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Information and communication technologies (ICTs) are known for supporting healthcare services in dealing with adverse situations. However, little is known on the contribution of ICTs in a prolonged crisis involving a new disease, such as the COVID-19 pandemic. In this study, we carry out an exploratory investigation of which ICTs contribute the most to the emergency care of patients diagnosed with COVID-19 according to healthcare technology experts and how physicians perceive these contributions. Initially, we applied an online survey to 109 healthcare technology experts. Then, we conducted 16 in-depth follow-up interviews with emergency medicine professionals from 10 countries to identify the ICTs contributing the most to treat COVID-19 patients. Results from the survey indicated four ICTs as the most useful to support the treatment of COVID-19 patients; they are remote consultations, digital platforms for data sharing, digital non-invasive care, and interconnected medical decision support. The interviews provided insight into the applicability of those ICTs for the studied context. The four main ICTs were also found to be logically compatible with the complexity of the pandemic, reducing undesirable complexity attributes (e.g. physical proximity between caregivers and infected patients) and amplifying desirable ones (e.g. interactions that support collaborative work and knowledge sharing). [ABSTRACT FROM AUTHOR]

Internet of Things (IoT)-based health monitoring is a key focus in Healthcare 4.0 for continuously tracking Vital signs and other health biometrics. While IoT technologies like sensors, connectivity, hardware, software, and batteries have been extensively studied, there has been a lack of emphasis on evaluating human-centered health capabilities using long-term data collected by the Internet of Medical Things (IoMT). This paper tackles the problem of human-centered Vital signs-based health long-term assessment by proposing a hybrid approach that includes model-free multi-profile monitoring, optimization models, control charts, and profile capability indices. In this new approach, a curves dissimilarity index-based profile monitoring method is employed to address the complexity and outlier elimination issues in the traditional regression model-based profile monitoring approach, and a mixed-integer linear mathematical model is developed to assign human-centered tolerance to the dissimilarity index of each Vital sign profile. Also, control charts are employed to monitor the health of individuals according to each Vital sign, and overall. In addition, the profile capability index and non-confirming probability values are interpreted as long-term health measures. The efficacy of the newly proposed method is demonstrated in a simulation study based on an individual's six Vital signs generated data set using MATLAB, CPLEX, and MINITAB. [ABSTRACT FROM AUTHOR]

Healthcare 4.0 (H4.0) is revolutionizing the way hospitals are managed, bringing innovations to the health service. Therefore, this work proposes the use of drivers aimed at hospital management improvement through H4.0 grounded in a Content Analysis. It resulted in 36 booster elements for hospital management grouped into five drivers: Process management, Data management, Resource management, Patient management, and Assistance and Development of Healthcare Professionals. These drivers contribute to strengthen the link between the main needs of hospital settings and technologies capable of meeting them in an applied manner. The most significant scientific contribution of this work lies in deepening and extending the recent block of knowledge on integrating hospital management with H4.0 technologies. Future studies should investigate the application of H4.0 technologies to assist in managing environmental, social and economic sustainability aspects of hospital settings. [ABSTRACT FROM AUTHOR]

This systematic review investigates the transformative impact of Healthcare 4.0 (HC4.0) technologies on healthcare service quality (HCSQ), focusing on their potential to enhance healthcare delivery while addressing critical challenges. This study reviewed 168 peer-reviewed articles from the Scopus database, published between 2005 and 2023. The selection process used clearly defined inclusion and exclusion criteria to identify studies focusing on advanced technologies such as artificial intelligence (AI), the Internet of Things (IoT), and big data analytics. Rayyan software facilitated systematic organization and duplicate removal, while manual evaluation ensured relevance and quality. The findings highlight HC4.0's potential to improve service delivery, patient outcomes, and operational efficiencies but also reveal challenges, including interoperability, ethical concerns, and access disparities for underserved populations. The results were synthesized descriptively, uncovering key patterns and thematic insights while acknowledging heterogeneity across studies. Limitations include the absence of a formal risk-of-bias assessment and the diversity of methodologies, which precluded quantitative synthesis. This review emphasizes the need for future research on integration frameworks, ethical guidelines, and equitable access policies to realize HC4.0's transformative potential. No external funding was received, and no formal protocol was registered. [ABSTRACT FROM AUTHOR]

Abstract Continuous monitoring of individuals’ health, particularly those with chronic diseases, out of healthcare centers could result in lower patient traffic in healthcare centers, much more real-time health control, and faster emergency services. Hence, using the internet of things (IoTs) as an enabler of Industry 4.0 facilitating remote health monitoring has gained more attention in recent years. Although plenty of research has been focused on IoT-based health monitoring, they neglected emerging concepts like human-centered health data analytics as a significant requirement in the Healthcare 5.0 era. This paper contributes to the status of human-centered IoT-based health monitoring by conducting a descriptive analysis of the corresponding literature according to biometrics monitored, applied software, hardware, sensors, and communication models, highlighting the lack of consideration of long-term, human-centered health monitoring in the existing IoT-based health monitoring literature. Results showed that the focus of the literature has mostly been on information transit technology development and not human-centered data analytics. In addition, a gap analysis of the current literature recommendations emphasized multi-biometrics monitoring and cybersecurity, not human-centered health data analysis. Finally, several guidelines are provided for human-centered IoT-based health monitoring in future research.

