Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy

Who are the publics engaging in AI?

Given the importance of public engagement in governments' adoption of artificial intelligence systems, artificial intelligence researchers and practitioners spend little time reflecting on who those publics are. Classifying publics affects assumptions and affordances attributed to the publics' ability to contribute to policy or knowledge production. Further complicating definitions are the publics' role in artificial intelligence production and optimization. Our structured analysis of the corpus used a mixed method, where algorithmic generation of search terms allowed us to examine approximately 2500 articles and provided the foundation to conduct an extensive systematic literature review of approximately 100 documents. Results show the multiplicity of ways publics are framed, by examining and revealing the different semantic nuances, affordances, political and expertise lenses, and, finally, a lack of definitions. We conclude that categorizing publics represents an act of power, politics, and truth-seeking in artificial intelligence.

Evaluating the readiness of healthcare administration students to utilize AI for sustainable leadership: a survey study

PURPOSE

This paper explores how healthcare administration students perceive the integration of Artificial Intelligence (AI) in healthcare leadership, mainly focusing on the sustainability aspects involved. It aims to identify gaps in current educational curricula and suggests enhancements to better prepare future healthcare professionals for the evolving demands of AI-driven healthcare environments.

DESIGN/METHODOLOGY/APPROACH

This study utilized a cross-sectional survey design to understand healthcare administration students' perceptions regarding integrating AI in healthcare leadership. An online questionnaire, developed from an extensive literature review covering fundamental AI knowledge and its role in sustainable leadership, was distributed to students majoring and minoring in healthcare administration. This methodological approach garnered participation from 62 students, providing insights and perspectives crucial for the study's objectives.

FINDINGS

The research revealed that while a significant majority of healthcare administration students (70%) recognize the potential of AI in fostering sustainable leadership in healthcare, only 30% feel adequately prepared to work in AI-integrated environments. Additionally, students were interested in learning more about AI applications in healthcare and the role of AI in sustainable leadership, underscoring the need for comprehensive AI-focused education in their curriculum.

RESEARCH LIMITATIONS/IMPLICATIONS

The research is limited by its focus on a single academic institution, which may not fully represent the diversity of perspectives in healthcare administration.

PRACTICAL IMPLICATIONS

This study highlights the need for healthcare administration curricula to incorporate AI education, aligning theoretical knowledge with practical applications, to effectively prepare future professionals for the evolving demands of AI-integrated healthcare environments.

ORIGINALITY/VALUE

This research paper presents insights into healthcare administration students' readiness and perspectives toward AI integration in healthcare leadership, filling a critical gap in understanding the educational needs in the evolving landscape of AI-driven healthcare.

Assessment of Saudi Public Perceptions and Opinions towards Artificial Intelligence in Health Care

Background and Objectives: The healthcare system in Saudi Arabia is growing rapidly with the utilization of advanced technologies. Therefore, this study aimed to assess the Saudi public perceptions and opinions towards artificial intelligence (AI) in health care. Materials and Methods: This cross-sectional web-based questionnaire study was conducted between January and April 2024. Data were analyzed from 830 participants. The perceptions of the public towards AI were assessed using 21-item questionnaires. Results: Among the respondents, 69.4% were males and 46% of them were aged above 41 years old. A total of 84.1% of the participants knew about AI, while 61.1% of them believed that AI is a tool that helps healthcare professionals, and 12.5% of them thought that AI may replace the physician, pharmacist, or nurse in the healthcare system. With regard to opinion on the widespread use of AI, 45.8% of the study population believed that healthcare professionals will be improved with the widespread use of artificial intelligence. The mean perception score of AI among males was 38.4 (SD = 6.1) and this was found to be higher than for females at 37.7 (SD = 5.3); however, no significant difference was observed (p = 0.072). Similarly, the mean perception score was higher among young adults aged between 20 and 25 years at 38.9 (SD = 6.1) compared to other age groups, but indicating no significant association between them (p = 0.198). Conclusions: The results showed that the Saudi public had a favorable opinion and perceptions of AI in health care. This suggests that health management recommendations should be made regarding how to successfully integrate and use medical AI while maintaining patient safety.

The effects of artificial intelligence on human resource activities and the roles of the human resource triad: opportunities and challenges

Introduction

This study analyzes the existing academic literature to identify the effects of artificial intelligence (AI) on human resource (HR) activities, highlighting both opportunities and associated challenges, and on the roles of employees, line managers, and HR professionals, collectively referred to as the HR triad.

Methods

We employed the scoping review method to capture and synthesize relevant academic literature in the AI-human resource management (HRM) field, examining 27 years of research (43 peer-reviewed articles are included).

Results

Based on the results, we propose an integrative framework that outlines the five primary effects of AI on HR activities: task automation, optimized HR data use, augmentation of human capabilities, work context redesign, and transformation of the social and relational aspects of work. We also detail the opportunities and challenges associated with each of these effects and the changes in the roles of the HR triad.

Discussion

This research contributes to the ongoing debate on AI-augmented HRM by discussing the theoretical contributions and managerial implications of our findings, along with avenues for future research. By considering the most recent studies on the topic, this scoping review sheds light on the effects of AI on the roles of the HR triad, enabling these key stakeholders to better prepare for this technological change. The findings can inform future academic research, organizations using or considering the application of AI in HRM, and policymakers. This is particularly timely, given the growing adoption of AI in HRM activities.

A Theoretical Exploration of Artificial Intelligence's Impact on Feto-Maternal Health from Conception to Delivery

The implementation of Artificial Intelligence (AI) in healthcare is enhancing diagnostic accuracy in clinical setups. The use of AI in healthcare is steadily increasing with advancing technology, extending beyond disease diagnosis to encompass roles in feto-maternal health. AI harnesses Machine Learning (ML), Natural Language Processing (NLP), Artificial Neural Networks (ANN), and computer vision to analyze data and draw conclusions. Considering maternal health, ML analyzes vast datasets to predict maternal and fetal health outcomes, while NLP interprets medical texts and patient records to assist in diagnosis and treatment decisions. ANN models identify patterns in complex feto-maternal medical data, aiding in risk assessment and intervention planning whereas, computer vision enables the analysis of medical images for early detection of feto-maternal complications. AI facilitates early pregnancy detection, genetic screening, and continuous monitoring of maternal health parameters, providing real-time alerts for deviations, while also playing a crucial role in the early detection of fetal abnormalities through enhanced ultrasound imaging, contributing to informed decision-making. This review investigates into the application of AI, particularly through predictive models, in addressing the monitoring of feto-maternal health. Additionally, it examines potential future directions and challenges associated with these applications.

Organoid bioinks: construction and application

Organoids have emerged as crucial platforms in tissue engineering and regenerative medicine but confront challenges in faithfully mimicking native tissue structures and functions. Bioprinting technologies offer a significant advancement, especially when combined with organoid bioinks-engineered formulations designed to encapsulate both the architectural and functional elements of specific tissues. This review provides a rigorous, focused examination of the evolution and impact of organoid bioprinting. It emphasizes the role of organoid bioinks that integrate key cellular components and microenvironmental cues to more accurately replicate native tissue complexity. Furthermore, this review anticipates a transformative landscape invigorated by the integration of artificial intelligence with bioprinting techniques. Such fusion promises to refine organoid bioink formulations and optimize bioprinting parameters, thus catalyzing unprecedented advancements in regenerative medicine. In summary, this review accentuates the pivotal role and transformative potential of organoid bioinks and bioprinting in advancing regenerative therapies, deepening our understanding of organ development, and clarifying disease mechanisms.

