Responsible Artificial Intelligence as a Secret Ingredient for Digital Health: Bibliometric Analysis, Insights, and Research Directions

Mapping and analyzing the application of digital health for stroke rehabilitation: scientometric analysis

INTRODUCTION

A modern and accessible healthcare system requires digital innovation and connectivity. The term "Digital health" covers vide range technologies, such as mobile health and applications, electronic records, telehealth and telemedicine, wearable devices, robotics, virtual reality and artificial intelligence.

METHODS

Scientometrics is the method that we have done in this study by Cite Space and VOSviewer software, and the result of searching the Web of Science database in plain text format to perform analysis and scientometrics and create outputs in the form of graphs and tables in the field of digital health has been used in stroke rehabilitation.

RESULT

A total of 2933 documents related to digital health technologies in stroke rehabilitation were identified by searching for the terms "stroke rehabilitation" or "stroke recovery" in the title and "digital health" across all fields. The strongest citations related to cerebrovascular disease spanned from 1994 to 2007, with randomised clinical trials occurring almost simultaneously and ended by 2012. Consequently, stroke rehabilitation by virtual reality technology has obtained the most citations and clinical trials and as an important part of digital health in the future research process.

CONCLUSION

This scientometric study offers insights into how digital health technology can assist stroke patients in self-managing their health and well-being, in addition to supporting integrated stroke rehabilitation. The analysis revealed that three themes were present: author contributors and collaboration networks, temporal evolution, the strongest citation explosions for digital health technologies in stroke rehabilitation research, and semantic analysis.

Revolutionising healthcare with artificial intelligence: A bibliometric analysis of 40 years of progress in health systems

The development of artificial intelligence (AI) has revolutionised the medical system, empowering healthcare professionals to analyse complex nonlinear big data and identify hidden patterns, facilitating well-informed decisions. Over the last decade, there has been a notable trend of research in AI, machine learning (ML), and their associated algorithms in health and medical systems. These approaches have transformed the healthcare system, enhancing efficiency, accuracy, personalised treatment, and decision-making. Recognising the importance and growing trend of research in the topic area, this paper presents a bibliometric analysis of AI in health and medical systems. The paper utilises the Web of Science (WoS) Core Collection database, considering documents published in the topic area for the last four decades. A total of 64,063 papers were identified from 1983 to 2022. The paper evaluates the bibliometric data from various perspectives, such as annual papers published, annual citations, highly cited papers, and most productive institutions, and countries. The paper visualises the relationship among various scientific actors by presenting bibliographic coupling and co-occurrences of the author's keywords. The analysis indicates that the field began its significant growth in the late 1970s and early 1980s, with significant growth since 2019. The most influential institutions are in the USA and China. The study also reveals that the scientific community's top keywords include 'ML', 'Deep Learning', and 'Artificial Intelligence'.

A Structured Analysis to study the Role of Machine Learning and Deep Learning in The Healthcare Sector with Big Data Analytics

Machine and deep learning are used worldwide. Machine Learning (ML) and Deep Learning (DL) are playing an increasingly important role in the healthcare sector, particularly when combined with big data analytics. Some of the ways that ML and DL are being used in healthcare include Predictive Analytics, Medical Image Analysis, Drug Discovery, Personalized Medicine, and Electronic Health Records (EHR) Analysis. It has become one of the advanced and popular tool for computer science domain.' The advancement of ML and DL for various fields has opened new avenues for research and development. It could revolutionize prediction and decision-making capabilities. Due to increased awareness about the ML and DL in the healthcare, it has become one of the vital approaches for the sector. High-volume of unstructured, and complex medical imaging data from health monitoring devices, gadgets, sensors, etc. Is the biggest trouble for healthcare sector. The current study uses analysis to examine research trends in adoption of machine learning and deep learning approaches in the healthcare sector. The WoS database for SCI/SCI-E/ESCI journals are used as the datasets for the comprehensive analysis. Apart from these various search strategy are utilised for the requisite scientific analysis of the extracted research documents. Bibliometrics R statistical analysis is performed for year-wise, nation-wise, affiliation-wise, research area, sources, documents, and author based analysis. VOS viewer software is used to create author, source, country, institution, global cooperation, citation, co-citation, and trending term co-occurrence networks. ML and DL, combined with big data analytics, have the potential to revolutionize healthcare by improving patient outcomes, reducing costs, and accelerating the development of new treatments, so the current study will help academics, researchers, decision-makers, and healthcare professionals understand and direct research.

