Data Privacy Issues Research Articles

Critical success factors for EHR systems implementation in developing countries: a systematic review

Purpose This study aims to identify the barriers that hinder the effective implementation of electronic health records (EHR) systems in developing countries while also uncovering the critical success factors (CSFs) that can facilitate their implementation. This study focused on the sociotechnical and environmental challenges that influenced the implementation of EHR systems. Design/methodology/approach This study used a systematic literature review methodology guided by the modified sociotechnical theory. It followed PRISMA guidelines to identify barriers and CSFs. This review included a comprehensive search of academic databases such as Taylor & Francis, Google Scholar, Emerald and PubMed, covering studies published between 2012 and mid-2024. The studies were screened and analysed using thematic analysis. Findings The review identified several barriers to EHR implementation, including a lack of information and communication technology infrastructure, inadequate training, and limited government support. It also highlighted CSFs such as enhancing training programmes, improving technical infrastructure, fostering government support and addressing privacy and security concerns. This study concluded that a holistic approach addressing technical, social and environmental factors was critical for the success of EHR systems in low-resource settings. Practical implications This study offered actionable recommendations for policymakers and health-care leaders in developing countries. These include strategies for enhancing user training, improving technical infrastructure, ensuring government support and addressing data privacy and security issues, which are crucial for successfully adopting EHR systems. Originality/value This research comprehensively examined the factors influencing EHR implementation in developing countries, offering a unique perspective by applying the modified sociotechnical theory. It bridged a significant gap in the literature by focusing on the specific challenges faced in resource-constrained environments.

Tabular Federated Learning to detect cyber faults in smart buildings

By incorporating artificial intelligence into applications, everyone in the current circumstance has become intelligent, thereby reducing the burden of incessant interruption or human control. In today’s environment, most of the hardware infrastructure in buildings is connected to the internet, transforming the building infrastructure into a smart building infrastructure. In a similar vein, it should be noted that intelligent building infrastructure is susceptible to cyber-induced defects, and the issue of data privacy is a significant worry within the realm of collaborative learning. The primary objective of this study is to devise and implement an Attentive Interpretable Tabular-based Federated Learning methodology for safeguarding data privacy while detecting cyber-induced problems in the infrastructure of intelligent buildings. In this paper, Federated Learning ecosystem–based deep learning models are used to find and describe cyber-induced faults and vulnerabilities. The suggested system will be evaluated using various measures, including accuracy, precision, recall, and losses. In addition, the built ecosystem is examined using various data distributions to determine whether the outcomes are stable.

Time-constrained persistent deletion for key–value store engine on ZNS SSD

The inherent out-of-place update characteristic of the Log-Structured Merge tree (LSM tree) cannot guarantee persistent deletion within a specific time window, leading to potential data privacy and security issues. Existing solutions like Lethe-Fade ensure time-constrained persistent deletion but introduce considerable write overhead, worsening the write amplification issue, particularly for key–value stores on ZNS SSD. To address this problem, we propose a zone-aware persistent deletion scheme for key–value store engines. Targeting mitigating the write amplification induced by level compaction, we design an adaptive SSTable selection strategy for each level in the LSM tree. Additionally, as the SSTable with deletion records would become invalid after the persistent deletion timer reaches its threshold, we design a tombstone-aware zone allocation strategy to reduce the data migration induced by garbage collection. In further, we optimize the victim zone selection in GC to reduce the invalid migration of tombstone files. Experimental results demonstrate that our scheme effectively ensures that most outdated physical versions are deleted before reaching the persistent deletion time threshold. When deleting 10% of keys in the key–value store engine, this scheme reduces write amplification by 74.7% and the garbage collection-induced write by 87.3% compared to the Lethe-Fade scheme.