Innovative governance for transformative energy policy in sub-Saharan Africa after COVID-19: Green pathways in Egypt, Nigeria, and South Africa

Learning from innovations that fail is imperative for innovations that succeed. The theoretical underpinnings for this innovative framing are reflexivity, transformative unlearning, and intelligent failure. This framework proposes a definition of "transformative governance" as governance that creates structural equities. Governments rebuilding their economies after the COVID-19 pandemic seek equitable green transformations; that are gendered, structural, and sustainable, learning from the implemented gender-sensitive responses (hereafter referred to as policy innovations). This paper argues that transformative practices, beliefs, values, assumptions, policies, and systematic learnings are complementary to post-crisis transformations. The aim is to promote systematic learnings from innovation governance failure regarding energy policy through the analysis of COVID-19 practices and the unlearning of policy innovation beliefs, values, and assumptions that are not transformative. I ask: how gender-equitable, structurally equitable, and green-transformative were the COVID-19 policy innovations? The study's approach is qualitative and situated within the constructivist research paradigm. It uses reflexive thematic analysis combined with innovative coded policy narrative and a transformative index-matching technique, to identify the gap within transformative interventions. The study included 58 policy innovations (n = 58) collected from the UNDP, KPMG, government reports, and news flashes from the three most populous nations in sub-Sahara Africa: Egypt, Nigeria, and South Africa. The study found that policy innovations were inequitable in terms of gender, structure, and sustainability whereas the derived transformative pathways are equitable and gender-transformative, structurally transformative, and green-transformative. The rationales behind a transformative approach to policy reflect the systemic failures across key areas: market dynamics, research and development, and green transformation. Policy innovators can align transformative pathways for innovative governance that implements transformative energy policy. To address the needs of multiple fragile and vulnerable identities, the derived post-pandemic framework is an intersectional plan with 10 policy learning pillars. The plan includes local energy transformation and reinforcement of energy justice components, such as the localization of the energy industry, community power, and social norms, including Ubuntu, which translates to "I am because we are." Reengagement in global supply chains requires South-South trade relations to be restrategized.

Using Artificial Intelligence to Label Free-Text Operative and Ultrasound Reports for Grading Pediatric Appendicitis

PURPOSE

Data science approaches personalizing pediatric appendicitis management are hampered by small datasets and unstructured electronic medical records (EMR). Artificial intelligence (AI) chatbots based on large language models can structure free-text EMR data. We compare data extraction quality between ChatGPT-4 and human data collectors.

METHODS

To train AI models to grade pediatric appendicitis preoperatively, several data collectors extracted detailed preoperative and operative data from 2100 children operated for acute appendicitis. Collectors were trained for the task based on satisfactory Kappa scores. ChatGPT-4 was prompted to structure free text from 103 random anonymized ultrasound and operative records in the dataset using the set variables and coding options, and to estimate appendicitis severity grade from the operative report. A pediatric surgeon then adjudicated all data, identifying errors in each method.

RESULTS

Within the 44 ultrasound (42.7%) and 32 operative reports (31.1%) discordant in at least one field, 98% of the errors were found in the manual data extraction. The appendicitis grade was erroneously assigned manually in 29 patients (28.2%), and by ChatGPT-4 in 3 (2.9%). Across datasets, the use of the AI chatbot was able to avoid misclassification in 59.2% of the records including both reports and extracted data approximately 40 times faster.

CONCLUSION

AI chatbot significantly outperformed manual data extraction in accuracy for ultrasound and operative reports, and correctly assigned the appendicitis grade. While wider validation is required and data safety concerns must be addressed, these AI tools show significant promise in improving the accuracy and efficiency of research data collection.

LEVELS OF EVIDENCE

Level III.

AI can empower agriculture for global food security: challenges and prospects in developing nations

Food and nutrition are a steadfast essential to all living organisms. With specific reference to humans, the sufficient and efficient supply of food is a challenge as the world population continues to grow. Artificial Intelligence (AI) could be identified as a plausible technology in this 5th industrial revolution in bringing us closer to achieving zero hunger by 2030-Goal 2 of the United Nations Sustainable Development Goals (UNSDG). This goal cannot be achieved unless the digital divide among developed and underdeveloped countries is addressed. Nevertheless, developing and underdeveloped regions fall behind in economic resources; however, they harbor untapped potential to effectively address the impending demands posed by the soaring world population. Therefore, this study explores the in-depth potential of AI in the agriculture sector for developing and under-developed countries. Similarly, it aims to emphasize the proven efficiency and spin-off applications of AI in the advancement of agriculture. Currently, AI is being utilized in various spheres of agriculture, including but not limited to crop surveillance, irrigation management, disease identification, fertilization practices, task automation, image manipulation, data processing, yield forecasting, supply chain optimization, implementation of decision support system (DSS), weed control, and the enhancement of resource utilization. Whereas AI supports food safety and security by ensuring higher crop yields that are acquired by harnessing the potential of multi-temporal remote sensing (RS) techniques to accurately discern diverse crop phenotypes, monitor land cover dynamics, assess variations in soil organic matter, predict soil moisture levels, conduct plant biomass modeling, and enable comprehensive crop monitoring. The present study identifies various challenges, including financial, infrastructure, experts, data availability, customization, regulatory framework, cultural norms and attitudes, access to market, and interdisciplinary collaboration, in the adoption of AI for developing nations with their subsequent remedies. The identification of challenges and opportunities in the implementation of AI could ignite further research and actions in these regions; thereby supporting sustainable development.

Unpacking public resistance to health Chatbots: a parallel mediation analysis

Introduction

Despite the numerous potential benefits of health chatbots for personal health management, a substantial proportion of people oppose the use of such software applications. Building on the innovation resistance theory (IRT) and the prototype willingness model (PWM), this study investigated the functional barriers, psychological barriers, and negative prototype perception antecedents of individuals' resistance to health chatbots, as well as the rational and irrational psychological mechanisms underlying their linkages.

Methods

Data from 398 participants were used to construct a partial least squares structural equation model (PLS-SEM).

Results

Resistance intention mediated the relationship between functional barriers, psychological barriers, and resistance behavioral tendency, respectively. Furthermore, The relationship between negative prototype perceptions and resistance behavioral tendency was mediated by resistance intention and resistance willingness. Moreover, negative prototype perceptions were a more effective predictor of resistance behavioral tendency through resistance willingness than functional and psychological barriers.

Discussion

By investigating the role of irrational factors in health chatbot resistance, this study expands the scope of the IRT to explain the psychological mechanisms underlying individuals' resistance to health chatbots. Interventions to address people's resistance to health chatbots are discussed.

Enhancing portfolio management using artificial intelligence: literature review

Building an investment portfolio is a problem that numerous researchers have addressed for many years. The key goal has always been to balance risk and reward by optimally allocating assets such as stocks, bonds, and cash. In general, the portfolio management process is based on three steps: planning, execution, and feedback, each of which has its objectives and methods to be employed. Starting from Markowitz's mean-variance portfolio theory, different frameworks have been widely accepted, which considerably renewed how asset allocation is being solved. Recent advances in artificial intelligence provide methodological and technological capabilities to solve highly complex problems, and investment portfolio is no exception. For this reason, the paper reviews the current state-of-the-art approaches by answering the core question of how artificial intelligence is transforming portfolio management steps. Moreover, as the use of artificial intelligence in finance is challenged by transparency, fairness and explainability requirements, the case study of post-hoc explanations for asset allocation is demonstrated. Finally, we discuss recent regulatory developments in the European investment business and highlight specific aspects of this business where explainable artificial intelligence could advance transparency of the investment process.