Continuous monitoring of individuals' health, particularly those with chronic diseases, out of healthcare centers could result in lower patient traffic in healthcare centers, much more real-time health control, and faster emergency services. Hence, using the internet of things (IoTs) as an enabler of Industry 4.0 facilitating remote health monitoring has gained more attention in recent years. Although plenty of research has been focused on IoT-based health monitoring, they neglected emerging concepts like human-centered health data analytics as a significant requirement in the Healthcare 5.0 era. This paper contributes to the status of human-centered IoT-based health monitoring by conducting a descriptive analysis of the corresponding literature according to biometrics monitored, applied software, hardware, sensors, and communication models, highlighting the lack of consideration of long-term, human-centered health monitoring in the existing IoT-based health monitoring literature. Results showed that the focus of the literature has mostly been on information transit technology development and not human-centered data analytics. In addition, a gap analysis of the current literature recommendations emphasized multi-biometrics monitoring and cybersecurity, not human-centered health data analysis. Finally, several guidelines are provided for human-centered IoT-based health monitoring in future research. Article Highlights: Descriptive analysis of literature on IoT-based health monitoring. Highlighting the lack of attention to the human-centered IoT-based health monitoring. Proposing guidelines for human-centered IoT-based health monitoring. [ABSTRACT FROM AUTHOR]

The healthcare industry has witnessed significant interest in applying DTs (DTs), due to technological advancements. DTs are virtual replicas of physical entities that adapt to real‐time data, enabling predictions of their physical counterparts. DT technology enhances understanding of disease occurrence, enabling more accurate diagnoses and treatments. Integrating emerging technologies like big data, cloud computing, Virtual Reality (VR), and internet‐of‐things (IoT) provides a solid foundation for DT implementation in healthcare. However, defining DTs within the healthcare context still has become increasingly challenging. Therefore, exploring the potential of DTs in healthcare contributes to research, emphasizing their transformative impact on personalized medicine and precision healthcare. In this study, we present diverse healthcare applications of DTs, including healthcare 4.0, cardiac analysis, monitoring and management, data privacy, socio‐ethical, and surgical. Moreover, this paper discusses the software and simulations of DTs that can be used in these applications of healthcare, as well as, the future trends of DTs in healthcare. This article is categorized under:Application Areas > Health CareTechnologies > Computational Intelligence [ABSTRACT FROM AUTHOR]

Background: Healthcare 4.0. refers to the integration of advanced technologies, such as artificial intelligence (AI) and big data analysis, into the healthcare sector. Recognizing the impact of Healthcare 4.0 technologies in laboratory medicine (LM), we seek to assess the overall awareness and implementation of Healthcare 4.0 among members of the Korean Society for Laboratory Medicine (KSLM). Methods: A web-based survey was conducted using an anonymous questionnaire. The survey comprised 36 questions covering demographic information (seven questions), big data (10 questions), and AI (19 questions). Results: In total, 182 (17.9%) of 1,017 KSLM members participated in the survey. Thirty-two percent of respondents considered AI to be the most important technology in LM in the era of Healthcare 4.0, closely followed by 31% who favored big data. Approximately 80% of respondents were familiar with big data but had not conducted research using it, and 71% were willing to participate in future big data research conducted by the KSLM. Respondents viewed AI as the most valuable tool in molecular genetics within various divisions. More than half of the respondents were open to the notion of using AI as assistance rather than a complete replacement for their roles. Conclusions: This survey highlighted KSLM members’ awareness of the potential applications and implications of big data and AI. We emphasize the complexity of AI integration in healthcare, citing technical and ethical challenges leading to diverse opinions on its impact on employment and training. This highlights the need for a holistic approach to adopting new technologies. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The presence of wireless and mobile technologies in developing countries, the availability of low-cost, miniaturized wireless sensors, as well as the cost-efficient services provided by new hardware infrastructures have enabled new healthcare services, or new levels of quality and cost-efficiency in established ones. One crucial aspect of Healthcare 4.0 is the concept of P4 in Medicine: predictive, preventive, personalized and participatory. This approach, based on a comprehensive understanding of each patient biology, opposite to clustering patients into treatment groups, is being applied to reduce global health budgets by minimizing unnecessary use of drugs and procedures. These innovations come from the broad set of ICTs, and among them, the pillars of HC4.0 for their importance: IoT, Cloud Computing, and Big Data. Health 4.0 is used to improve the efficiency of physicians by enabling them to optimize resources and data availability. The future of health management will become timesaving and personalized as new technologies will empower individuals to conduct their health monitoring by using cyber–physical systems. Thanks to IoT, Cloud and Fog, as well as Big Data, researchers are allowed to design novel solutions, which are able to efficiently and effectively renew consolidated healthcare practices. [ABSTRACT FROM AUTHOR]

