Background Internet of Things (IoT) is growing exponentially and can become an enormous source of information. IoT has provided new opportunities in different domains but also challenges are apparent that must be addressed. Little attention has been paid to the potential use of IoT in the food safety domain and therefore the aim of this study was to fill this gap. Scope and approach This paper reviews the use of IoT technology in food safety. A literature review was conducted using academic documents written in English language and published in peer-reviewed scientific journals. The relevant articles were analysed using the bibliometric networks to investigate the relationships between authors, countries, and content. Key findings and conclusions IoT in food safety is a relatively new approach; the first article appeared in 2011 and has increased since then. Majority of these studies were performed by Chinese universities and the main IoT applications reported were on food supply chains to trace food products, followed by monitoring of food safety and quality. The vast majority of publications were related to food, meat, cold chain products and agricultural products. These studies used sensors to monitor mainly temperature, humidity, and location. The most frequently used communication technologies were Internet, radio frequency identifications (RFID) and wireless sensor networks (WSN). This article identifies knowledge gaps to inform the community, industry, government authorities about research directions for IoT in food safety.

Internet of Things in food safety: Literature review and a bibliometric analysis

Background: As a critical driving power to promote health care, the health care–related artificial intelligence (AI) literature is growing rapidly.
Objective: The purpose of this analysis is to provide a dynamic and longitudinal bibliometric analysis of health care–related AI publications.
Methods: The Web of Science (Clarivate PLC) was searched to retrieve all existing and highly cited AI-related health care research papers published in English up to December 2019. Based on bibliometric indicators, a search strategy was developed to screen the title for eligibility, using the abstract and full text where needed. The growth rate of publications, characteristics of research activities, publication patterns, and research hotspot tendencies were computed using the HistCite software.
Results: The search identified 5235 hits, of which 1473 publications were included in the analyses. Publication output increased an average of 17.02% per year since 1995, but the growth rate of research papers significantly increased to 45.15% from 2014 to 2019. The major health problems studied in AI research are cancer, depression, Alzheimer disease, heart failure, and diabetes. Artificial neural networks, support vector machines, and convolutional neural networks have the highest impact on health care. Nucleosides, convolutional neural networks, and tumor markers have remained research hotspots through 2019.
Conclusions: This analysis provides a comprehensive overview of the AI-related research conducted in the field of health care, which helps researchers, policy makers, and practitioners better understand the development of health care–related AI research and possible practice implications. Future AI research should be dedicated to filling in the gaps between AI health care research and clinical applications.

Artificial Intelligence in Health Care: Bibliometric Analysis.

Blockchain technology has emerged as a promising technology with far-reaching implications for the food industry. The combination of immutability, enhanced visibility, transparency and data integrity provides numerous benefits that improve trust in extended food supply chains (FSCs). Blockchain can enhance traceability, enable more efficient recall and aids in risk reduction of counterfeits and other forms of illicit trade. Moreover, blockchain can enhance the integrity of credence claims such as sustainably sourced, organic or faith-based claims such as kosher or halal by integrating the authoritative source of the claim (e.g., the certification body or certification owner) into the blockchain to verify the claim integrity and reassure business customers and end consumers. Despite the promises and market hype, a comprehensive overview of the potential benefits and challenges of blockchain in FSCs is still missing. To bridge this knowledge gap, we present the findings from a systematic review and bibliometric analysis of sixty-one (61) journal articles and synthesize existing research. The main benefits of blockchain technology in FCSs are improved food traceability, enhanced collaboration, operational efficiencies and streamlined food trading processes. Potential challenges include technical, organizational and regulatory issues. We discuss the theoretical and practical implications of our research and present several ideas for future research.

Blockchain Technology in the Food Industry: A Review of Potentials, Challenges and Future Research Directions

With the rapid development of artificial intelligence (AI) technologies and a continuously growing interest in their application in educational contexts, there has been significant growth in the scientific literature in relation to the application of AI in education (AIEd). This study aims to present multiple perspectives on the development of AIEd in terms of relevant grants, conferences, journals, software tools, article trends, top issues, institutions, and researchers to provide an overview of AIEd for its further development and implementation. With this study, we contribute to the research field by enabling educators and scholars to understand the status and development of relevant grants and publications concerning AIEd. Also, findings concerning active actors can help educators and scholars identify the active researchers and institutions in the research on AIEd. Furthermore, researchers and educators are able to identify relevant journals and be more aware of major issues in AIEd studies. In addition, we also highlight the significance and necessity of the launch of the new Elsevier journal AIEd-related journal named Computers & Education: Artificial Intelligence.

A multi-perspective study on artificial intelligence in education: Grants, conferences, journals, software tools, institutions, and researchers

Smartphone as a monitoring tool for bipolar disorder: a systematic review including data analysis, machine learning algorithms and predictive modelling

Artificial intelligence (AI)-based techniques have been widely applied in depression research and treatment. Nonetheless, there is currently no systematic review or bibliometric analysis in the medical literature about the applications of AI in depression. We performed a bibliometric analysis of the current research landscape, which objectively evaluates the productivity of global researchers or institutions in this field, along with exploratory factor analysis (EFA) and latent dirichlet allocation (LDA). From 2010 onwards, the total number of papers and citations on using AI to manage depressive disorder have risen considerably. In terms of global AI research network, researchers from the United States were the major contributors to this field. Exploratory factor analysis showed that the most well-studied application of AI was the utilization of machine learning to identify clinical characteristics in depression, which accounted for more than 60% of all publications. Latent dirichlet allocation identified specific research themes, which include diagnosis accuracy, structural imaging techniques, gene testing, drug development, pattern recognition, and electroencephalography (EEG)-based diagnosis. Although the rapid development and widespread use of AI provide various benefits for both health providers and patients, interventions to enhance privacy and confidentiality issues are still limited and require further research.

https://www.mdpi.com/1660-4601/16/12/2150/pdf

The Current Research Landscape on the Artificial Intelligence Application in the Management of Depressive Disorders: A Bibliometric Analysis

The rising prevalence and global burden of diabetes fortify the need for more comprehensive and effective management to prevent, monitor, and treat diabetes and its complications. Applying artificial intelligence in complimenting the diagnosis, management, and prediction of the diabetes trajectory has been increasingly common over the years. This study aims to illustrate an inclusive landscape of application of artificial intelligence in diabetes through a bibliographic analysis and offers future direction for research. Bibliometrics analysis was combined with exploratory factor analysis and latent Dirichlet allocation to uncover emergent research domains and topics related to artificial intelligence and diabetes. Data were extracted from the Web of Science Core Collection database. The results showed a rising trend in the number of papers and citations concerning AI applications in diabetes, especially since 2010. The nucleus driving the research and development of AI in diabetes is centered around developed countries, mainly consisting of the United States, which contributed 44.1% of the publications. Our analyses uncovered the top five emerging research domains to be: (i) use of artificial intelligence in diagnosis of diabetes, (ii) risk assessment of diabetes and its complications, (iii) role of artificial intelligence in novel treatments and monitoring in diabetes, (iv) application of telehealth and wearable technology in the daily management of diabetes, and (v) robotic surgical outcomes with diabetes as a comorbid. Despite the benefits of artificial intelligence, challenges with system accuracy, validity, and confidentiality breach will need to be tackled before being widely applied for patients' benefits.

https://www.mdpi.com/1660-4601/17/6/1982/pdf

Kenneth K. Gwee

Papers

The Current Research Landscape on the Artificial Intelligence Application in the Management of Depressive Disorders: A Bibliometric Analysis

Modeling the Research Landscapes of Artificial Intelligence Applications in Diabetes (GAPRESEARCH).