AI in Social Media Marketing : Opportunities and Challenges

Using artificial intelligence (AI) technology to improve brand engagement, targeting accuracy, and content personalisation, social media marketing (SMM) has undergone tremendous evolution. In order to highlight the transformational power of AI through computer vision, natural language processing (NLP), and predictive analytics, this article investigates how AI is integrated into SMM. Artificial intelligence (AI) has revolutionised the way businesses engage with their consumers by enabling more granular customer data, tailored marketing strategies, and streamlined operations. Even with its benefits, AI in SMM has drawbacks such algorithmic bias, data privacy, and ethical issues. This paper offers a thorough review of AI's involvement in SMM, highlighting both opportunities and challenges, by looking at successful implementations and lessons learnt from mistakes.

The future of wearable health technology: From monitoring to preventive healthcare

Wearable health technology has undergone rapid development, evolving from simple fitness trackers to sophisticated medical devices that provide real-time monitoring and data collection. As these technologies become increasingly integrated into healthcare systems, their potential to shift from reactive treatment to proactive preventive care is gaining attention. This review paper explores the current state of wearable health devices, including the key technologies that enable monitoring and data analysis, and discusses the latest advancements such as AI integration, smart fabrics, and implantable wearables. The paper also delves into the significant role these technologies play in preventive healthcare by enabling continuous monitoring, early disease detection, and personalized health interventions. Furthermore, it addresses the challenges facing wearables, including issues of data accuracy, privacy, and user compliance. Finally, the paper explores future directions and ethical considerations, emphasizing the potential for wearables to reshape healthcare by promoting more accessible, equitable, and preventive healthcare solutions.

Integrating Artificial Intelligence into ESG Practices: Opportunities, Challenges, and Strategic Solutions for Corporate Sustainability

Environmental, social, and governance (ESG) practices have become increasingly important in corporate strategy in recent years, while the rapid development of artificial intelligence (AI) has created new opportunities and challenges for corporate sustainability. AI technology driving companies for ESG time is getting more and more attention. This study examines the application of AI technologies in environmental management, social responsibility, and corporate governance, demonstrating their potential to optimize resource utilization, reduce carbon emissions, improve recruitment fairness, and prevent fraud. However, integrating AI with ESG faces many challenges, including technological complexity, high costs, data privacy and ethical issues, and organizational and cultural resistance. To address these challenges, this study proposes solutions to reduce financial burdens, secure data, and enhance cultural buy-in through strategies such as technology partnerships, open-source tools, and employee training. By delving into the convergence of AI and ESG, this study provides companies with a guiding direction to fully utilize the potential of AI while maintaining long-term sustainability.

The Advantages of Health Recommending System with Wearable Devices

The health recommendation system is one of the critical application scenarios of machine learning technology. With the advantages of wearable and mobile devices to collect rich user health monitoring data, machine learning algorithms can accurately advise users’ health status and corresponding living habits. However, some technical and regulatory challenges still need to be solved at this stage. Specifically, to ensure real-time recommendation results, the health recommendation system needs to adopt the method of training computational learning algorithms on wearable devices. However, due to wearable devices’ limited computing resources and storage space, it is often difficult to obtain satisfactory results by changing the strategy. In addition, the issue of data privacy deserves attention because wearable devices collect a lot of user privacy information, but the data manager is often not the user himself. Finally, Bluetooth protocol usually connects wearable devices, making them vulnerable to intentional attacks. This paper investigates the above problems and expounds on the solutions to related problems.

Conceptualizing the Implications of Artificial Intelligence (AI) Tools and Personalization Marketing on Consumer Purchase Intention: Insights from the Malaysian E-Commerce Market