AI energized hydrogel design, optimization and application in biomedicine

Traditional hydrogel design and optimization methods usually rely on repeated experiments, which is time-consuming and expensive, resulting in a slow-moving of advanced hydrogel development. With the rapid development of artificial intelligence (AI) technology and increasing material data, AI-energized design and optimization of hydrogels for biomedical applications has emerged as a revolutionary breakthrough in materials science. This review begins by outlining the history of AI and the potential advantages of using AI in the design and optimization of hydrogels, such as prediction and optimization of properties, multi-attribute optimization, high-throughput screening, automated material discovery, optimizing experimental design, and etc. Then, we focus on the various applications of hydrogels supported by AI technology in biomedicine, including drug delivery, bio-inks for advanced manufacturing, tissue repair, and biosensors, so as to provide a clear and comprehensive understanding of researchers in this field. Finally, we discuss the future directions and prospects, and provide a new perspective for the research and development of novel hydrogel materials for biomedical applications.

Multidisciplinary collaboration on exoskeleton development adopting user-centered design: a systematic integrative review

Purpose: The world population is ageing, along with an increasing possibility of functional limitations that affect independent living. Assistive technologies such as exoskeletons for rehabilitative purposes and daily activities assistance maintaining the independence of people with disabilities, especially older adults who wish to ageing-in-place. The purpose of this systematic integrative review was threefold: to explore the development team compositions and involvement, to understand the recruitment and engagement of stakeholders, and to synthesise reported or anticipated consequences of multidisciplinary collaboration.Methods: Databases searched included PubMed, CINAHL Plus, PsycINFO, Web of Science, Scopus, and IEEE Xplore. A total of 34 studies that reported the development of exoskeleton adopting user-centered design (UCD) method in healthcare or community settings that were published in English from 2000 to July 2022 were included.Results: Three major trends emerged from the analysis of included studies. First, there is a need to redefine multidisciplinary collaboration, from within-discipline collaboration to cross-discipline collaboration. Second, the level of engagement of stakeholders during the exoskeleton development remained low. Third, there was no standardised measurement to quantify knowledge production currently.Conclusion: As suggested by the synthesised results in this review, exoskeleton development has been increasing to improve the functioning of people with disabilities. Exoskeleton development often required expertise from different disciplines and the involvement of stakeholders to increase acceptance, thus we propose the Multidisciplinary Collaboration Appraisal Tool to assess multidisciplinary collaboration using the UCD approach. Future research is required to understand the effectiveness of multidisciplinary collaboration on exoskeleton development using the UCD approach.IMPLICATIONS FOR REHABILITATIONGlobal trend of population ageing causes a higher risk of disability in older adults who require rehabilitation and assistance in daily living.Assistive technologies such as exoskeletons have the potential to contribute to rehabilitation training and daily activity assistance demand closer multidisciplinary collaboration.A Multidisciplinary Collaboration Appraisal Tool using user-centered design approach (MCAT) is proposed to understand the effectiveness as well as limitations and barriers associated with multidisciplinary collaboration in developing exoskeletons.

The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century

As healthcare systems around the world face challenges such as escalating costs, limited access, and growing demand for personalized care, artificial intelligence (AI) is emerging as a key force for transformation. This review is motivated by the urgent need to harness AI's potential to mitigate these issues and aims to critically assess AI's integration in different healthcare domains. We explore how AI empowers clinical decision-making, optimizes hospital operation and management, refines medical image analysis, and revolutionizes patient care and monitoring through AI-powered wearables. Through several case studies, we review how AI has transformed specific healthcare domains and discuss the remaining challenges and possible solutions. Additionally, we will discuss methodologies for assessing AI healthcare solutions, ethical challenges of AI deployment, and the importance of data privacy and bias mitigation for responsible technology use. By presenting a critical assessment of AI's transformative potential, this review equips researchers with a deeper understanding of AI's current and future impact on healthcare. It encourages an interdisciplinary dialogue between researchers, clinicians, and technologists to navigate the complexities of AI implementation, fostering the development of AI-driven solutions that prioritize ethical standards, equity, and a patient-centered approach.

A Comprehensive Evaluation of AI-Assisted Diagnostic Tools in ENT Medicine: Insights and Perspectives from Healthcare Professionals

The integration of Artificial Intelligence (AI) into healthcare has the potential to revolutionize medical diagnostics, particularly in specialized fields such as Ear, Nose, and Throat (ENT) medicine. However, the successful adoption of AI-assisted diagnostic tools in ENT practice depends on the understanding of various factors; these include influences on their effectiveness and acceptance among healthcare professionals. This cross-sectional study aimed to assess the usability and integration of AI tools in ENT practice, determine the clinical impact and accuracy of AI-assisted diagnostics in ENT, measure the trust and confidence of ENT professionals in AI tools, gauge the overall satisfaction and outlook on the future of AI in ENT diagnostics, and identify challenges, limitations, and areas for improvement in AI-assisted ENT diagnostics. A structured online questionnaire was distributed to 600 certified ENT professionals with at least one year of experience in the field. The questionnaire assessed participants' familiarity with AI tools, usability, clinical impact, trust, satisfaction, and identified challenges. A total of 458 respondents completed the questionnaire, resulting in a response rate of 91.7%. The majority of respondents reported familiarity with AI tools (60.7%) and perceived them as generally usable and clinically impactful. However, challenges such as integration with existing systems, user-friendliness, accuracy, and cost were identified. Trust and satisfaction levels varied among participants, with concerns regarding data privacy and support. Geographic and practice setting differences influenced perceptions and experiences. The study highlights the diverse perceptions and experiences of ENT professionals regarding AI-assisted diagnostics. While there is general enthusiasm for these tools, challenges related to integration, usability, trust, and cost need to be addressed for their widespread adoption. These findings provide valuable insights for developers, policymakers, and healthcare providers aiming to enhance the role of AI in ENT practice.

Trends in publication impact of evidence-based healthcare terminology (2013-2022)

AIMS

This article explored the publication impact of evidence-based healthcare terminology to determine usage and discuss options for low usage terms.

BACKGROUND

A plethora of terms describe the scholarship of evidence-based healthcare. Several terms are synonyms, creating redundancy and confusion. The abundance and overlap of terms may impede the discovery of evidence.

DESIGN

This discursive article explored and discussed publication impact of evidence-based healthcare terms.

METHODS

Evidence-based healthcare terms were identified, and their 10-year (2013-2022) publication impact was assessed in the CINAHL and Medline databases. A card sort method was also used to identify terms with low usage.

RESULTS

A total of 18/32 terms were included in the review. The terms evidence-based practice, quality improvement, research and translational research were the most highly published terms. Publication data were presented yearly over a 10-year period. Most terms increased in publication use over time, except for three terms whose use decreased. Several terms related to translational research have multiple synonyms. It remains unknown whether these terms are interchangeable and possibly redundant, or if there are nuanced differences between terms.

CONCLUSION

We suggest a follow-up review in 3-5 years to identify publication trends to assess context and terms with continued low publication usage. Terms with persistent low usage should be considered for retirement in the reporting of scholarly activities. Additionally, terms with increasing publication trends should be treated as emerging terms that contribute to evidence-based healthcare terminology.

IMPLICATIONS FOR NURSING

Confusion about the use of appropriate terminology may hinder progress in the scholarship of evidence-based healthcare. We encourage scholars to be aware of publication impact as it relates to the use of specific terminology and be purposeful in the selection of terms used in scholarly projects and publications.

