Deployment Of Artificial Intelligence Research Articles

Designing AI for mental health diagnosis: challenges from sub-Saharan African value-laden judgements on mental health disorders

Recently clinicians have become more reliant on technologies such as artificial intelligence (AI) and machine learning (ML) for effective and accurate diagnosis and prognosis of diseases, especially mental health disorders....

The decision-making by citizens: Evaluating the effects of rule-driven and learning-driven automated responders on citizen-initiated contact

While many studies have investigated the impact of artificial intelligence (AI) deployment in the public sector on government-citizen interactions, findings remain controversial due to the technical complexity and contextual diversity. This study distinguishes between rule-driven and learning-driven AI and explores their impact as automated respondents on citizen-initiated contact, an important scenario for public participation with initiative. Based on a conjoint experiment with 763 participations (4578 observations), this study suggests that AI deployments enormously reduce the likelihood of citizen-initiated contact compared to human response, with learning-driven AI having a higher negative effect than rule-driven AI. In addition, the causal effects of respondent image, contact channel, contact purpose, and matter attributes on citizen-initiated contact, as well as their moderating effects, are explored. These findings make theoretical implications and calls for public participation in the roaring AI deployment in the public sector.

Examining the Social Consequences of Automation and Artificial Intelligence in Industrial Management

This article examines the social consequences of automation and artificial intelligence (AI) in industrial management, drawing on the findings of Damioli, Van Rooy, and Vertsy’s (2021) study on the impact of AI on labor productivity. The integration of AI technologies in industrial settings has led to significant improvements in efficiency and productivity. However, these advancements come with substantial social implications that affect both the workforce and broader societal structures. Internally, automation and AI have shifted the demand from low-skilled to high-skilled labor, necessitating ongoing education and skill development for employees. Additionally, the reliance on AI for managerial decisions raises ethical concerns regarding algorithmic biases and the potential erosion of human judgment.Externally, the broader societal impact includes economic disparities, as regions with a high concentration of low-skilled jobs experience greater unemployment and underemployment. The displacement of workers due to automation presents challenges such as increased socio-economic divides and disruptions to community stability. Ethical considerations, including data privacy and security, are crucial to ensuring fair and responsible AI deployment. This article underscores the need for strategic collaboration between industry leaders, policymakers, and educational institutions to address these social consequences. By fostering an inclusive approach to technological advancement, it is possible to balance the productivity benefits of AI with the imperative to mitigate its adverse social impacts.

Use of Artificial Intelligence in Medical Devices for Post-Market Surveillance

Artificial intelligence (AI) has emerged as a transformative tool in post-market surveillance (PMS) for monitoring the safety and performance of medical products. This article explores the role of AI in optimizing PMS practices, focusing on its applications in signal detection, risk assessment, and regulatory compliance. By harnessing machine learning algorithms and big data analytics, AI facilitates the automated analysis of real-world evidence, including patient outcomes data, adverse event reports, and electronic health records. Through pattern recognition and anomaly detection, AI algorithms enable the early identification of potential safety issues and facilitate timely interventions to mitigate risks. Moreover, AI-driven PMS systems enhance regulatory oversight by providing regulators with comprehensive and actionable insights into product safety profiles and emerging trends. However, concerns regarding data privacy, algorithm bias, and interpretability underscore the need for transparent and ethically responsible AI deployment in PMS frameworks.

Ethical considerations in AI use for SMEs and supply chains: Current challenges and future directions

