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The purpose of this study is to examine the state of research into adoption of machine learning systems within the health sector, to identify themes that have been studied and observe the important gaps in the literature that can inform a research agenda going forward.

A systematic literature strategy was utilized to identify and analyze scientific papers between 2012 and 2022. A total of 28 articles were identified and reviewed.

The outcomes reveal that while advances in machine learning have the potential to improve service access and delivery, there have been sporadic growth of literature in this area which is perhaps surprising given the immense potential of machine learning within the health sector. The findings further reveal that themes such as recordkeeping, drugs development and streamlining of treatment have primarily been focused on by the majority of authors in this area.

The search was limited to journal articles published in English, resulting in the exclusion of studies disseminated through alternative channels, such as conferences, and those published in languages other than English. Considering that scholars in developing nations may encounter less difficulty in disseminating their work through alternative channels and that numerous emerging nations employ languages other than English, it is plausible that certain research has been overlooked in the present investigation.

This review provides insights into future research avenues for theory, content and context on adoption of machine learning within the health sector.

The purpose of this study is to design a wearable medical device as a human care platform and to introduce the design details, key technologies and practical implementation methods of the system.

A multi-channel data acquisition scheme based on PCI-E (rapid interconnection of peripheral components) was proposed. The flexible biosensor is integrated with the flexible data acquisition card with monitoring capability, and the embedded (device that can operate independently) chip STM32F103VET6 is used to realize the simultaneous processing of multi-channel human health parameters. The human health parameters were transferred to the upper computer LabVIEW by intelligent clothing through USB or wireless Bluetooth to complete the transmission and processing of clinical data, which facilitates the analysis of medical data.

The smart clothing provides a mobile medical cloud platform for wearable medical through cloud computing, which can continuously monitor the body's wrist movement, body temperature and perspiration for 24 h. The result shows that each channel is completely accurate to the top computer display, which can meet the expected requirements, and the wearable instant care system can be applied to healthcare.

The smart clothing in this study is based on the monitoring and diagnosis of textiles, and the electronic communication devices can cooperate and interact to form a wearable textile system that provides medical monitoring and prevention services to individuals in the fastest and most accurate way. Each channel of the system is precisely matched to the display screen of the host computer and meets the expected requirements. As a real-time human health protection platform technology, continuous monitoring of human vital signs can complete the application of human motion detection, medical health monitoring and human–computer interaction. Ultimately, such an intelligent garment will become an integral part of our everyday clothing.

The purpose of this paper is to give an overview of artificial intelligence (AI) and other AI-enabled technologies and to describe how COVID-19 affects various industries such as health care, manufacturing, retail, food services, education, media and entertainment, banking and insurance, travel and tourism. Furthermore, the authors discuss the tactics in which information technology is used to implement business strategies to transform businesses and to incentivise the implementation of these technologies in current or future emergency situations.

The review provides the rapidly growing literature on the use of smart technology during the current COVID-19 pandemic.

The 127 empirical articles the authors have identified suggest that 39 forms of smart technologies have been used, ranging from artificial intelligence to computer vision technology. Eight different industries have been identified that are using these technologies, primarily food services and manufacturing. Further, the authors list 40 generalised types of activities that are involved including providing health services, data analysis and communication. To prevent the spread of illness, robots with artificial intelligence are being used to examine patients and give drugs to them. The online execution of teaching practices and simulators have replaced the classroom mode of teaching due to the epidemic. The AI-based Blue-dot algorithm aids in the detection of early warning indications. The AI model detects a patient in respiratory distress based on face detection, face recognition, facial action unit detection, expression recognition, posture, extremity movement analysis, visitation frequency detection, sound pressure detection and light level detection. The above and various other applications are listed throughout the paper.

Research is largely delimited to the area of COVID-19-related studies. Also, bias of selective assessment may be present. In Indian context, advanced technology is yet to be harnessed to its full extent. Also, educational system is yet to be upgraded to add these technologies potential benefits on wider basis.

First, leveraging of insights across various industry sectors to battle the global threat, and smart technology is one of the key takeaways in this field. Second, an integrated framework is recommended for policy making in this area. Lastly, the authors recommend that an internet-based repository should be developed, keeping all the ideas, databases, best practices, dashboard and real-time statistical data.

As the COVID-19 is a relatively recent phenomenon, such a comprehensive review does not exist in the extant literature to the best of the authors’ knowledge. The review is rapidly emerging literature on smart technology use during the current COVID-19 pandemic.