Artificial intelligence in future nursing care: Exploring perspectives of nursing professionals - A descriptive qualitative study

Background

The healthcare landscape is rapidly evolving, with artificial intelligence (AI) emerging as a transformative force. In this context, understanding the viewpoints of nursing professionals regarding the integration of AI in future nursing care is crucial.

Aims

This study aimed to provide insights into the perceptions of nursing professionals regarding the role of AI in shaping the future of healthcare.

Methods

A cohort of 23 nursing professionals was recruited between April 7, 2023, and May 4, 2023, for this study. Employing a thematic analysis approach, qualitative data from interviews with nursing professionals were analyzed. Verbatim transcripts underwent rigorous coding, and these codes were organized into themes through constant comparative analysis. The themes were refined and developed through the grouping of related codes, ensuring an authentic representation of participants' viewpoints.

Results

After careful data analysis, ten key themes emerged including: (I) Perceptions of AI readiness; (II) Benefits and concerns; (III) Enhanced patient outcomes; (IV) Collaboration and workflow; (V) Human-tech balance: (VI) Training and skill development; (VII) Ethical and legal considerations; (VIII) AI implementation barriers; (IX) Patient-nurse relationships; (X) Future vision and adaptation.

Conclusion

This study provides valuable insights into nursing professionals' perspectives on the integration of AI in future nursing care. It highlights their enthusiasm for AI's potential benefits while emphasizing the importance of ethical and compassionate nursing practice. The findings underscore the need for comprehensive training programs to equip nursing professionals with the skills necessary for successful AI integration. Ultimately, this research contributes to the ongoing discourse on the role of AI in nursing, paving the way for a future where innovative technologies complement and enhance the delivery of patient-centered care.

IUPHAR Review: New strategies for medications to treat substance use disorders

Substance use disorders (SUDs) and drug overdose are a public health emergency and safe and effective treatments are urgently needed. Developing new medications to treat them is expensive, time-consuming, and the probability of a compound progressing to clinical trials and obtaining FDA-approval is low. The small number of FDA-approved medications for SUDs reflects the low interest of pharmaceutical companies to invest in this area due to market forces, characteristics of the population (e.g., stigma, and socio-economic and legal disadvantages), and the high bar regulatory agencies set for new medication approval. In consequence, most research on medications is funded by government agencies, such as the National Institute on Drug Abuse (NIDA). Multiple scientific opportunities are emerging that can accelerate the discovery and development of new medications for SUDs. These include fast and efficient tools to screen new molecules, discover new medication targets, use of big data to explore large clinical data sets and artificial intelligence (AI) applications to make predictions, and precision medicine tools to individualize and optimize treatments. This review provides a general description of these new research strategies for the development of medications to treat SUDs with emphasis on the gaps and scientific opportunities. It includes a brief overview of the rising public health toll of SUDs; the justification, challenges, and opportunities to develop new medications; and a discussion of medications and treatment endpoints that are being evaluated with support from NIDA.

Tooth morphology, internal fit, occlusion and proximal contacts of dental crowns designed by deep learning-based dental software: A comparative study

OBJECTIVES

This study compared the tooth morphology, internal fit, occlusion, and proximal contacts of dental crowns automatically generated via two deep learning (DL)-based dental software systems with those manually designed by an experienced dental technician using conventional software.

METHODS

Thirty partial arch scans of prepared posterior teeth were used. The crowns were designed using two DL-based methods (AA and AD) and a technician-based method (NC). The crown design outcomes were three-dimensionally compared, focusing on tooth morphology, internal fit, occlusion, and proximal contacts, by calculating the geometric relationship. Statistical analysis utilized the independent t-test, Mann-Whitney test, one-way ANOVA, and Kruskal-Wallis test with post hoc pairwise comparisons (α = 0.05).

RESULTS

The AA and AD groups, with the NC group as a reference, exhibited no significant tooth morphology discrepancies across entire external or occlusal surfaces. The AD group exhibited higher root mean square and positive average values on the axial surface (P < .05). The AD and NC groups exhibited a better internal fit than the AA group (P < .001). The cusp angles were similar across all groups (P = .065). The NC group yielded more occlusal contact points than the AD group (P = .006). Occlusal and proximal contact intensities varied among the groups (both P < .001).

CONCLUSIONS

Crowns designed by using both DL-based software programs exhibited similar morphologies on the occlusal and axial surfaces; however, they differed in internal fit, occlusion, and proximal contacts. Their overall performance was clinically comparable to that of the technician-based method in terms of the internal fit and number of occlusal contact points.

CLINICAL SIGNIFICANCE

DL-based dental software for crown design can streamline the digital workflow in restorative dentistry, ensuring clinically-acceptable outcomes on tooth morphology, internal fit, occlusion, and proximal contacts. It can minimize the necessity of additional design optimization by dental technician.

A comprehensive review on artificial intelligence in water treatment for optimization. Clean water now and the future

Given the severe effects that toxic compounds present in wastewater streams have on humans, it is imperative that water and wastewater streams pollution be addressed globally. This review comprehensively examines various water and wastewater treatment methods and water quality management methods based on artificial intelligence (AI). Machine learning (ML) and AI have become a powerful tool for addressing problems in the real world and has gained a lot of interest since it can be used for a wide range of activities. The foundation of ML techniques involves training of a network with collected data, followed by application of learned network to the process simulation and prediction. The creation of ML models for process simulations requires measured data. In order to forecast and simulate chemical and physical processes such chemical reactions, heat transfer, mass transfer, energy, pharmaceutics and separation, a variety of machine-learning algorithms have recently been developed. These models have shown to be more adept at simulating and modeling processes than traditional models. Although AI offers many advantages, a number of disadvantages have kept these methods from being extensively applied in actual water treatment systems. Lack of evidence of application in actual water treatment scenarios, poor repeatability and data availability and selection are a few of the main problems that need to be resolved.

Revisiting the role of HR in the age of AI: bringing humans and machines closer together in the workplace

The functions of human resource management (HRM) have changed radically in the past 20 years due to market and technological forces, becoming more cross-functional and data-driven. In the age of AI, the role of HRM professionals in organizations continues to evolve. Artificial intelligence (AI) is transforming many HRM functions and practices throughout organizations creating system and process efficiencies, performing advanced data analysis, and contributing to the value creation process of the organization. A growing body of evidence highlights the benefits AI brings to the field of HRM. Despite the increased interest in AI-HRM scholarship, focus on human-AI interaction at work and AI-based technologies for HRM is limited and fragmented. Moreover, the lack of human considerations in HRM tech design and deployment can hamper AI digital transformation efforts. This paper provides a contemporary and forward-looking perspective to the strategic and human-centric role HRM plays within organizations as AI becomes more integrated in the workplace. Spanning three distinct phases of AI-HRM integration (technocratic, integrated, and fully-embedded), it examines the technical, human, and ethical challenges at each phase and provides suggestions on how to overcome them using a human-centric approach. Our paper highlights the importance of the evolving role of HRM in the AI-driven organization and provides a roadmap on how to bring humans and machines closer together in the workplace.

AI and business management: Tracking future research agenda through bibliometric network analysis

This study has been designed to analyse the academic landscape of AI on the Scopus and Web of Science (WOS) indices and compare the findings. AI is one of the most prominent and preferred research areas, only a few studies are dedicated to the bibliometric aspect of it. There is a need to compare studies on AI over different databases to identify the impact and usefulness of those studies in decision-making in business management. To conduct this analysis, the authors have collected data from both Scopus and WOS. 'VOSviewer', 'R-Studio', and 'MS Excel' software have been used for performance analysis and science mapping. This is one of the exceptional studies which perform a comparative analysis between two indices and also identifies funding sponsors for support of research in AI. "Dwivedi, Y.K." is the most productive author and "Huang, Minghui" is the most impactful author. "National Natural Science Foundation of China" is the funding agency which has significantly supported AI research. Technical aspects like "Machine learning", "neural networks", and "blockchain" with 'Sustainability', 'sustainable development', 'accounting', and 'auditing' are trending themes for managerial decision-making.

