bias was assessed using PROBAST. Information about each model was tabulated and summary statistics were reported. The study was registered on PROSPERO (CRD42022334730) and PRISMA 2020 reporting guidelines were followed. Searches identified 1573 records, of which 45 were eligible for inclusion. These studies contained 80 models of interest, including 37 diagnostic models, 22 prognostic models, and 21 other diagnostically relevant models. Common tasks included treatment response prediction (11/80), malignancy status classification (10/80), stain quantification (9/80), and histological subtyping (7/80). Models were developed using 1-1375 histopathology slides from 1-776 ovarian cancer patients. A high or unclear risk of bias was found in all studies, most frequently due to limited analysis and incomplete reporting regarding participant recruitment. Limited research has been conducted on the application of AI to histopathology images for diagnostic or prognostic purposes in ovarian cancer, and none of the models have been demonstrated to be ready for real-world implementation. Key aspects to accelerate clinical translation include transparent and comprehensive reporting of data provenance and modelling approaches, and improved quantitative evaluation using cross-validation and external validations. This work was funded by the Engineering and Physical Sciences Research Council.
PubMed: 37653025
DOI: 10.1038/s41698-023-00432-6 -
Foods (Basel, Switzerland) Aug 2023With the rise of globalization and technological competition, the food supply chain has grown more complex due to the multiple players and factors involved in the chain.... (Review)
Review
With the rise of globalization and technological competition, the food supply chain has grown more complex due to the multiple players and factors involved in the chain. Traditional systems fail to offer effective and reliable traceability solutions considering the increasing requirement for accountability and transparency in the food supply chain. Blockchain technology has been claimed to offer the food industry a transformative future. The inherent features of blockchain, including immutability and transparency, create a dependable and secure system for tracking food products across the whole supply chain, ensuring total control over their traceability from the origin to the final consumer. This research offers a comprehensive overview of multiple models to understand how the integration of blockchain and other digital technologies has transformed the food supply chain. This comprehensive systematic review of blockchain-based food-supply-chain frameworks aimed to uncover the capability of blockchain technology to revolutionize the industry and examined the current landscape of blockchain-based food traceability solutions to identify areas for improvement. Furthermore, the research investigates recent advancements and investigates how blockchain aligns with other emerging technologies of Industry 4.0 and Web 3.0. Blockchain technology plays an important role in improving food traceability and supply-chain operations. Potential synergies between blockchain and other emerging technologies of Industry 4.0 and Web 3.0 are digitizing food supply chains, which results in better management, automation, efficiencies, sustainability, verifiability, auditability, accountability, traceability, transparency, tracking, monitoring, response times and provenance across food supply chains.
PubMed: 37628025
DOI: 10.3390/foods12163026 -
Frontiers in Digital Health 2024In the big data era, where corporations commodify health data, non-fungible tokens (NFTs) present a transformative avenue for patient empowerment and control. NFTs are...
INTRODUCTION
In the big data era, where corporations commodify health data, non-fungible tokens (NFTs) present a transformative avenue for patient empowerment and control. NFTs are unique digital assets on the blockchain, representing ownership of digital objects, including health data. By minting their data as NFTs, patients can track access, monetize its use, and build secure, private health information systems. However, research on NFTs in healthcare is in its infancy, warranting a comprehensive review.
METHODS
This study conducted a systematic literature review and thematic analysis of NFTs in healthcare to identify use cases, design models, and key challenges. Five multidisciplinary research databases (Scopus, Web of Science, Google Scholar, IEEE Explore, Elsevier Science Direct) were searched. The approach involved four stages: paper collection, inclusion/exclusion criteria application, screening, full-text reading, and quality assessment. A classification and coding framework was employed. Thematic analysis followed six steps: data familiarization, initial code generation, theme searching, theme review, theme definition/naming, and report production.