This literature review aims to assess the current research on healthcare job availability and skilled professionals. The objective of this research is to identify challenges caused by the imbalance between healthcare service demand and qualified professionals and propose potential solutions and future research directions.

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method was employed as the guiding framework for conducting this review. A qualitative research design analyzed 38 peer-reviewed, evidence-based research works from 50 journal publications. Inclusion criteria focused on empirical studies, observational research and comprehensive reviews published within the last ten years. Thematic and discourse analysis categorized themes and factors explored in selected publications.

The findings highlight significant challenges in the healthcare sector regarding job availability and skilled professionals. Developed countries face understaffed healthcare facilities, resulting in increased workloads and compromised care. Developing countries experience high rates of unemployment among healthcare graduates due to limited resources and mentorship.

Improving educational infrastructure, expanding training opportunities and increasing healthcare investments are crucial for nurturing a skilled workforce. Implementing effective retention policies, fostering international collaborations and addressing socioeconomic determinants can create a sustainable job market.

The healthcare sector faces critical challenges in balancing job availability and skilled professionals. Strategic solutions are proposed to create a sustainable and equitable healthcare workforce. By implementing recommendations and conducting further research, access to quality healthcare and global public health outcomes can be improved.

Following the surge of documents laying out organizations' ethical principles for their use of artificial intelligence (AI), there is a growing demand for translating ethical principles to practice through AI governance (AIG). AIG has emerged as a rapidly growing, yet fragmented, research area. This paper synthesizes the organizational AIG literature by outlining research themes and knowledge gaps as well as putting forward future agendas.

The authors undertake a systematic literature review on AIG, addressing the current state of its conceptualization and suggesting future directions for AIG scholarship and practice. The review protocol was developed following recommended guidelines for systematic reviews and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA).

The results of the authors’ review confirmed the assumption that AIG is an emerging research topic with few explicit definitions. Moreover, the authors’ review identified four themes in the AIG literature: technology, stakeholders and context, regulation and processes. The central knowledge gaps revealed were the limited understanding of AIG implementation, lack of attention to the AIG context, uncertain effectiveness of ethical principles and regulation, and insufficient operationalization of AIG processes. To address these gaps, the authors present four future AIG agendas: technical, stakeholder and contextual, regulatory, and process. Going forward, the authors propose focused empirical research on organizational AIG processes, the establishment of an AI oversight unit and collaborative governance as a research approach.

To address the identified knowledge gaps, the authors present the following working definition of AIG: AI governance is a system of rules, practices and processes employed to ensure an organization's use of AI technologies aligns with its strategies, objectives, and values, complete with legal requirements, ethical principles and the requirements set by stakeholders. Going forward, the authors propose focused empirical research on organizational AIG processes, the establishment of an AI oversight unit and collaborative governance as a research approach.

For practitioners, the authors highlight training and awareness, stakeholder management and the crucial role of organizational culture, including senior management commitment.

For society, the authors review elucidates the multitude of stakeholders involved in AI governance activities and complexities related to balancing the needs of different stakeholders.

By delineating the AIG concept and the associated research themes, knowledge gaps and future agendas, the authors review builds a foundation for organizational AIG research, calling for broad contextual investigations and a deep understanding of AIG mechanisms. For practitioners, the authors highlight training and awareness, stakeholder management and the crucial role of organizational culture, including senior management commitment.

This study aims to address the global food waste problem in restaurants by analyzing customer sales information provided by restaurant tickets to gain valuable insights into directing sales of perishable products and optimizing product purchases according to customer demand.

A system based on unsupervised machine learning (ML) data models was created to provide a simple and interpretable management tool. This system performs analysis based on two elements: first, it consolidates and visualizes mutual and nontrivial relationships between information features extracted from tickets using multicomponent analysis, bootstrap resampling and ML domain description. Second, it presents statistically relevant relationships in color-coded tables that provide food waste-related recommendations to restaurant managers.

The study identified relationships between products and customer sales in specific months. Other ticket elements have been related, such as products with days, hours or functional areas and products with products (cross-selling). Big data (BD) technology helped analyze restaurant tickets and obtain information on product sales behavior.

This study addresses food waste in restaurants using BD and unsupervised ML models. Despite limitations in ticket information and lack of product detail, it opens up research opportunities in relationship analysis, cross-selling, productivity and deep learning applications.