A longitudinal study on artificial intelligence adoption: understanding the drivers of ChatGPT usage behavior change in higher education

As the field of artificial intelligence (AI) continues to progress, the use of AI-powered chatbots, such as ChatGPT, in higher education settings has gained significant attention. This paper addresses a well-defined problem pertaining to the critical need for a comprehensive examination of students' ChatGPT adoption in higher education. To examine such adoption, it is imperative to focus on measuring actual user behavior. While measuring students' ChatGPT usage behavior at a specific point in time can be valuable, a more holistic approach is necessary to understand the temporal dynamics of AI adoption. To address this need, a longitudinal survey was conducted, examining how students' ChatGPT usage behavior changes over time among students, and unveiling the drivers of such behavior change. The empirical examination of 222 Dutch higher education students revealed a significant decline in students' ChatGPT usage behavior over an 8 month period. This period was defined by two distinct data collection phases: the initial phase (T1) and a follow-up phase conducted 8 months later (T2). Furthermore, the results demonstrate that changes in trust, emotional creepiness, and Perceived Behavioral Control significantly predicted the observed change in usage behavior. The findings of this research carry significant academic and managerial implications, as they advance our comprehension of the temporal aspects of AI adoption in higher education. The findings also provide actionable guidance for AI developers and educational institutions seeking to optimize student engagement with AI technologies.

AI-enabled organoids: Construction, analysis, and application

Organoids, miniature and simplified in vitro model systems that mimic the structure and function of organs, have attracted considerable interest due to their promising applications in disease modeling, drug screening, personalized medicine, and tissue engineering. Despite the substantial success in cultivating physiologically relevant organoids, challenges remain concerning the complexities of their assembly and the difficulties associated with data analysis. The advent of AI-Enabled Organoids, which interfaces with artificial intelligence (AI), holds the potential to revolutionize the field by offering novel insights and methodologies that can expedite the development and clinical application of organoids. This review succinctly delineates the fundamental concepts and mechanisms underlying AI-Enabled Organoids, summarizing the prospective applications on rapid screening of construction strategies, cost-effective extraction of multiscale image features, streamlined analysis of multi-omics data, and precise preclinical evaluation and application. We also explore the challenges and limitations of interfacing organoids with AI, and discuss the future direction of the field. Taken together, the AI-Enabled Organoids hold significant promise for advancing our understanding of organ development and disease progression, ultimately laying the groundwork for clinical application.

Role of Artificial Intelligence in Drug Discovery and Target Identification in Cancer

Drug discovery and development (DDD) is a highly complex process that necessitates precise monitoring and extensive data analysis at each stage. Furthermore, the DDD process is both timeconsuming and costly. To tackle these concerns, artificial intelligence (AI) technology can be used, which facilitates rapid and precise analysis of extensive datasets within a limited timeframe. The pathophysiology of cancer disease is complicated and requires extensive research for novel drug discovery and development. The first stage in the process of drug discovery and development involves identifying targets. Cell structure and molecular functioning are complex due to the vast number of molecules that function constantly, performing various roles. Furthermore, scientists are continually discovering novel cellular mechanisms and molecules, expanding the range of potential targets. Accurately identifying the correct target is a crucial step in the preparation of a treatment strategy. Various forms of AI, such as machine learning, neural-based learning, deep learning, and network-based learning, are currently being utilised in applications, online services, and databases. These technologies facilitate the identification and validation of targets, ultimately contributing to the success of projects. This review focuses on the different types and subcategories of AI databases utilised in the field of drug discovery and target identification for cancer.

Evaluation of the efficiency, trueness, and clinical application of novel artificial intelligence design for dental crown prostheses

OBJECTIVE

The unique structure of human teeth limits dental repair to custom-made solutions. The production process requires a lot of time and manpower. At present, artificial intelligence (AI) has begun to be used in the medical field and improve efficiency. This study attempted to design a variety of dental restorations using AI and evaluate their clinical applicability.

METHODS

Using inlay and crown restoration types commonly used in dental standard models, we compared differences in artificial wax-up carving (wax-up), artificial digital designs (digital) and AI designs (AI). The AI system was designed using computer calculations, and the other two methods were designed by humans. Restorations were made by 3D printing resin material. Image evaluations were compared with cone beam computed tomography (CBCT) by calculating the root mean squared error.

RESULTS

Surface truth results showed that AI (68.4 µm) and digital-designed crowns (51.0 µm) had better reproducibility. Using AI for the crown reduced the time spent by 400% (compared to digital) and 900% (compared to wax-up). Optical microscopic and CBCT images showed that AI and digital designs had close margin gaps (p < 0.05). The margin gap of the crown showed that the wax-up group was 4.1 and 4.3 times greater than those of the AI and digital crowns, respectively. Therefore, the utilization of artificial intelligence can assist in the production of dental restorations, thereby enhancing both production efficiency and accuracy.

SIGNIFICANCE

It is expected that the development of AI can contribute to the reproducibility, efficiency, and goodness of fit of dental restorations.

College Students' Employability, Cognition, and Demands for ChatGPT in the AI Era Among Chinese Nursing Students: Web-Based Survey

BACKGROUND

With the rapid development of artificial intelligence (AI) and the widespread use of ChatGPT, nursing students' artificial intelligence quotient (AIQ), employability, cognition, and demand for ChatGPT are worthy of attention.

OBJECTIVE

We aimed to investigate Chinese nursing students' AIQ and employability status as well as their cognition and demand for the latest AI tool-ChatGPT. This study was conducted to guide future initiatives in nursing intelligence education and to improve the employability of nursing students.

METHODS

We used a cross-sectional survey to understand nursing college students' AIQ, employability, cognition, and demand for ChatGPT. Using correlation analysis and multiple hierarchical regression analysis, we explored the relevant factors in the employability of nursing college students.

RESULTS

In this study, out of 1788 students, 1453 (81.30%) had not used ChatGPT, and 1170 (65.40%) had never heard of ChatGPT before this survey. College students' employability scores were positively correlated with AIQ, self-regulation ability, and their home location and negatively correlated with school level. Additionally, men scored higher on college students' employability compared to women. Furthermore, 76.5% of the variance was explained by the multiple hierarchical regression model for predicting college students' employability scores.

CONCLUSIONS

Chinese nursing students have limited familiarity and experience with ChatGPT, while their AIQ remains intermediate. Thus, educators should pay more attention to cultivating nursing students' AIQ and self-regulation ability to enhance their employability. Employability, especially for female students, those from rural backgrounds, and students in key colleges, deserves more attention in future educational efforts.

Analyzing ChatGPT adoption drivers with the TOEK framework

With the rapid advancements in AI technology and its growing impact on various aspects of daily life, understanding the factors that influence users' adoption intention becomes essential. This study focuses on the determinants affecting the adoption intention of ChatGPT, an AI-driven language model, among university students. The research extends the Technology-Organization-Environment (TOE) framework by integrating the concept of knowledge application. A cross-sectional research design was employed, gathering data through a survey conducted to university students. Structural equation modeling was used to analyze the data, aimed at examining the relationships between key determinants influencing adoption intention. The findings of this research indicate that factors such as network quality, accessibility, and system responsiveness contribute to satisfaction. Furthermore, satisfaction, organizational culture, social influence, and knowledge application significantly affect adoption intention. These findings offer both theoretical and practical implications.