The integration of Artificial Intelligence (AI) in Small and Medium-sized Enterprises (SMEs) and supply chains has revolutionized operational efficiencies and decision-making processes. This review paper aims to explore the ethical implications of AI adoption in these sectors, identifying current challenges and proposing future directions for ethical AI deployment. Through an extensive review of existing literature, the paper examines key ethical concerns such as data privacy, bias in AI algorithms, transparency, and the socio-economic impact on the workforce. The findings indicate that SMEs face unique ethical challenges due to their limited resources and expertise, which exacerbate issues related to AI implementation. Additionally, supply chains grapple with transparency and accountability, necessitating immediate attention to ensure ethical practices. The review concludes that establishing a robust ethical framework is crucial for guiding AI integration in SMEs and supply chains. It recommends the development of standardized ethical guidelines, enhanced stakeholder engagement, and increased investment in AI literacy and infrastructure. Future research should focus on creating adaptable ethical models that evolve alongside technological advancements and industry needs, ensuring that AI contributes to sustainable and equitable growth. This paper contributes to the ongoing discourse on ethical AI, offering actionable insights for policymakers, business leaders, and researchers dedicated to fostering responsible AI practices in SMEs and supply chains. Keywords: Ethical AI, SMEs (Small and Medium-sized Enterprises), Supply Chains, Algorithmic Bias, Data Privacy, Transparency, Accountability, AI Trust, Regulatory Compliance, Stakeholder Engagement, Job Displacement, Explainable AI (XAI), Workforce Reskilling, Sustainable AI Practices, AI Ethics Frameworks.

Fully Scalable Fuzzy Neural Network for Data Processing.

The primary objective of the research presented in this article is to introduce an artificial neural network that demands less computational power than a conventional deep neural network. The development of this ANN was achieved through the application of Ordered Fuzzy Numbers (OFNs). In the context of Industry 4.0, there are numerous applications where this solution could be utilized for data processing. It allows the deployment of Artificial Intelligence at the network edge on small devices, eliminating the need to transfer large amounts of data to a cloud server for analysis. Such networks will be easier to implement in small-scale solutions, like those for the Internet of Things, in the future. This paper presents test results where a real system was monitored, and anomalies were detected and predicted.

Unlocking AI's potential in the food supply chain: A novel approach to overcoming barriers

This paper delves into the challenges impeding the seamless integration of artificial intelligence (AI) within the food supply chain (FSC) and introduces a novel methodological framework that combines the NK Model with the Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique. Through an exhaustive literature analysis and expert discussions, the research identifies and categorizes significant obstacles to AI deployment in the FSC. These hurdles include the imperative for a skilled labor force, financial limits, regulatory complexity and technological limitations. The unique DEMATEL-NK approach highlights the interconnected nature of these barriers, pinpointing the most critical impediments. The study's implications extend to the broader domains of AI adoption in agriculture and the food industry, offering a nuanced perspective for policymakers, industry stakeholders, and researchers. The findings underscore the imperative of overcoming these barriers for the successful implementation of AI technologies in the FSC, promising advancements in efficiency, quality, and sustainability. The innovative methodology not only sheds light on the interconnectedness of these barriers but also provides a systematic approach for prioritizing and implementing solutions. This research offers a fresh viewpoint on barrier relationships, guiding decision-makers in crafting effective strategies and interventions to propel AI integration in the FSC forward.

Targeting Machine Learning and Artificial Intelligence Algorithms in Health Care to Reduce Bias and Improve Population Health.

Policy Points Artificial intelligence (AI) is disruptively innovating health care and surpassing our ability to define its boundaries and roles in health care and regulate its application in legal and ethical ways. Significant progress has been made in governance in the United States and the European Union. It is incumbent on developers, end users, the public, providers, health care systems, and policymakers to collaboratively ensure that we adopt a national AI health strategy that realizes the Quintuple Aim; minimizes race-based medicine; prioritizes transparency, equity, and algorithmic vigilance; and integrates the patient and community voices throughout all aspects of AI development and deployment.

Exploring the nexus of attitude, contextual factors, and AI utilization intentions: A PLS-SEM analysis among primary mathematics teachers in China

This study delves into the pivotal integration of Artificial Intelligence (AI) in primary mathematics education, examining the nuanced interplay between teachers’ attitudes toward AI, Contextual Factors (CFs), such as institutional support and resource availability, and their Intention to Use AI (IUAI) in teaching practices. With the advent of digital transformation in education, understanding the dynamics of AI adoption in primary settings becomes increasingly critical. Surveying 476 primary mathematics teachers in China, this research employs partial least squares structural equation modeling (PLS-SEM) to unravel the complexities influencing AI integration. The findings reveal that teachers’ attitudes significantly impact their IUAI, while CF, particularly institutional support and resource availability, play a crucial role in shaping these attitudes and directly influencing IUAI. This investigation not only bridges a vital research gap by offering empirical insights into the determinants of AI adoption in a primary education context but also provides a robust framework for educators, policymakers, and stakeholders to foster a conducive environment for AI integration. By aligning AI deployment with pedagogical strategies, this study contributes significantly to enhancing the quality and effectiveness of mathematics education, underscoring the transformative potential of AI in shaping future educational landscapes.