Stuck in translation: Stakeholder perspectives on impediments to responsible digital health

Spurred by recent advances in machine learning and electronic hardware, digital health promises to profoundly transform medicine. At the same time, however, it raises conspicuous ethical and regulatory issues. This has led to a growing number of calls for responsible digital health. Based on stakeholder engagement methods, this paper sets out to identify core impediments hindering responsible digital health in Switzerland. We developed a participatory research methodology to access stakeholders' fragmented knowledge of digital health, engaging 46 digital health stakeholders over a period of five months (December 2020-April 2021). We identified ineffective stakeholder collaboration, lack of ethical awareness among digital health innovators, and lack of relevant regulation as core impediments to responsible digital health. The stakeholders' accounts indicate that ethical concerns may considerably slow the pace of digital health innovation - implying that responsible innovation is a core catalyst for the progress of digital health overall.

Artificial Intelligence and Blockchain Integration in Business: Trends from a Bibliometric-Content Analysis

Artificial intelligence (AI) and blockchain are the two disruptive technologies emerging from the Fourth Industrial Revolution (IR4.0) that have introduced radical shifts in the industry. The amalgamation of AI and blockchain holds tremendous potential to create new business models enabled through digitalization. Although research on the application and convergence of AI and blockchain exists, our understanding of the utility of its integration for business remains fragmented. To address this gap, this study aims to characterize the applications and benefits of integrated AI and blockchain platforms across different verticals of business. Using bibliometric analysis, this study reveals the most influential articles on the subject based on their publications, citations, and importance in the intellectual network. Using content analysis, this study sheds light on the subject's intellectual structure, which is underpinned by four major thematic clusters focusing on supply chains, healthcare, secure transactions, and finance and accounting. The study concludes with 10 application areas in business that can benefit from these technologies.

An AI-based Decision Support System for Predicting Mental Health Disorders

Approximately one billion individuals suffer from mental health disorders, such as depression, bipolar disorder, schizophrenia, and anxiety. Mental health professionals use various assessment tools to detect and diagnose these disorders. However, these tools are complex, contain an excessive number of questions, and require a significant amount of time to administer, leading to low participation and completion rates. Additionally, the results obtained from these tools must be analyzed and interpreted manually by mental health professionals, which may yield inaccurate diagnoses. To this extent, this research utilizes advanced analytics and artificial intelligence to develop a decision support system (DSS) that can efficiently detect and diagnose various mental disorders. As part of the DSS development process, the Network Pattern Recognition (NEPAR) algorithm is first utilized to build the assessment tool and identify the questions that participants need to answer. Then, various machine learning models are trained using participants' answers to these questions and other historical data as inputs to predict the existence and the type of their mental disorder. The results show that the proposed DSS can automatically diagnose mental disorders using only 28 questions without any human input, to an accuracy level of 89%. Furthermore, the proposed mental disorder diagnostic tool has significantly fewer questions than its counterparts; hence, it provides higher participation and completion rates. Therefore, mental health professionals can use this proposed DSS and its accompanying assessment tool for improved clinical decision-making and diagnostic accuracy.

Analysis of critical success factors for blockchain technology implementation in healthcare sector

Purpose

Recently, blockchain technology (BT) has resolved healthcare data management challenges. It helps healthcare providers automate medical records and mining to aid in data sharing and making more accurate diagnoses. This paper attempts to identify the critical success factors (CSFs) for successfully implementing BT in healthcare.

Design/methodology/approach

The paper is methodologically structured in four phases. The first phase leads to identifying success factors by reviewing the extant literature. In the second phase, expert opinions were solicited to authenticate the critical success factors required to implement BT in the healthcare sector. Decision Making Trial and Evaluation Laboratory (DEMATEL) method was employed to find the cause-and-effect relationship among the third phase’s critical success factors. In phase 4, the authors resort to validating the final results and findings.

Findings

Based on the analysis, 21 CSFs were identified and grouped under six dimensions. After applying the DEMATEL technique, nine factors belong to the causal group, and the remaining 12 factors fall under the effect group. The top three influencing factors of blockchain technology implementation in the healthcare ecosystem are data transparency, track and traceability and government support, whereas; implementation cost was the least influential.

Originality/value

This study provides a roadmap and may facilitate healthcare professionals to overcome contemporary challenges with the help of BT.