Intelligent selection of healthcare supply chain mode - an applied research based on artificial intelligence

Introduction

Due to the inefficiency and high cost of the current healthcare supply chain mode, in order to adapt to the great changes in the global economy and public health, it is urgent to choose an effective mode for sustainable development of healthcare supply chain. The aim of this paper is to use artificial intelligence systems to make intelligent decisions for healthcare supply chain mode selection.

Methods

Firstly, according to the economic benefits, social benefits and environmental benefits of healthcare supply chain, this paper identifies different healthcare supply chain modes in combination with artificial intelligence technology. Secondly, this paper presents the intelligent choice optimization method of healthcare supply chain mode based on deep reinforcement learning algorithm. Finally, the effect of artificial intelligence in healthcare supply chain mode selection is verified by simulation experiment.

Results and Discussion

The experimental results show that healthcare supply chain mode selected by artificial intelligence is basically consistent with the target mode, while healthcare supply chain mode selected by the basic selection method, BP neural network method and big data method is different from the target mode, which indicates that AI has more advantages in the selection of medical supply chain mode. Therefore, we recommend the application of artificial intelligence to healthcare supply chain management. This study not only makes up for the ineffective problems of existing methods, but also makes up for the gaps in the application of AI technology in the field of healthcare supply chain. The scientific value of this paper is that the proposed framework and the artificial intelligence algorithm enrich the relevant theories of healthcare supply chain research and provide methodological guidance for intelligent decision-making of healthcare supply chain. At the same time, for medical enterprises, this research provides a new practical guideline for the application of artificial intelligence in the sustainable development and modern management of healthcare supply chain.

Improving Social Bot Detection Through Aid and Training

OBJECTIVE

We test the effects of three aids on individuals' ability to detect social bots among Twitter personas: a bot indicator score, a training video, and a warning.

BACKGROUND

Detecting social bots can prevent online deception. We use a simulated social media task to evaluate three aids.

METHOD

Lay participants judged whether each of 60 Twitter personas was a human or social bot in a simulated online environment, using agreement between three machine learning algorithms to estimate the probability of each persona being a bot. Experiment 1 compared a control group and two intervention groups, one provided a bot indicator score for each tweet; the other provided a warning about social bots. Experiment 2 compared a control group and two intervention groups, one receiving the bot indicator scores and the other a training video, focused on heuristics for identifying social bots.

RESULTS

The bot indicator score intervention improved predictive performance and reduced overconfidence in both experiments. The training video was also effective, although somewhat less so. The warning had no effect. Participants rarely reported willingness to share content for a persona that they labeled as a bot, even when they agreed with it.

CONCLUSIONS

Informative interventions improved social bot detection; warning alone did not.

APPLICATION

We offer an experimental testbed and methodology that can be used to evaluate and refine interventions designed to reduce vulnerability to social bots. We show the value of two interventions that could be applied in many settings.

Investigating the impact of artificial intelligence on human resource functions in the health sector of China: A mediated moderation model

Artificial intelligence (AI) is rapidly transforming the way human resources (HR) functions are carried out in the health sector of China. This study aims to scrutinize the impact of artificial intelligence on the human resource functions operating in the healthcare sector through technological awareness, social media influence, and personal innovativeness. Additionally, this study examines the moderating role of perceived risk between technological awareness and human resources functions. An online questionnaire was administered to human resources professionals in the health sector of China to gather data from 363 respondents. Partial least squares structural equation modeling (PLS-SEM), a statistical procedure, is implemented to investigate the hypothesis of the projected model of artificial intelligence and human resource functions. The research findings reveal that artificial intelligence significantly influences human resource functions through technological awareness, social media influence, and personal innovativeness. Furthermore, perceived risk significantly moderates the relationship between technological awareness and human resource functions. The findings of this study have important implications for HR practitioners and policymakers in the health sectors of China, who can leverage artificial intelligence technologies to optimize and improve organizational performance. However, its adoption needs to be carefully planned and managed to reap the full benefits of this transformative technology.

An Integrated Multi-Omics and Artificial Intelligence Framework for Advance Plant Phenotyping in Horticulture

This review discusses the transformative potential of integrating multi-omics data and artificial intelligence (AI) in advancing horticultural research, specifically plant phenotyping. The traditional methods of plant phenotyping, while valuable, are limited in their ability to capture the complexity of plant biology. The advent of (meta-)genomics, (meta-)transcriptomics, proteomics, and metabolomics has provided an opportunity for a more comprehensive analysis. AI and machine learning (ML) techniques can effectively handle the complexity and volume of multi-omics data, providing meaningful interpretations and predictions. Reflecting the multidisciplinary nature of this area of research, in this review, readers will find a collection of state-of-the-art solutions that are key to the integration of multi-omics data and AI for phenotyping experiments in horticulture, including experimental design considerations with several technical and non-technical challenges, which are discussed along with potential solutions. The future prospects of this integration include precision horticulture, predictive breeding, improved disease and stress response management, sustainable crop management, and exploration of plant biodiversity. The integration of multi-omics and AI holds immense promise for revolutionizing horticultural research and applications, heralding a new era in plant phenotyping.

HDSNE a new unsupervised multiple image database fusion learning algorithm with flexible and crispy production of one database: a proof case study of lung infection diagnose In chest X-ray images

Continuous release of image databases with fully or partially identical inner categories dramatically deteriorates the production of autonomous Computer-Aided Diagnostics (CAD) systems for true comprehensive medical diagnostics. The first challenge is the frequent massive bulk release of medical image databases, which often suffer from two common drawbacks: image duplication and corruption. The many subsequent releases of the same data with the same classes or categories come with no clear evidence of success in the concatenation of those identical classes among image databases. This issue stands as a stumbling block in the path of hypothesis-based experiments for the production of a single learning model that can successfully classify all of them correctly. Removing redundant data, enhancing performance, and optimizing energy resources are among the most challenging aspects. In this article, we propose a global data aggregation scale model that incorporates six image databases selected from specific global resources. The proposed valid learner is based on training all the unique patterns within any given data release, thereby creating a unique dataset hypothetically. The Hash MD5 algorithm (MD5) generates a unique hash value for each image, making it suitable for duplication removal. The T-Distributed Stochastic Neighbor Embedding (t-SNE), with a tunable perplexity parameter, can represent data dimensions. Both the Hash MD5 and t-SNE algorithms are applied recursively, producing a balanced and uniform database containing equal samples per category: normal, pneumonia, and Coronavirus Disease of 2019 (COVID-19). We evaluated the performance of all proposed data and the new automated version using the Inception V3 pre-trained model with various evaluation metrics. The performance outcome of the proposed scale model showed more respectable results than traditional data aggregation, achieving a high accuracy of 98.48%, along with high precision, recall, and F1-score. The results have been proved through a statistical t-test, yielding t-values and p-values. It's important to emphasize that all t-values are undeniably significant, and the p-values provide irrefutable evidence against the null hypothesis. Furthermore, it's noteworthy that the Final dataset outperformed all other datasets across all metric values when diagnosing various lung infections with the same factors.

Adversarial Attack and Defense in Breast Cancer Deep Learning Systems

Deep-learning-assisted medical diagnosis has brought revolutionary innovations to medicine. Breast cancer is a great threat to women's health, and deep-learning-assisted diagnosis of breast cancer pathology images can save manpower and improve diagnostic accuracy. However, researchers have found that deep learning systems based on natural images are vulnerable to attacks that can lead to errors in recognition and classification, raising security concerns about deep systems based on medical images. We used the adversarial attack algorithm FGSM to reveal that breast cancer deep learning systems are vulnerable to attacks and thus misclassify breast cancer pathology images. To address this problem, we built a deep learning system for breast cancer pathology image recognition with better defense performance. Accurate diagnosis of medical images is related to the health status of patients. Therefore, it is very important and meaningful to improve the security and reliability of medical deep learning systems before they are actually deployed.