The value and originality of this work lie in the application of BD and unsupervised ML technologies to analyze restaurant tickets and obtain information on product sales behavior. Better sales projection can adjust product purchases to customer demand, reducing food waste and optimizing profits.

Against the background of the I50 paradigm, this paper queries in what ways blockchain and blockchain-based applications deployed in the smart city context facilitate the integration of the I50 paradigm in smart urban contexts.

A mixed methods approach is applied. First, by means of desk research and thematic literature review, a conceptual model integrating the I50 paradigm, smart city and blockchain-based solutions is built. Second, science mapping bibliometric analysis (SciMat) based on keywords’ co-occurrence is applied to a sample of 491 research articles to identify key domains of blockchain-based applications’ use in smart city. Third, a semi-systematic literature review complements insights gained through SciMat. Fourth, the findings are interpreted through the precepts of the conceptual model devised earlier.

The key blockchain-based applications in smart cities pertain to two domains, i.e. the foundational, service facilitation-oriented domain, including security (and safety), networks, computing, resource management and the service delivery-oriented domain, including mobility, energy and healthcare. Blockchain serves as the key building block for applications developed to deliver functions specific to each of the thus identified domains. A substantial layering of blockchain-based tools and applications is necessary to advance from the less to the more complex functional domains of the smart city.

At the conceptual level, the intricacies of the (making of the) I50 paradigm are discussed and a case for I50 – smart city – blockchain nexus is made. Easton’s input–output model as well as constructivism is referenced. At the empirical level, the key major domains of blockchain-based applications are discussed; those that bear the prospect of integrating the I50 paradigm in the smart city are highlighted. At the methodological level, a strategic move is made aimed at restoring the literature review’s role as subservient to the key line of exploration, to justify and ultimately support it, rather than to showcase the literature review as the ultimate purpose for itself.

Manufacturing small and medium-sized enterprises (SMEs) have already noticed the tangible benefits offered by artificial intelligence (AI). Several approaches have been proposed with a view to support them in the processes entailed in this innovation path. These include multisided platforms created to enable the connection between SMEs and AI developers, making it easier for them to network each other. While such platforms are complex, they facilitate simultaneous interaction with several stakeholders and reaching out to new potential users (both SMEs and AI developers), through a collaboration with supporting ecosystems such as digital innovation hubs (DIHs).

Mixed methods were used. The literature review was performed to identify the existing approaches within and outside the manufacturing domain. Computer-assisted telephonic (in-depth) interviewing , was conducted to include perspectives of AI platform stakeholders and collect primary data from various European countries.

Several challenges and barriers for AI platform stakeholders were identified alongside the corresponding best practices and guidelines on how to address them.

An effective approach was proposed to provide support to the industrial platform managers in this field, by developing guidelines and best practices on how a platform should build its services to support the ecosystem.

The rapid advancement of technology has transformed the health-care industry and enabled the emergence of m-Health solutions such as health apps. The viability and success of these apps depends on the definition of a monetization model appropriate to their specificities. In this sense, the purpose of this paper is to study the mechanisms of monetization of health apps, to stablish how alternative revenues determine if a health app is to be free or paid.

Probability models are used to identify the factors that explain if a health app is free or paid.

Results show that the presence of alternative monetization mechanisms negatively impacts the likelihood of a health app being paid for. The use of personal data to customize advertising (the monetization of “privacy capital”) or the inclusion of ads on the app are alternative means of monetization with potential to decrease the likelihood of a health app being paid for. The possibility of in-app purchases has a lower negative impact on the probability of a health app being paid for. The choice of platform to commercialize an app is also a strategic decision that influences the likelihood of an app being paid for.

This work stands out for bringing together the two largest platforms present in Portugal and for focusing on the perspective of revenue and monetization of health apps and not on the perspective of downloads.

This essay aims to draw attention to the idea of a new research approach to knowledge management (KM) labelled responsible KM (rKM) as a possible answer to not only address the consequences of the pandemic but also other present and upcoming societal challenges.

This essay has been prepared by a KM researcher who shares their own personal views and opinion regarding past and current societal developments and based on that offers a potentially new KM direction.

Switching the focus to rKM may help address current and upcoming social challenges that can only be addressed jointly by the global community and which would also involve a new consideration of the “knowledge” resource.

The essay proposes a new alternative approach to KM called rKM that is based on ideas that to the author’s knowledge have not been discussed in this way in the contemporary literature on KM.