Incorporating AI into the Inner Circle of Emotional Intelligence for Sustainability

This paper delves into the fusion of artificial intelligence (AI) and emotional intelligence (EQ) by analyzing the frameworks of international sustainability agendas driven by UNESCO, WEF, and UNICEF. It explores the potential of AI integrated with EQ to effectively address the Sustainable Development Goals (SDGs), with a focus on education, healthcare, and environmental sustainability. The integration of EQ into AI use is pivotal in using AI to improve educational outcomes and health services, as emphasized by UNESCO and UNICEF’s significant initiatives. This paper highlights the evolving role of AI in understanding and managing human emotions, particularly in personalizing education and healthcare. It proposes that the ethical use of AI, combined with EQ principles, has the power to transform societal interactions and decision-making processes, leading to a more inclusive, sustainable, and healthier global community. Furthermore, this paper considers the ethical dimensions of AI deployment, guided by UNESCO’s recommendations on AI ethics, which advocate for transparency, accountability, and inclusivity in AI developments. It also examines the World Economic Forum’s insights into AI’s potential to revolutionize learning and healthcare in underserved populations, emphasizing the significance of fair AI advancements. By integrating perspectives from prominent global organizations, this paper offers a strategic approach to combining AI with EQ, enhancing the capacity of AI systems to meaningfully address global challenges. In conclusion, this paper advocates for the establishment of a new Sustainable Development Goal, SDG 18, focused on the ethical integration of AI and EQ across all sectors, ensuring that technology advances the well-being of humanity and global sustainability.

Advancements, Applications, and Future Directions of Artificial Intelligence in Healthcare

Background: The integration of artificial intelligence (AI) into healthcare represents a transformative shift in medical procedures, offering substantial benefits across various domains. With advancements in AI technologies such as machine learning (ML), deep learning (DL), and natural language processing (NLP), healthcare systems are witnessing improvements in early detection, patient treatment, and overall administration. This article traces the evolution of AI, from foundational contributions by Alan Turing during World War II to contemporary applications like ChatGPT, and examines the impact of AI in enhancing diagnostic accuracy and treatment outcomes. Methods: This comprehensive review analyzes the existing literature on AI applications in healthcare, focusing on various AI methodologies and their integration into clinical settings. It evaluates the effectiveness of AI in processing large datasets, improving diagnostic precision, and facilitating data-driven decision-making. The study also explores the ethical, legal, and technical challenges associated with AI deployment in medical environments. Results: AI technologies have demonstrated significant improvements in healthcare, particularly in early disease detection, personalized treatment plans, and resource management. The use of AI in analyzing vast medical datasets has enhanced diagnostic accuracy, reduced costs, and optimized patient care. However, challenges related to ethical considerations, patient privacy, and system reliability remain critical barriers to full-scale AI adoption. Conclusion: Despite the challenges, AI is positioned as an indispensable tool in modern medicine, capable of enhancing preventive care, personalizing treatments, and improving healthcare delivery. This review proposes a framework for evaluating the benefits, challenges, and strategies of AI integration in healthcare. Further research is essential to maximize AI's potential while addressing ethical and practical concerns, ensuring safe and effective implementation in clinical settings.