Healthcare 4.0 (H4.0) is the term corresponding to Industry 4.0 (I4.0). Like other industries, the healthcare industry has also gone through several technological changes and got nourished with them. The health care system of many countries is not mature enough to swiftly transform from its current state to the H4.0 state. Even the matured healthcare systems are facing several challenges while adopting H4.0 practices. The implementation of H4.0 has many challenges (or barriers) to be overcome. This paper has attempted to understand and analyze the challenges that the current Indian hospital management system faces while implementing H4.0. This study uses an integrated approach to the decision-making process to understand the importance of these barriers from the perspective of the Indian health care system. With the help of the Analytical Hierarchy Process (AHP), Interpretative Structural Modeling (ISM) and MICMAC analysis, the paper has mapped the importance of barriers which need to be overcome for to make implementation of H4.0 possible. [ABSTRACT FROM AUTHOR]

Introduction: Technologies like Big data are among one of the Quality 4.0 tools that seem to bring digital transformation in healthcare. Therefore, its awareness in healthcare providers is valuable to yield life-saving outcomes. A scarce literature has been found on the assessment of level of Big Data awareness, its utilization in healthcare and its role in firm performance. Aims & Objectives: The objective of the study is to identify the relationship between the level of awareness, usage and mediation of big data with firm’s performance (healthcare establishment) and the relationship of moderation of resistance to change with the level of awareness of big data and big data usage. Place and Duration of Study: Riphah International University, Lahore, Pakistan. Healthcare providers were approached through social media groups for the period of three months during 2022 across all provinces of Pakistan. Materials & Methods: A cross-sectional study was conducted using both self-administered and e-questionnaire as a survey tool. The tool was designed and validated specifically for Healthcare Establishments via extensive literature review and review by the experts. 540 questionnaires were randomly floated among the healthcare providers for the period of three months. Accurately filled 235 responses with a response rate of 52.2% were analyzed by using SPSS version 21. Results: Results supported the proposed model and showed positive and significant relationship between the level of awareness of Big Data and firm performance (? = 0.57, p< 0.01) that is mediated by Big Data usage (LLCI = 0.1920; ULCI = 0.3008). There is a positive and significant relationship between Big Data usage & firm performance (? = 0.66, p< 0.01). Furthermore, we observed resistance to change as moderator between level of awareness of Big Data and firm performance (? = -0.0068, p>0.05). Conclusion: The current study concludes that level of awareness of Big Data & Big Data usage has a significant positive relationship with firm’s performance. So, on the bases of above facts we recommend that healthcare establishments need to educate and train their managers and professionals about role of Big Data and its usage in health-related activities for improved performance.

Health organisations have adopted technologies since the 1960s, but only after the Industry 4.0 were such technologies systematized and organised under the H4.0 acronym. The pace at which digital information and communication applications have been developed in recent years challenge healthcare managers to choose assertively those with the largest potential impacts on their operations. In this paper, we propose using value stream mapping, a technique from the lean healthcare (LH) toolbox, to guide the choice of H4.0 digital applications that are more likely to support the improvement of value flows in healthcare organisations. We propose a three-step method, starting with mapping current and future value streams of the process under analysis, gathering data from team members on the indicated kaizen bursts and H4.0 digital applications, and finally assessing and ranking H4.0 digital applications that best support improvements and comply with attributes that characterise successful technological innovations. Our propositions are illustrated through a case study conducted in the sterilisation unit of a large public university hospital. Our findings indicate that three H4.0 digital applications should be prioritised to support the improvement of the value stream under analysis. Our method combines the simplicity of LH with more sophisticated solutions brought by H4.0. [ABSTRACT FROM AUTHOR]