RESULTS
Analysis of 19 selected papers revealed three primary use cases: patient-centric data management, supply chain management for data provenance, and digital twin development. Notably, most solutions were prototypes or frameworks without real-world implementations. Four overarching themes emerged: data governance (ownership, tracking, privacy), data monetization (commercialization, incentivization, sharing), data protection, and data storage. The focus lies on user-controlled, private, and secure health data solutions. Additionally, data commodification is explored, with mechanisms proposed to incentivize data maintenance and sharing. NFTs are also suggested for tracking medical products in supply chains, ensuring data integrity and provenance. Ethereum and similar platforms dominate NFT minting, while compact NFT storage options are being explored for faster data access.
CONCLUSION
NFTs offer significant potential for secure, traceable, decentralized healthcare data exchange systems. However, challenges exist, including dependence on blockchain, interoperability issues, and associated costs. The review identified research gaps, such as developing dual ownership models and data pricing strategies. Building an open standard for interoperability and adoption is crucial. The scalability, security, and privacy of NFT-backed healthcare applications require further investigation. Thus, this study proposes a research agenda for adopting NFTs in healthcare, focusing on governance, storage models, and perceptions.
PubMed: 38919876
DOI: 10.3389/fdgth.2024.1377531 -
Frontiers in Public Health 2023Blockchain technology includes numerous elements such as distributed ledgers, decentralization, authenticity, privacy, and immutability. It has progressed past the hype...
Blockchain technology includes numerous elements such as distributed ledgers, decentralization, authenticity, privacy, and immutability. It has progressed past the hype to find actual use cases in industries like healthcare. Blockchain is an emerging area that relies on a consensus algorithm and the idea of a digitally distributed ledger to eliminate any intermediary risks. By enabling them to trace data provenance and any changes made, blockchain technology can enable different healthcare stakeholders to share access to their networks without violating data security and integrity. The healthcare industry faces challenges like fragmented data, security and privacy concerns, and interoperability issues. Blockchain technology offers potential solutions by ensuring secure, tamper-proof storage across multiple network nodes, improving interoperability and patient privacy. Encrypting patient data further enhances security and reduces unauthorized access concerns. Blockchain technology, deployed over the Internet, can potentially use the current healthcare data by using a patient-centric approach and removing the intermediaries. This paper discusses the effective utilization of blockchain technology in the healthcare industry. In contrast to other applications, the exoteric evaluation in this paper shows that the innovative technology called blockchain technology has a major role to play in the existing and future applications of the healthcare industry and has significant benefits.
Topics: Humans; Blockchain; Electronic Health Records; Computer Security; Delivery of Health Care; Confidentiality
PubMed: 37790716
DOI: 10.3389/fpubh.2023.1229386 -
Comprehensive Reviews in Food Science... May 2024Food authentication and contamination are significant concerns, especially for consumers with unique nutritional, cultural, lifestyle, and religious needs. Food...
Food authentication and contamination are significant concerns, especially for consumers with unique nutritional, cultural, lifestyle, and religious needs. Food authenticity involves identifying food contamination for many purposes, such as adherence to religious beliefs, safeguarding health, and consuming sanitary and organic food products. This review article examines the issues related to food authentication and food fraud in recent periods. Furthermore, the development and innovations in analytical techniques employed to authenticate various food products are comprehensively focused. Food products derived from animals are susceptible to deceptive practices, which can undermine customer confidence and pose potential health hazards due to the transmission of diseases from animals to humans. Therefore, it is necessary to employ suitable and robust analytical techniques for complex and high-risk animal-derived goods, in which molecular biomarker-based (genomics, proteomics, and metabolomics) techniques are covered. Various analytical methods have been employed to ascertain the geographical provenance of food items that exhibit rapid response times, low cost, nondestructiveness, and condensability.
Topics: Animals; Humans; Food Analysis; Food Contamination; Metabolomics; Proteomics
PubMed: 38741454
DOI: 10.1111/1541-4337.13360