Artificial intelligence (AI) has emerged as a powerful tool, enabling online retailers to offer highly personalized shopping experiences tailored to individual preferences and behaviors. As e-commerce continues to grow in Malaysia, understanding the influence of AI and personalization marketing on consumer purchase intentions has become increasingly important for businesses seeking to remain competitive. However, the rapid adoption of AI also raises concerns about data privacy, ethical AI usage, and compliance with emerging data protection regulations, such as Malaysia’s Personal Data Protection Act (PDPA). This study aims to explore the potential impact of artificial intelligence (AI) and personalization marketing on consumer purchase intention within the Malaysian e-commerce market. By reviewing previous literature and theoretical frameworks, the study explores how the use of AI tools including predictive analytics automation, and personalization experiences might influence consumer behavior in online shopping environments. The study adopts a quantitative approach, in which quota sampling will be used for the participant selection. A self-administered questionnaire with a five-point Likert scale will be employed to gather data from e-commerce users in Malaysia. The findings from this study have important implications for both e-commerce businesses and policymakers in Malaysia. For businesses, understanding which aspects of AI and personalization most influence consumer purchase intentions can help them strategically implement these technologies to enhance customer engagement and drive sales. For policymakers, the study highlights the need to consider ethical and legal issues, such as data privacy and policy issues, in the growing use of AI in the e-commerce market.

Role of artificial intelligence in predicting neurological outcomes in post cardiac resuscitation

Being an extremely high mortality rate condition, cardiac arrest cases have rightfully been evaluated via various studies and scoring factors for effective resuscitative practices and neurological outcomes post resuscitation. This narrative review aims to explore the role of Artificial Intelligence (AI) in predicting neurological outcomes post cardiac resuscitation. Methodology involved detailed review of all relevant recent studies of AI, different machine learning algorithms, prediction tools and assessing their benefit in predicting neurological outcomes in post cardiac resuscitation cases as compared to more traditional prognostic scoring systems and tools. Previously, outcome determining clinical, blood and radiological factors were prone to other influencing factors like limited accuracy and time constraints. Studies conducted also emphasized that to predict poor neurological outcomes, a more multimodal approach helped adjust for confounding factors, interpret diverse datasets and provide a reliable prognosis which only demonstrates need for AI to help overcome challenges faced. Advanced machine learning algorithms like Artificial Neural Networks (ANN) using supervised learning by AI have improved accuracy of prognostic models outperforming conventional models. Several real-world cases of effective AI powered algorithm models have been cited here. Studies comparing machine learning tools like XGBoost, AI Watson, hyperspectral imaging, ChatGPT-4 and AI based gradient boosting have noted their beneficial uses. AI could help reduce workload, healthcare costs and help personalize care, process vast genetic and lifestyle data and help reduce side effects from treatments. Limitations of AI have been covered extensively in this article including data quality, bias, privacy issues and transparency. Our objectives should be to use more diverse data sources, use interpretable data output giving process explanation, validation method and implement policies to safeguard patient data. Despite the limitations, the advancements already made by AI and its potential in predicting neurological outcomes in post cardiac resuscitation cases has been quite promising and boosts a continually improving system, albeit requiring close human supervision with training and improving models, with plans to educate clinicians, the public and sharing collected data.

Research on Blockchain Interactive Zero Knowledge Proof Privacy Protection Scheme Based on Improved Paillier Homomorphic Encryption

In the context of the digital age, data privacy and security issues are increasingly prominent. Blockchain technology plays an important role in data sharing due to its transparency and immutability, but it also brings the risk of privacy leakage. Zero knowledge proof technology provides a solution for verifying data correctness without exposing data content, which is particularly important for blockchain as it can ensure the validity and compliance of transactions while protecting user privacy. Although zero knowledge proof is quite mature in theory, its application in blockchain systems still faces challenges such as computational efficiency, complexity of smart contracts, and system compatibility. This study aims to propose a privacy protection scheme that supports interactive zero knowledge proof by improving the homomorphic encryption Paillier algorithm, in order to enhance the privacy protection capability of blockchain systems and maintain system efficiency and security. The study will adopt an interdisciplinary approach, combining cryptography, computer science, and network security theory, to deeply analyze the application effect of zero knowledge proof technology in blockchain, explore its optimization space and applicability.