Considered a new stage of human development, driven by a set of technologies developed from the three previous revolutions, the Fourth Industrial Revolution, or Industry 4.0, is based on the fusion of technologies and the interaction between physical, digital, and biological environments. Many sectors are being modified, and in the specific case of everything related to health care, the term Healthcare 4.0 appears. When technologies are created, evolved, and incorporated into healthcare systems, they impact several areas, including the architectural planning of the environment. In this way, this study aims to contextualize the current scenario of Healthcare 4.0, mapping emerging technologies as opportunities for optimizing complex systems, such as those in the health sector, and identifying their relationships and possible impacts on the hospital built environment. To this end, a scoping review was carried out in the Google Scholar, IEEE Xplore, PubMed, SciELO, Scopus, and Web of Science databases, aiming to provide an overview of research on the subject, where the following were identified: Healthcare 4.0, challenges, solutions and opportunities, benefits, technologies and characteristics of the physical environment. It was noticed that literature on the subject is scarce, highlighting the lack of studies and the existence of a field still under construction. [ABSTRACT FROM AUTHOR]

In the age of Healthcare 4.0, the deep integration of digital intelligent technology and medicine has put forward higher requirements for the digital literacy of medical students. Based on the literature review, this paper analyzes the current situation and deficiencies of digital literacy training in medical education: although most medical colleges have begun to attach great importance to digital technology education, there is a lack of systematic training programs and practice platforms. Based on this, guided by the theory of ecology of education, this paper focuses on four aspects of Collaboration between Hospitals and Universities, Policy support, Smart campus and Smart hospital at the macro level, five aspects of the training concept, Training mode, Evaluation mechanism, Teachers' digital literacy and Curriculum system at the meso level. At the micro level, there are mainly three functional entities, including the guidance and guarantee group, the transmission and development group, and the inheritance and pioneering group. These three entities, based on the macro and meso systems, promote the process of comprehensive improvement of medical students' digital literacy through the transmission of material flow, energy flow, and information flow (knowledge flow). [ABSTRACT FROM AUTHOR]

Human microarray gene profile analysis has become popular for early cancer detection. Due to security risks, Healthcare 4.0 standards make processing patient gene data difficult. Healthcare 4.0 guidelines must secure gene profile data storage and exchange. Recently several blockchain-based methods have been proposed for secure data processing for Cloud-based Healthcare 4.0 architecture. However, existing solutions not providing all the essential features like scalability, strong security measures, computational efficiency, and multiple security parameters. To overcome these problems, we propose a novel approach for the secure processing of the microarray gene expression data using computationally efficient, scalable, and highly secured Healthcare 4.0 standards. We propose a new Healthcare 4.0 architecture based on lightweight cryptography and blockchain technologies. Blockchain technology's recent advancements in cloud data storage and sharing indicate potential security features. Smart healthcare applications need a centralized approach for safe data storage and exchange. Edge layer (patients/gene users), fog, cloud storage, and blockchain make up Healthcare 4.0. Fog nodes store and search edge-layer gene data in blockchain-enabled cloud storage using lightweight cryptography. Elliptic Curve Cryptography (ECC) protects data processing and privacy using Elliptic Curve Diffie–Hellman (ECDH) and Elliptic Curve Digital Signature Algorithm (ECDSA). Meta-data from cloud-saved data is stored in blockchain to prevent tampering, forging, and quantum. We designed different threat-based case studies to demonstrate the model's efficiency and scalability. Computing efficiency and security are better with the proposed strategy. The outcome case studies showed that the suggested approach protected against various opponent models. The proposed method reduces encryption and decryption times by 325 and 20 ms, respectively. [ABSTRACT FROM AUTHOR]

Healthcare 4.0 is about collecting huge amounts of data and getting it to work in applications, enabling healthcare management decisions well‐informed while providing for important gains in effectiveness and cost control. Diagnostics based on the digital footprint depend on wearable technology's ability to gather and extract essential patient data. Artificial intelligence (AI) technologies allow the analysis of real‐time observed data and continuously developing from data to understand the world surrounding them. To connect and access intelligent healthcare services, people, and devices at any time, a secure wireless mobile communication system is essential. This article suggests a mHealth‐based patient monitoring system (mHealth‐PMS) based on AI for healthcare 4.0. Mobile healthcare applications motorized by Convolutional Neural Network (CNN) have enabled people to triage their conditions and preemptive treatment decisions. Information collected has been analysed for substantiating cause, and alert and preventive messages have been immediately sent through the mobile application. The performance analysis has been executed, and the proposed mobile application‐based surveillance provided much‐enhanced reporting of information quickly on diseases, symptoms, factors, and more. The mHealth‐PMS strategy shows an accuracy ratio of 95.6%, monitoring ratio of 93.5%, data management ratio of 94.4%, data security ratio of 91.7%, data privacy ratio of 92.1%, prediction ratio of 95.3%, a cost‐effective ratio of 25.5% compared to the existing methods. [ABSTRACT FROM AUTHOR]