Ethical Issues in AI-Enabled Disease Surveillance: Perspectives from Global Health

Infectious diseases, as COVID-19 is proving, pose a global health threat in an interconnected world. In the last 20 years, resistant infectious diseases such as severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), H1N1 influenza (swine flu), Ebola virus, Zika virus, and now COVID-19 have been impacting global health defences, and aggressively flourishing with the rise of global travel, urbanization, climate change, and ecological degradation. In parallel, this extraordinary episode in global human health highlights the potential for artificial intelligence (AI)-enabled disease surveillance to collect and analyse vast amounts of unstructured and real-time data to inform epidemiological and public health emergency responses. The uses of AI in these dynamic environments are increasingly complex, challenging the potential for human autonomous decisions. In this context, our study of qualitative perspectives will consider a responsible AI framework to explore its potential application to disease surveillance in a global health context. Thus far, there is a gap in the literature in considering these multiple and interconnected levels of disease surveillance and emergency health management through the lens of a responsible AI framework.

Digitalization in Food Supply Chains: A Bibliometric Review and Key-Route Main Path Analysis

Technological advances such as blockchain, artificial intelligence, big data, social media, and geographic information systems represent a building block of the digital transformation that supports the resilience of the food supply chain (FSC) and increases its efficiency. This paper reviews the literature surrounding digitalization in FSCs. A bibliometric and key-route main path analysis was carried out to objectively and analytically uncover the knowledge development in digitalization within the context of sustainable FSCs. The research began with the selection of 2140 articles published over nearly five decades. Then, the articles were examined according to several bibliometric metrics such as year of publication, countries, institutions, sources, authors, and keywords frequency. A keyword co-occurrence network was generated to cluster the relevant literature. Findings of the review and bibliometric analysis indicate that research at the intersection of technology and the FSC has gained substantial interest from scholars. On the basis of keyword co-occurrence network, the literature is focused on the role of information communication technology for agriculture and food security, food waste and circular economy, and the merge of the Internet of Things and blockchain in the FSC. The analysis of the key-route main path uncovers three critical periods marking the development of technology-enabled FSCs. The study offers scholars a better understanding of digitalization within the agri-food industry and the current knowledge gaps for future research. Practitioners may find the review useful to remain ahead of the latest discussions of technology-enabled FSCs. To the authors’ best knowledge, the current study is one of the few endeavors to explore technology-enabled FSCs using a comprehensive sample of journal articles published during the past five decades.

Wireless Sensor Networks in Agriculture: Insights from Bibliometric Analysis

This study investigates how wireless sensor network (WSN) applications in agriculture are discussed in the current academic literature. On the basis of bibliometric techniques, 2444 publications were extracted from the Scopus database and analyzed to identify the temporal distribution of WSN research, the most productive journals, the most cited authors, the most influential studies, and the most relevant keywords. The computer program VOSviewer was used to generate the keyword co-occurrence network and partition the pertinent literature. Findings show the remarkable growth of WSN research in recent years. The most relevant journals, cited countries, and influential studies were also identified. The main results from the keyword co-occurrence clustering and the detailed analysis illustrate that WSN is a key enabler for precision agriculture. WSN research also focuses on the role of other technologies such as the Internet of Things, cloud computing, artificial intelligence, and unmanned aerial vehicles in supporting several agriculture activities, including smart irrigation and soil management. This study illuminates researchers’ and practitioners’ views of what has been researched and identifies possible opportunities for future studies. To the authors’ best knowledge, this bibliometric study represents the first attempt to map global WSN research using a comprehensive sample of documents published over nearly three decades.

The Application of the Principles of Responsible AI on Social Media Marketing for Digital Health

Social media enables medical professionals and authorities to share, disseminate, monitor, and manage health-related information digitally through online communities such as Twitter and Facebook. Simultaneously, artificial intelligence (AI) powered social media offers digital capabilities for organizations to select, screen, detect and predict problems with possible solutions through digital health data. Both the patients and healthcare professionals have benefited from such improvements. However, arising ethical concerns related to the use of AI raised by stakeholders need scrutiny which could help organizations obtain trust, minimize privacy invasion, and eventually facilitate the responsible success of AI-enabled social media operations. This paper examines the impact of responsible AI on businesses using insights from analysis of 25 in-depth interviews of health care professionals. The exploratory analysis conducted revealed that abiding by the responsible AI principles can allow healthcare businesses to better take advantage of the improved effectiveness of their social media marketing initiatives with their users. The analysis is further used to offer research propositions and conclusions, and the contributions and limitations of the study have been discussed.