Posthumanist Technologies in Business: AI and Cloud Computing for Global Optimization and Ethical Challenges

. This study explores the integration of artificial intelligence (AI) and cloud computing within posthumanist technologies, focusing on their impact on business optimization and information security. A systematic review of 40 studies across sectors such as banking and retail highlights the benefits of AI in automating tasks and enhancing decision-making, while cloud computing provides flexibility and scalability. However, risks like data privacy issues, algorithmic bias, and cybersecurity vulnerabilities demand attention. The research emphasizes the need for ethical frameworks and security strategies to mitigate these risks. Additionally, it stresses the importance of equitable access to these technologies for small and medium-sized enterprises (SMEs) and marginalized communities. The study provides actionable recommendations for businesses and calls for future research on the long-term societal implications of these technologies

Prescription digital therapeutics in obesity management: Present and future

Prescription digital therapeutics (PDTs) are emerging as innovative solutions in the management of obesity, complementing traditional methods such as lifestyle interventions, pharmacotherapy, and surgery. This mini-review explores the current landscape and future potential of PDTs in obesity management. We begin by defining PDTs and examining key players and products in the market. These products exemplify the integration of artificial intelligence -driven personalized coaching and real-time data tracking to enhance user engagement and treatment efficacy. Clinical evidence supporting the effectiveness of PDTs in promoting weight loss and improving metabolic health is discussed, with an emphasis on the comparative studies with traditional interventions. The review also addresses challenges such as regulatory hurdles, user adherence, data privacy, and accessibility issues. Looking forward, advancements in technology, personalized medicine, and better integration with healthcare systems are poised to further enhance the impact of PDTs. This article underscores the potential of PDTs to revolutionize obesity management and calls for continued innovation and research in this field.

Mechanism Study on Innovative Influences of AI and Blockchain on Supply Chain Logistics: Case Study of JD and Alibaba

This study explores the innovative impact mechanisms of artificial intelligence (AI) and blockchain in supply chain and logistics through the case study of JD and Alibaba. The successful cases of JD and Alibaba in supply chain management show that by integrating AI and blockchain technologies, companies can automate, smarten and make supply chain logistics more efficient, thereby reducing costs, improving efficiency and enhancing overall competitiveness. Applying AI in supply chain logistics, such as demand forecasting, inventory optimization and transport route planning, can provide accurate data analysis and decision support. It can help reduce inventory costs, shorten transportation time and optimize resource allocation in the supply chain. However, AI and blockchain technologies still face several challenges, such as data privacy and security issues, technology costs and standardization. Future research and practice will continue to work on solving these challenges, further optimizing and improving the application of AI and blockchain in supply chain logistics, and promoting innovation and development in supply chain logistics.

Privacy-Preserving Federated Learning-Based Intrusion Detection Technique for Cyber-Physical Systems

The Internet of Things (IoT) has revolutionized various industries, but the increased dependence on all kinds of IoT devices and the sensitive nature of the data accumulated by them pose a formidable threat to privacy and security. While traditional IDSs have been effective in securing critical infrastructures, the centralized nature of these systems raises serious data privacy concerns as sensitive information is sent to a central server for analysis. This research paper introduces a Federated Learning (FL) approach designed for detecting intrusions in diverse IoT networks to address the issue of data privacy by ensuring that sensitive information is kept in the individual IoT devices during model training. Our framework utilizes the Federated Averaging (FedAvg) algorithm, which aggregates model weights from distributed devices to refine the global model iteratively. The proposed model manages to achieve above 90% accuracies across various metrics, including precision, recall, and F1 score, while maintaining low computational demands. The results show that the proposed system successfully identifies various types of cyberattacks, including Denial-of-Service (DoS), Distributed Denial-of-Service (DDoS), data injection, ransomware, and several others, showcasing its robustness. This research makes a great advancement to the IDSs by providing an efficient and reliable solution that is more scalable and privacy friendly than any of the existing models.

ИНКЛЮЗИВТІ БІЛІМ БЕРУДЕГІ ЖАСАНДЫ ИНТЕЛЛЕКТІНІҢ БОЛАШАҒЫ

This article examines the role and future of artificial intelligence (AI) in inclusive education. Inclusive education provides equal educational opportunities for all students, including children with special needs. Technology allows teachers to create curricula adapted to the individual needs of each student, optimize the learning process and improve feedback and evaluation systems. The article discusses the potential of AI in inclusive education, its advantages in improving student academic achievement and personalizing the learning process. In addition, the problems and ethical dilemmas that arise when using AI, including issues of data privacy and social inequality, are considered. The results of the study show that improving inclusive education through the effective use of AI technologies is promising and opens up new opportunities in the education system.