Background: Healthcare 4.0 has transformed supply chain management in the healthcare sector, but there is a lack of comprehensive frameworks to evaluate the impact of Healthcare 4.0 technologies on sector operations, particularly in developing countries. Methods: This study introduces a multi-criteria framework that synergically combines the techno-economic implications of Healthcare 4.0 technologies to improve healthcare supply chain management. The proposed approach innovatively integrates fuzzy VIKOR and Entropy methods to handle data vagueness and uncertainty, using data collected from healthcare supply chain specialists in Lagos, Nigeria. Results: The developed framework identifies the most and least critical technical and economic parameters for Healthcare 4.0 implementation in healthcare supply chain management. It also determines the suitability of different Healthcare 4.0 technologies for supply chain management in the healthcare sector. Conclusions: The main innovation of this study lies in the development of a comprehensive and context-specific framework for evaluating Healthcare 4.0 technologies in healthcare supply chains. The framework offers a new perspective on technology evaluation and provides practical insights for decision-makers. The findings contribute to advancing knowledge and practice in this field, promoting the proper adoption of Healthcare 4.0 technologies in healthcare, particularly in developing countries. [ABSTRACT FROM AUTHOR]

This research investigates the mediation of resilience abilities on the relationship between Industry 4.0 technologies adoption and healthcare supply chain performance during the COVID-19 outbreak in Brazil and India. We surveyed 179 practitioners from organizations at different tiers of the healthcare supply chain (e.g., manufacturers, distributors, and care providers) in July 2021. Multivariate data techniques are used to the collected data to verify the hypotheses anchored on concepts from resource dependence theory. We identify two constructs of Industry 4.0 technologies (named after their predominant roles) and two constructs of resilience abilities (named according to the main abilities encompassed). Our findings indicate that resilience abilities mediate the impact of Industry 4.0 technologies on the performance of the healthcare supply chain since the beginning of the COVID-19 pandemic. However, the role played by adaptive and restorative abilities seems more prominent than the one played by anticipation and monitoring abilities. Further, sensing and communication technologies directly affect the healthcare supply chain's performance. Our study brings together three emerging topics related to the literature on the healthcare supply chain (Industry 4.0 adoption, resilience abilities development, and the disruptions caused by the COVID-19 pandemic). Although digitalization of the healthcare supply chain does improve its performance, our research indicated that its impact could be significantly enhanced when resilience abilities are concurrently developed, particularly in the Indian and Brazilian contexts. [ABSTRACT FROM AUTHOR]

This systematic review investigates the transformative impact of Healthcare 4.0 (HC4.0) technologies on healthcare service quality (HCSQ), focusing on their potential to enhance healthcare delivery while addressing critical challenges. This study reviewed 168 peer-reviewed articles from the Scopus database, published between 2005 and 2023. The selection process used clearly defined inclusion and exclusion criteria to identify studies focusing on advanced technologies such as artificial intelligence (AI), the Internet of Things (IoT), and big data analytics. Rayyan software facilitated systematic organization and duplicate removal, while manual evaluation ensured relevance and quality. The findings highlight HC4.0’s potential to improve service delivery, patient outcomes, and operational efficiencies but also reveal challenges, including interoperability, ethical concerns, and access disparities for underserved populations. The results were synthesized descriptively, uncovering key patterns and thematic insights while acknowledging heterogeneity across studies. Limitations include the absence of a formal risk-of-bias assessment and the diversity of methodologies, which precluded quantitative synthesis. This review emphasizes the need for future research on integration frameworks, ethical guidelines, and equitable access policies to realize HC4.0’s transformative potential. No external funding was received, and no formal protocol was registered.

This study proposes a problem-oriented methodology, using the algebraic operations proposed in the Quality Function Deployment's house-of-quality, to prioritise the integration of Industry 4.0 (I4.0) technologies in hospitals, allowing us to account for both the importance of healthcare value chain problems and the current level of adoption of I4.0 technologies. The proposed method combines different techniques that aimed at assessing the maturity of organisations regarding specific issues and indicating improvement opportunities. Our propositions are illustrated through two case studies carried out in a large Brazilian public hospital and in a private hospital in India, allowing a comparative analysis in which we identify similarities and divergences in improvement priorities in each institution. Findings indicate that the proposed method supports the systemic integration of I4.0 technologies into healthcare organisations regardless hospital ownership, as digital applications are ranked according to their potential to solve the problems prioritised by managers from both case studies. Digital integration in healthcare organisations has been restricted to specific sectors, departments, treatment or processes. Due to high complexity of hospitals, most of these initiatives have fallen short on results, frustrating managers' efforts. Thus, our method provides hospitals' managers, authorities, and governments guidelines to prioritise the digitisation of healthcare organisations. [ABSTRACT FROM AUTHOR]