How can we transform travel medicine by leveraging on AI-powered search engines?

A ChatGPT search engine with chatbot function offers travellers pre-, during and post-travel consultations. Users should critically evaluate information provided and assess source reliability. Integrating user feedback and reviews could enhance information quality. With responsible artificial intelligence (AI) development, leveraging travel medicine specialists’ expertise and AI-based search engines can transform travel medicine.

Applying Machine Learning to Healthcare Operations Management: CNN-Based Model for Malaria Diagnosis

The purpose of this study is to explore how machine learning technologies can improve healthcare operations management. A machine learning-based model to solve a specific medical problem is developed to achieve this research purpose. Specifically, this study presents an AI solution for malaria infection diagnosis by applying the CNN (convolutional neural network) algorithm. Based on malaria microscopy image data from the NIH National Library of Medicine, a total of 24,958 images were used for deep learning training, and 2600 images were selected for final testing of the proposed diagnostic architecture. The empirical results indicate that the CNN diagnostic model correctly classified most malaria-infected and non-infected cases with minimal misclassification, with performance metrics of precision (0.97), recall (0.99), and f1-score (0.98) for uninfected cells, and precision (0.99), recall (0.97), and f1-score (0.98) for parasite cells. The CNN diagnostic solution rapidly processed a large number of cases with a high reliable accuracy of 97.81%. The performance of this CNN model was further validated through the k-fold cross-validation test. These results suggest the advantage of machine learning-based diagnostic methods over conventional manual diagnostic methods in improving healthcare operational capabilities in terms of diagnostic quality, processing costs, lead time, and productivity. In addition, a machine learning diagnosis system is more likely to enhance the financial profitability of healthcare operations by reducing the risk of unnecessary medical disputes related to diagnostic errors. As an extension for future research, propositions with a research framework are presented to examine the impacts of machine learning on healthcare operations management for safety and quality of life in global communities.

Analysis of international competitive situation of key core technology in strategic emerging industries: New generation of information technology industry as an example

In the context of the current technological revolution and unprecedented major changes, countries are facing the situation of accelerating the development of key core technologies, which is caused by the transformation from the dispute over trade to the dispute over ecology and scientific and technological strength. Competitive situation analysis is an important link of key core technology innovation. The construction of a universal model of international competitive situation analysis of key core technology can provide scientific support for decision makers of science and technology innovation to solve technical difficulties. This study takes the new generation of information technology industry as an example, identifies key core technologies of the industry and evaluates the competitive situation of the major world countries. Studies indicate that in the field of new generation information technology, the US and Japan is in the leading position globally. In addition, China has active innovation activities in all fields, but overall there remains a considerable gap with the world-leading level, and its R&D quality needs to be further improved.

Exploring the Darkverse: A Multi-Perspective Analysis of the Negative Societal Impacts of the Metaverse

The Metaverse has the potential to form the next pervasive computing archetype that can transform many aspects of work and life at a societal level. Despite the many forecasted benefits from the metaverse, its negative outcomes have remained relatively unexplored with the majority of views grounded on logical thoughts derived from prior data points linked with similar technologies, somewhat lacking academic and expert perspective. This study responds to the dark side perspectives through informed and multifaceted narratives provided by invited leading academics and experts from diverse disciplinary backgrounds. The metaverse dark side perspectives covered include: technological and consumer vulnerability, privacy, and diminished reality, human-computer interface, identity theft, invasive advertising, misinformation, propaganda, phishing, financial crimes, terrorist activities, abuse, pornography, social inclusion, mental health, sexual harassment and metaverse-triggered unintended consequences. The paper concludes with a synthesis of common themes, formulating propositions, and presenting implications for practice and policy.

A step-by-step researcher's guide to the use of an AI-based transformer in epidemiology: an exploratory analysis of ChatGPT using the STROBE checklist for observational studies

Objective

This study aims at investigating how AI-based transformers can support researchers in designing and conducting an epidemiological study. To accomplish this, we used ChatGPT to reformulate the STROBE recommendations into a list of questions to be answered by the transformer itself. We then qualitatively evaluated the coherence and relevance of the transformer's outputs.

Study design

Descriptive study.

Methods

We first chose a study to be used as a basis for the simulation. We then used ChatGPT to transform each STROBE checklist's item into specific prompts. Each answer to the respective prompt was evaluated by independent researchers in terms of coherence and relevance.

Results

The mean scores assigned to each prompt were heterogeneous. On average, for the coherence domain, the overall mean score was 3.6 out of 5.0, and for relevance it was 3.3 out of 5.0. The lowest scores were assigned to items belonging to the Methods section of the checklist.

Conclusions

ChatGPT can be considered as a valuable support for researchers in conducting an epidemiological study, following internationally recognized guidelines and standards. It is crucial for the users to have knowledge on the subject and a critical mindset when evaluating the outputs. The potential benefits of AI in scientific research and publishing are undeniable, but it is crucial to address the risks, and the ethical and legal consequences associated with its use.

Increasing clinical medical service satisfaction: An investigation into the impacts of Physicians' use of clinical decision-making support AI on patients' service satisfaction

BACKGROUND

The medical industry is one of the key industries for the application of artificial intelligence (AI). Although it is believed that the combination of CDSS and physicians could improve the medical service, there are still many concerns about the usage of CDSS. Based on these concerns, limited studies have answered the question that when a physician makes decision independently or with AI's help, will there be any differences in patients' satisfaction with the medical service?

METHODS

This study uses the service fairness theory as a theoretical lens and employs three vignette experiments to address this research gap. There are totally 740 subjects recruited to participate into the three experiments. Group comparison methods and structural equation model are used to verify the hypotheses.

RESULTS

The experimental results reveal that: (1) physicians using AI can reduce patients' service satisfaction (M_difference=0.404,p=0.004); (2) the negative relationship between AI usage and service satisfaction can partially be mediated through distributive fairness and procedural fairness; (3) physicians actively informing their patients about the usage of AI can help mitigate the reduction in service satisfaction (M_difference=0.400,p=0.003) and three types of fairness M_difference^distributive=0.307,p=0.042;M_difference^procedural=0.483,p<0.001;M_difference^{interactional}=0.253,p=0.027.

CONCLUSION

This study investigates the effect of physicians using decision-making support AI on their patients' service satisfaction. These results contribute to the existing literature pertaining to AI and fairness theory, and also help in formulating some practical suggestions for medical staff and AI development companies.

Examining the research taxonomy of artificial intelligence, deep learning & machine learning in the financial sphere-a bibliometric analysis

This paper surveys the extant literature on machine learning, artificial intelligence, and deep learning mechanisms within the financial sphere using bibliometric methods. We considered the conceptual and social structure of publications in ML, AI, and DL in finance to better understand the research's status, development, and growth. The study finds an upsurge in publication trends within this research arena, with a bit of concentration around the financial domain. The institutional contributions from USA and China constitute much of the literature on applying ML and AI in finance. Our analysis identifies emerging research themes, with the most futuristic being ESG scoring using ML and AI. However, we find there is a lack of empirical academic research with a critical appraisal of these algorithmic-based advanced automated financial technologies. There are severe pitfalls in the prediction process using ML and AI due to algorithmic biases, mostly in the areas of insurance, credit scoring and mortgages. Thus, this study indicates the next evolution of ML and DL archetypes in the economic sphere and the need for a strategic turnaround in academics regarding these forces of disruption and innovation that are shaping the future of finance.