Artificial intelligence and informatics in neonatal resuscitation

Neonatal intensive care unit resuscitative care continually evolves and increasingly relies on data. Data driven precision resuscitation care can be enabled by leveraging informatics tools and artificial intelligence. Despite technological advancements, these data are often underutilized due to suboptimal data capture, aggregation, and low adoption of artificial intelligence and analytic tools. This review describes the fundamentals and explores the evidence behind informatics and artificial intelligence tools supporting neonatal intensive care unit resuscitative care, training and education. Key findings include the need for effective interface design for accurate data capture followed by storage and translation to wisdom using analytics and artificial intelligence tools. This review addresses the issues of data privacy, bias, liability and ethical frameworks when adopting these tools. While these emerging technologies hold great promise to improve resuscitation, further study of these applications in neonatal population and awareness of informatics and artificial intelligence principles among clinicians is imperative.

Cutting-Edge Amalgamation of Web 3.0 and Hybrid Chaotic Blockchain Authentication for Healthcare 4.0

Healthcare 4.0 is considered the most promising technology for gathering data from humans and strongly couples with a communication system for precise clinical and diagnosis performance. Though sensor-driven devices have largely made our everyday lives easier, these technologies have been suffering from various security challenges. Because of data breaches and privacy issues, this heightens the demand for a comprehensive healthcare solution. Since most healthcare data are sensitive and valuable and transferred mostly via the Internet, the safety and confidentiality of patient data remain an important concern. To face the security challenges in Healthcare 4.0, Web 3.0 and blockchain technology have been increasingly deployed to resolve the security breaches due to their immutability and decentralized properties. In this research article, a Web 3.0 ensemble hybrid chaotic blockchain framework is proposed for effective and secure authentication in the Healthcare 4.0 industry. The proposed framework uses the Infura Web API, Web 3.0, hybrid chaotic keys, Ganache interfaces, and MongoDB. To allow for more secure authentication, an ensemble of scroll and Henon maps is deployed to formulate the high dynamic hashes during the formation of genesis blocks, and all of the data are backed in the proposed model. The complete framework was tested in Ethereum blockchain using Web 3.0, in which Python 3.19 is used as the major programming tool for developing the different interfaces. Formal analysis is carried out with Burrows–Abadi–Needham Logic (BAN) to assess the cybersecurity reliability of the suggested framework, and NIST standard tests are used for a thorough review. Furthermore, the robustness of the proposed blockchain is also measured and compared with the other secured blockchain frameworks. Experimental results demonstrate that the proposed model exhibited more defensive characteristics against multiple attacks and outperformed the other models in terms of complexity and robustness. Finally, the paper gives a panoramic view of integrating Web 3.0 with the blockchain and the inevitable directions of a secured authentication framework for Healthcare 4.0.

A critical review of machine learning applications in supply chain risk management

This article examines the transformative role of Machine Learning (ML) in Supply Chain Risk Management (SCRM), emphasizing its ability to enhance risk prediction and mitigation through real-time, data-driven insights. It demonstrates how ML applications like demand forecasting, inventory optimization, supplier risk assessment, and fraud detection improve supply chain resilience by analyzing large datasets to identify patterns, predict risks, and recommend proactive actions. However, the article also highlights key challenges, including data quality, availability, and privacy issues, which limit the effectiveness of ML models. Integrating ML with legacy systems poses additional technical and financial difficulties, particularly for smaller businesses. High implementation costs and scalability constraints further hinder widespread adoption. Ethical concerns, such as data bias and privacy, still need to be explored, raising questions about responsible ML use in supply chains. Future research should address these gaps by improving data governance frameworks for accuracy and privacy, developing scalable ML models suited to various supply chain environments, and exploring synergies with technologies like blockchain and the Internet of Things (IoT). These advancements will help realize ML’s full potential, fostering more agile, transparent, and resilient supply chains capable of navigating complex global risks. By tackling these challenges, ML can shift SCRM from reactive to proactive, ensuring long-term operational continuity and a competitive edge in the evolving global market.