The integration of Healthcare 4.0 and wireless body area networks (HC-WBAN 4.0) offers a unique opportunity to revolutionize healthcare by enabling real-time patient health monitoring. This integration can lead to various benefits, including early detection of health issues, timely intervention, and personalized treatment. However, this integration also poses several challenges, such as managing and analyzing large volumes of data generated by WBANs and ensuring reliable communication between WBAN devices and healthcare systems. To address these challenges, we propose a novel approach that leverages data-driven and context-aware packet scheduling algorithms based on machine/deep learning techniques. Our proposed approach can reduce redundant data generated by WBAN sensors and optimize the quality of service (QoS) by dynamically adapting the packet generation rate of each sensor. We use an auto-regression model and deep reinforcement learning (AR-DRL) to learn each sensor's optimal packet generation rate, which results in improved energy efficiency, transmission reliability, network stability, and transmission cost savings. Our simulation results show that our proposed approach outperforms state-of-the-art methods, achieving up to 72.87% and 66.44% reduction in transmission cost for a higher and minimum redundancy rate, respectively. These results demonstrate the potential of our approach to improve the efficiency and reliability of WBANs for healthcare applications and pave the way for more effective integration of Healthcare 4.0 and WBANs. Our proposed approach can help healthcare providers make more informed decisions, improve patient outcomes, and reduce healthcare costs. [ABSTRACT FROM AUTHOR]

The healthcare data requires accurate disease detection analysis, real-timemonitoring, and advancements to ensure proper treatment for patients. Consequently, Machine Learning methods are widely utilized in Smart Healthcare Systems (SHS) to extract valuable features fromheterogeneous and high-dimensional healthcare data for predicting various diseases and monitoring patient activities. These methods are employed across different domains that are susceptible to adversarial attacks, necessitating careful consideration. Hence, this paper proposes a crossover-based Multilayer Perceptron (CMLP) model. The collected samples are pre-processed and fed into the crossover-based multilayer perceptron neural network to detect adversarial attacks on themedical records of patients. Once an attack is detected, healthcare professionals are promptly alerted to prevent data leakage. The paper utilizes two datasets, namely the synthetic dataset and the University of Queensland Vital Signs (UQVS) dataset, from which numerous samples are collected. Experimental results are conducted to evaluate the performance of the proposed CMLP model, utilizing various performancemeasures such as Recall, Precision, Accuracy, and F1-score to predict patient activities. Comparing the proposed method with existing approaches, it achieves the highest accuracy, precision, recall, and F1-score. Specifically, the proposedmethod achieves a precision of 93%, an accuracy of 97%, an F1-score of 92%, and a recall of 92%. [ABSTRACT FROM AUTHOR]

The evolution of healthcare from its early beginnings to the recent healthcare 4.0 revolution has remarkably improved human life and living standards. The integration of emerging technologies has played a pivotal role in this progress. Notably, remote analysis of patient data has emerged as a promising approach, enabling telemedicine and remote patient monitoring enhancing healthcare accessibility and efficiency. However, the vulnerabilities of patient data to malicious network attacks pose critical challenges in the healthcare industry, potentially compromising patients' safety and undermining trust in the system. Thus, we have introduced a cutting-edge deep neural network (DNN) model to mitigate the risks associated with remote patient data transfer in healthcare 4.0 by bifurcating the data into malicious or non-malicious. The primary objective of the proposed framework is to ensure the secure and private communication of patient data, thereby fostering a more dependable and trustworthy healthcare ecosystem. Finally, the proposed framework is evaluated against various standard metrics such as accuracy, loss considering binary cross-entropy, receiver operating characteristic (ROC) curve, precision-recall curve and confusion matrix. The implications of the proposed framework offer data security to the healthcare system as it contributes to creating a more resilient and dependable ecosystem, thus promoting better patient care and ultimately elevating the overall life expectancy and well-being of individuals. [ABSTRACT FROM AUTHOR]

Securing Electronic Medical Records for Healthcare 4.0 is one of the prime requirements in the medical care service domains. Healthcare 4.0 refers to the evolution process of healthcare systems that leverage digital technologies, connectivity, and data-driven insights to provide more personalized and efficient care. With the increasing adoption of electronic healthcare records and the growing importance of data in the healthcare sector, ensuring the privacy of patient data becomes paramount. To ensuring the security for healthcare 4.0 in the current work a voice authentication-based system is devised. This enables the protection of electronic healthcare records (EHRs) in the association of healthcare 4.0. More precisely here the voice authentication is used as an additional layer of security for accessing and managing EHRs. The Voice authentication can also be categorized as the voice biometric. This is a technology that uses unique voice characteristics and patterns to verify the identity of individuals. The result establishes the innovative smart security arrangement for the Healthcare 4.0 using the voice authentication-based identity management infrastructure; and a modified version of asymmetric cryptosystem has been used inside the blockchain. The simulation outcome indicates better performance in the form of block generation time, block number, ensure the transaction, and minimize the latency of the transaction. [ABSTRACT FROM AUTHOR]