Exploring chatbot trust: Antecedents and behavioural outcomes

An awareness about the antecedents and behavioural outcomes of trust in chatbots can enable service providers to design suitable marketing strategies. An online questionnaire was administered to users of four major banking chatbots (SBI Intelligent Assistant, HDFC Bank's Electronic Virtual Assistant, ICICI bank's iPal, and Axis Aha) in India. A total of 507 samples were received of which 435 were complete and subject to analysis to test the hypotheses. Based on the results, it is found that the hypothesised antecedents, except interface, design, and technology fear factors, could explain 38.6% of the variance in the banking chatbot trust. Further, in terms of behavioural outcomes chatbot trust could explain, 9.9% of the variance in customer attitude, 11.4% of the variance in behavioural intention, and 13.6% of the variance in user satisfaction. The study provides valuable insights for managers on how they can leverage chatbot trust to increase customer interaction with their brand. By proposing and testing a novel conceptual model and examining the factors that impact chatbot trust and its key outcomes, this study significantly contributes to the AI marketing literature.

A Bibliometric Review of the Evolution of Blockchain Technologies

Bitcoin was created in 2008 as the first decentralized cryptocurrency, providing an innovative data management technology, which was later named blockchain. It ensured data validation without intervention from intermediaries. During its early stages, it was conceived as a financial technology by most researchers. It was not until 2015, when the Ethereum cryptocurrency was officially launched worldwide, along with its revolutionary technology called smart contracts, that researchers began to change their perception of the technology and look for uses outside the financial world. This paper analyzes the literature since 2016, one year after Ethereum, analyzing the evolution of interest in the technology to date. For this purpose, a total of 56,864 documents created between 2016 and 2022 from four major publishers were analyzed, providing answers to the following questions. Q1: How has interest in blockchain technology increased? Q2: What have been the major blockchain research interests? Q3: What have been the most outstanding works of the scientific community? The paper clearly exposes the evolution of blockchain technology, making it clear that, as the years go by, it is becoming a complementary technology instead of the main focus of studies. Finally, we highlight the most popular and recurrent topics discussed in the literature over the analyzed period of time.

Artificial Intelligence and Groups: Effects of Attitudes and Discretion on Collaboration

We theorize about human-team collaboration with AI by drawing upon research in groups and teams, social psychology, information systems, engineering, and beyond. Based on our review, we focus on two main issues in the teams and AI arena. The first is whether the team generally views AI positively or negatively. The second is whether the decision to use AI is left up to the team members (voluntary use of AI) or mandated by top management or other policy-setters in the organization. These two aspects guide our creation of a team-level conceptual framework modeling how AI introduced as a mandated addition to the team can have asymmetric effects on collaboration level depending on the team’s attitudes about AI. When AI is viewed positively by the team, the effect of mandatory use suppresses collaboration in the team. But when a team has negative attitudes toward AI, mandatory use elevates team collaboration. Our model emphasizes the need for managing team attitudes and discretion strategies and promoting new research directions regarding AI’s implications for teamwork.

The contributions of AI in the development of ideological and political perspectives in education

Opportunities and difficulties have arisen for the educational system as a result of the development of "artificial intelligence" technology. In the era of "artificial intelligence", the main features of ideological and political education in colleges are the development of the intelligence revolution, the development of teaching concepts, and the omnipotence of teaching content, and teaching methods. This study further explores the necessity and development of "artificial intelligence technology" in college ideological and political education through a questionnaire survey, and promotes the organic integration of artificial intelligence and ideological and political education. The results show that [1] College students have positive attitudes toward the application of artificial intelligence in college ideological and political education, and college students are looking forward to the intelligent services and changes brought by artificial intelligence technology in college ideological and political education [2]. According to the results of the questionnaire survey, this paper proposes the development path of college ideological and political education in the era of artificial intelligence, that is, schools and teachers need to improve the transformation of traditional education as well as the construction of contemporary Internet education. This study offers the possibility of interdisciplinary research, expands the scope of ideological and political education research, and provides a reference for front-line teaching to a certain extent.

The future of platforms, big data and new forms of capital accumulation

Purpose

The paper argues that the classical Marxist theory of capitalist accumulation is inadequate to understand new forms of capitalism and their accumulation processes determined by “platforms” and “big data”. Big data platforms are shaping the processes of production, labour, the price of products and market conditions. “Digital platforms” and “big data” have become an integral part of the processes of production, distribution and exchange relations. These twin pillars are central to the capitalist accumulation processes. The article argues that the classical Marxist theory of capitalist accumulation is inadequate to understand new forms of capitalism and their accumulation processes determined by “platforms” and “big data”.

Design/methodology/approach

As a conceptual paper, this paper follows critical methodological lineages and traditions based on non-linear historical narratives around the conceptualisation, construction and transition of the “Marxist theory of capital accumulation” in the age of platform economy. This paper follows a discourse analysis (Fairclough, 2003) to locate the way in which an artificial intelligence (AI)-led platform economy helps identify and conceptualise new forms of capitalist accumulation. It engages with Jørgensen and Phillips' (2002) contextual and empirical discursive traditions to undertake a qualitative comparative analysis by exploring a broad range of complex factors with case studies and examples from leading firms within the platform economy. Finally, it adopts two steps of “Theory Synthesis and Theory Adaptation” as outlined by Jaakkola (2020) to synthesise, adopt and expand the Marxist theory of capital accumulation under platform capitalism.

Findings

This article identifies new trends and forms of data driven capitalist accumulation processes within the platform capitalism. The findings suggest that an AI led platform economy creates new forms of capitalist accumulation. The article helps to develop theoretical understanding and conceptual frameworks to understand and explain these new forms of capital accumulation.

Originality/value

This study builds upon the limited theorisation on the AI and new capitalist accumulation processes. This article identifies new trends and forms of data driven capitalist accumulation processes within platform capitalism. The article helps to understand digital and platform capitalisms in the lens of digital labour and expands the theory of capitalist accumulation and its new forms in the age of datafication. While critiquing the Marxist theory of capitalist accumulation, the article offers alternative approaches for the future.

Can artificial intelligence’s limitations drive innovative work behaviour?

Artificial intelligence (AI) is deemed to increase workers’ productivity by enhancing their creative abilities and acting as a general-purpose tool for innovation. While much is known about AI’s ability to create value through innovation, less is known about how AI’s limitations drive innovative work behaviour (IWB). With AI’s limits in perspective, innovative work behaviour might serve as workarounds to compensate for AI limitations. Therefore, the guiding research question is: How will AI limitations, rather than its apparent transformational strengths, drive workers’ innovative work behaviour in a workplace? A search protocol was employed to identify 65 articles based on relevant keywords and article selection criteria using the Scopus database. The thematic analysis suggests several themes: (i) Robots make mistakes, and such mistakes stimulate workers’ IWB, (ii) AI triggers ‘fear’ in workers, and this ‘fear’ stimulates workers’ IWB, (iii) Workers are reskilled and upskilled to compensate for AI limitations, (iv) AI interface stimulates worker engagement, (v) Algorithmic bias requires IWB, and (vi) AI works as a general-purpose tool for IWB. In contrast to prior reviews, which generally focus on the apparent transformational strengths of AI in the workplace, this review primarily identifies AI limitations before suggesting that the limitations could also drive innovative work behaviour. Propositions are included after each theme to encourage future research.