Keeping medical information safe and confidential: a qualitative study on perceptions of Israeli physicians

BackgroundPatients expect their information to remain confidential, and physicians have a legal and ethical obligation to keep it this way. Confidentiality is not just a legal requirement but a crucial element in establishing trust between patients and healthcare providers. Patients must feel confident that their personal and medical information is kept confidential and shared only with those who need to know. Previous studies have primarily concentrated on patients’ perceptions of medical confidentiality, data privacy, and data protection issues. However, research on the practical practices and perceptions of medical confidentiality among hospital physicians is scant, underscoring the need for a deeper understanding of this critical issue.MethodsThrough qualitative methods and as part of a large-scale study on aspects of patient dignity and responsiveness in Israel, physicians shared their views and practices on managing medical information.ResultsThe study revealed the practical challenges physicians face in upholding various aspects of data protection within hospital settings. These challenges, strategies, and deviations from data protection principles that physicians discussed are of significant practical relevance. The importance of patient consent and the practical measures for safeguarding patient information were also highlighted. While physicians acknowledged the importance of protecting patient information, they also grappled with the realities of doing so in a complex healthcare environment. In future healthcare policies, it is critical to ensure robust measures are in place to safeguard and uphold medical confidentiality. These can include specific measures to increase compliance, such as regularly monitoring compliance with confidentiality policies, producing safe and anonymous channels to voice concerns, and enforcing consequences for any breaches to ensure accountability.ConclusionsWhile protecting medical information has emerged as an important goal, it is equally crucial to strike a balance between the need to share information to advance and provide quality medical care. Physicians and policymakers must navigate this delicate balance. Additionally, organizations should strengthen compliance to enhance their monitoring and enforcement of confidentiality policies. Ineffective implementation of medical confidentiality leads to theoretical guidelines that do not translate effectively into practice.

Harnessing AI for Early Detection of Cardiovascular Diseases: Insights from Predictive Models Using Patient Data

Cardiovascular diseases (CVDs) continue to be the leading cause of death in the world, taking millions of lives every year. Early detection and treatment of these diseases are critical in preventing deaths and illnesses. Currently, traditional diagnostic methods like routine clinical examinations and other standard risk assessment tools fail to detect CVDs in early on set stages, posing numerous limitations for preventable interventions. This work explores incorporating artificial intelligence (AI) to catalyze early CVD diagnosis using AI models for patient data including electrocardiogram (ECG), wearable-generated metrics, and medical history. The research showcased the ability of AI to predict with high accuracy when CVD would start using machine learning methods such as Random Forest, Support Vector Machines (SVM) and Neural Networks. The powerful AI models were carefully constructed by training and testing them on an extensive dataset that contained measurements of diverse patient characteristics including many other readouts related to cardiovascular risk. Compared to traditional methods, the models outperformed, and Neural Networks had an accuracy of 92% in identifying high-risk patients. A hallmark of this work is that AI identifies subtle patterns and relationships within patient data ones that conventional approaches might not find. These AI-based predictive models might soon become part of the routine clinical evaluation for cardiovascular disease, providing a personalized and early health intervention to empower your cardiovascular care. Timely identification of these high-risk patients could allow for targeted interventions, leading to improved health and reductions in healthcare spending. Despite the promising potential that AI holds in healthcare, it also has brought up issues of data privacy, ethical use and the need for extensive clinical validation. This paper suggests a predictive strategy could be implemented using AI algorithms to lead early detection and management of CVD, which opens an opportunity for individualized care accompanied by big data era. Future studies should focus on improving such models with more diverse and larger datasets as well as overcoming logistical difficulties encountered in integrating AI into clinical workflows.