Over the last decade, and more recently with the coronavirus disease 2019 (COVID-19) pandemic, increased att ention has been given to the dynamic between health and urbanism. Features such as city design, the environment, and socioeconomic factors have been studied worldwide. Most studies have focused on a single element of the urban environment, making it difficult to understand the possible influence of related urban features. Furthermore, studies have addressed the issue of urbanism and health on different international, national, urban, and local scales, resulting in multiple inconsistencies. With the enhanced growth of cities, it is argued that the neighbourhood scale is the ideal scale to understand the built environmental-health nexus. More specifically, the paper reviews studies that focus on neighbourhood design and its influence on health, and studies that focus on residents' health-related behaviour. In addition, it maps the key developments in e-health and its expected influence on health services in neighbourhoods. Insights from these reviews are used to offer a preliminary conceptual framework for addressing health in neighbourhoods. [ABSTRACT FROM AUTHOR]

Industrial 4.0 technological breakthroughs highly impact healthcare 4.0 and enable transformative impact on the healthcare system by shifting towards efficient, patient-centric, data-driven, and robust global healthcare services. This paper presents a robust security framework; federated learning (FL) based RPL vulnerability analysis and attack detection (FRVA), for ensuring secure Healthcare 4.0. The FRVA is proposed to defend the RPLhealthcare 4.0 against multiple attacks by applying deep learning-based fuzzing and FL-enabled hybrid learning. RPL vulnerabilities are analyzed using randomly generated inputs by deep learning-based fuzzing. Further, it feeds the RPL vulnerability-rich fuzzed output dataset to the FL-hybrid learning model. The second model improved the customized local learning models using globally shared information according to FL, resulting in high learning accuracy with precise attack detection. The proposed FRVA runs the vulnerability analysis and attack detection at the edges to prolong the network lifetime with high security. Moreover, the performance of the FRVA is validated through Python-based simulations using different metrics. The simulation results demonstrate that the proposed FLbased hybrid CNN-LSTM strategy enhances the accuracy by 5.55% and 12.9%, respectively, compared with the individual CNN and LSTM methods. It also enhances the accuracy by 25.83% and 5.97% than the other conventional FL-based detection strategies. [ABSTRACT FROM AUTHOR]

Although brain tumours are few, they have one of the highest mortality rates among all types of cancer due to their abnormal growth and proliferation. Brain tumours develop due to the accumulation of abnormal tissues in the brain. Various forms of abnormal tissue exist, however, in the majority of cases, they develop in a regular manner and perish without creating any detrimental effects. Digital twins are occasionally known as digital mirrors, digital mapping, and digital replicas. All of these are synonymous terms for the identical entity. It is a technique for transferring digital or physical information from one realm to another. Image processing involves enhancing or eliminating data from a photograph to achieve a certain objective. Convolutional neural networks are a specific type of neural network that take signals from images as input and produce the image itself or a subset of its elements as output. This research presents a technique for identifying brain cancers using digital replicas and advanced machine learning algorithms by analysing MRI images. Images obtained from MRI machines are stored in a centralised cloud using Internet of Things (IoT) digital devices. The input pictures and other health-related data are then retrieved from cloud storage. The Particle Swarm Optimization approach chooses features. Brain tumor images are classified using machine learning techniques such as convolutional neural networks, support vector machines, and extreme learning machines. The CNN algorithm demonstrates greater accuracy when assessing MRI images for the purpose of identifying brain tumours.

Summary: In recent years, smart healthcare, artificial intelligence (AI)‐aided diagnostics, and automated surgical robots are just a few of the innovations that have emerged and gained popularity with the advent of Healthcare 4.0. Such technologies are powered by machine learning (ML) and deep learning (DL), which are preferable for disease diagnosis, identifying patterns, prescribing treatments, and forecasting diseases like stroke prediction, cancer prediction and so forth. Nevertheless, much data is needed for AI, ML, and DL‐based systems to train effectively and provide the desired outcomes. Further, it raises concerns about data privacy, security, communication overhead, regulatory compliance and so forth. Federated learning (FL) is a technology that protects data security and privacy by limiting data sharing and utilizing model information of distributed systems to enhance performance. However, existing approaches are traditionally verified on pre‐established datasets that fail to capture real‐life applicability. Therefore, this study proposes an AI‐enabled stroke prediction architecture consisting of FL based on the artificial neural network (ANN) model using data from actual stroke cases. This architecture can be implemented on healthcare‐based wearable devices (WD) for real‐time use as it is effective, precise, and computationally affordable. In order to continuously enhance the performance of the global model, the proposed FL‐based architecture aggregates the optimizer weights of many clients using a fifth‐generation (5G) communication channel. Then, the performance of the proposed FL‐based architecture is studied based on multiple parameters such as accuracy, precision, recall, bit error rate, and spectral noise. It outperforms the traditional approaches regarding accuracy, which is 5% to 10% higher. [ABSTRACT FROM AUTHOR]

Quantum machine learning (QML) is an emerging field that combines the power of quantum computing with machine learning (ML) techniques to solve complex problems. In recent years, QML algorithms have shown tremendous potential in various applications such as image recognition, natural language processing, health care, finance, and drug discovery. QML algorithms aim to reduce computation costs and solve complex problems beyond the scope of classical machine learning algorithms. In this article, we study the performance of two QML algorithms, that is, quantum support vector classifiers (QSVC) and variational quantum classifiers (VQC), for chronic heart disease prediction in Healthcare 4.0. The performance of the two classifiers is assessed using different evaluation metrics like accuracy, precision, recall, and F1 score. The authors concluded the superior performance of QSVC over VQC with an accuracy of 82%. [ABSTRACT FROM AUTHOR]

The implementation of Industry 4.0 in the healthcare sector to improve community health is of great importance. Therefore, it is crucial to identify the critical success factors for implementing Industry 4.0 in the healthcare sector, model them, and analyze scenarios for targeted interventions. This issue has not been investigated in previous studies, and this research aims to fill this research gap. This research was conducted in two qualitative and quantitative stages. The statistical population was experts in both stages, and the judgmental and snowball sampling methods were used. The first stage had a population size of 17, determined based on theoretical saturation, while the second stage had a population size of 10. Thematic analysis was used as the data analysis method in the first stage, and fuzzy cognitive mapping was used in the second stage. The results showed that "competent managers," "support and cooperation," and "competent human resources" have the most significant impact, while "project management," "appropriate planning," and "support and cooperation" are the most susceptible. Furthermore, "support and cooperation," "appropriate planning," and "project management" are the most central. Three forward and three backward scenarios were designed for more effective interventions. It is recommended to improve the organization's educational system, strengthen the succession system, implement transparent contracts, and improve the quality of human resource management to achieve independent variables. [ABSTRACT FROM AUTHOR]

The healthcare industry is transforming into Healthcare 4.0 (H4.0), an era characterized by smart and connected healthcare systems. This study presents a conceptual framework that classifies H4.0 digital technologies into information and communication technology bundles within the healthcare value chain. It also identifies barriers and evaluates digital technologies' impact on quality measures through a systematic literature review and meta-analysis approach following the PRISMA protocol. The analysis reveals that digital technologies in the healthcare sector traditionally consist of sensing-communication and processing-actuation technologies. The findings highlight the significant influence of H4.0 digital technologies on three quality measures: patient safety, patient experience/satisfaction, and clinical effectiveness. While these technologies offer potential benefits, they pose challenges for patients and clinicians, including intellectual property and significance concerns, especially in North America. The proposed framework addresses these issues and enables stakeholders to prioritize, review, and analyze H4.0 digital technologies to enhance patient safety, experience, and clinical effectiveness. This research contributes to the existing literature by being the first comprehensive analysis of the impact of H4.0 technologies on the quality of care. The framework provided in this study offers valuable guidance for stakeholders in selecting appropriate technologies to improve patient outcomes and support the healthcare value chain. [ABSTRACT FROM AUTHOR]

Abstract Industry 4.0 refers to the digital transformation in the manufacturing domain through new technology. Currently, it expands well beyond manufacturing, affecting many areas of life and posing implications for all types of business. This paper focuses on the relationships between Industry 4.0 and Healthcare which transitions to increased interconnectivity, automation and smart decision making. The integration context of Industry 4.0 into Healthcare is only partly understood. Little was done until now to consolidate what is known on the integration benefits and the challenges. This article reports results of a systematic mapping study that analysed 69 papers to extract knowledge about the concepts of Industry 4.0 and the emerging Healthcare 4.0., and the relationships between them. We found 10 different perspectives of Healthcare 4.0, ranging from strategic to tactical and operational levels. Next, our results show: (i) nine applications of Industry 4.0 in the Healthcare domain: Augmented Reality and Simulation, Autonomous Robotics, Cybersecurity, Big Data Analytics, Internet of Things, Cloud Computing, Additive Manufacturing and Systems Integration; and (ii) 10 benefits and nine challenges in Healthcare 4.0. The most frequently mentioned benefits are patients' diagnosis, monitoring, treatment, and financial benefits. The most researched challenges are data fragmentation, heterogeneity, complexity, and privacy.