A starting framework for urban AI applications

There has been a growing concern about the ethical implications of Artificial Intelligence (AI) in urban environments. Scholars have highlighted the dual nature of AI, which can both enhance urban sustainability and pose significant privacy risks. The literature acknowledges that AI introduces specific challenges in urban contexts. However, there remains a gap in comprehensive sector-specific ethical frameworks tailored to the unique challenges posed by AI applications in cities. This study aims to fill this gap by proposing a starting framework for the ethics of Urban AI applications, providing principles to guide responsible AI development and deployment in urban settings. The study employed a two-phase methodology: a content analysis of 100 AI ethics documents to develop a preliminary framework, followed by a survey of experts and stakeholders to validate and expand this framework by identifying urban-specific risks and ethical considerations. The developed ethical framework for Urban AI applications provides a starting point to pave the way for ethical and sustainable urban AI systems that enhance the quality of life for all city residents.

Toward a person-environment fit framework for artificial intelligence implementation in the public sector

Using an embedded mixed method design, we compared a nationally representative sample of US adults and a sample of US-based emergency managers (EM) on their attitudes toward artificial intelligence (AI) and their intentions to rely on AI in a set of decision-making scenarios relevant to emergency management. Emergency managers reported significantly less positive attitudes toward AI and were less likely to rely on AI for decisions compared to the nationally representative sample. Our analysis of EMs' open-ended responses explaining their choices to use or not use AI-based solutions reflected specific concerns about implementation rather than wariness toward AI generally. These concerns included the complexity of the potential outcomes in the scenarios, the value they placed on human input and their own extensive experience, procedural concerns, collaborative decision-making, team-building, training, and the ethical implications of decisions, rather than a rejection of AI more generally. Managers' insights integrated with our quantitative findings led to a person-environment fit framework for AI implementation in the public sector. Our findings and framework have implications for how AI systems should be introduced and integrated in emergency managerial contexts and in public sector organizations more generally. Public managers' perceptions and intentions to use AI and organizational oversight processes are at least as important as technology design considerations when public sector organizations are considering the deployment of AI.

The Delphi Panel investigation of artificial intelligence in investor relations

In recent years, there has been a discernible upswing in the attention dedicated to artificial intelligence (AI) within the domains of public relations, advertising, and marketing. Notably, the subdomain of investor relations has maintained a significant historical engagement with AI, actively employing AI and AI-enabled tools for several decades, a practice traceable back to the 1980s. This protracted involvement presents a reservoir of invaluable insights germane to comprehending the broader integration of AI within the purview of public relations. This scholarly inquiry embarks on a Delphi panel examination to scrutinize the deployment of AI in investor relations, proffers a systematic classification of AI-enabled tools within this realm, and prognosticates the trajectory of AI's influence on investor relations and financial communications. The panel of Delphi participants comprises seasoned authorities in the field, boasting a cumulative professional experience spanning 161 years. Leveraging the depth of expertise inherent in investor relations, the study not only illuminates the current landscape but also posits conceivable trajectories for the evolution of AI across other subfields within the domain of public relations.

Artificial Intelligence-based automated CT brain interpretation to accelerate treatment for acute stroke in rural India: An interrupted time series study.

In resource-limited settings, timely treatment of acute stroke is dependent upon accurate diagnosis that draws on non-contrast computed tomography (NCCT) scans of the head. Artificial Intelligence (AI) based devices may be able to assist non-specialist physicians in NCCT interpretation, thereby enabling faster interventions for acute stroke patients in these settings. We evaluated the impact of an AI device by comparing the time to intervention (TTI) from NCCT imaging to significant intervention before (baseline) and after the deployment of AI, in patients diagnosed with stroke (ischemic or hemorrhagic) through a retrospective interrupted time series analysis at a rural hospital managed by non-specialists in India. Significant intervention was defined as thrombolysis or antiplatelet therapy in ischemic strokes, and mannitol for hemorrhagic strokes or mass effect. We also evaluated the diagnostic accuracy of the software using the teleradiologists' reports as ground truth. Impact analysis in a total of 174 stroke patients (72 in baseline and 102 after deployment) demonstrated a significant reduction of median TTI from 80 minutes (IQR: 56·8-139·5) during baseline to 58·50 (IQR: 30·3-118.2) minutes after AI deployment (Wilcoxon rank sum test-location shift: -21·0, 95% CI: -38·0, -7·0). Diagnostic accuracy evaluation in a total of 864 NCCT scans demonstrated the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) in detecting intracranial hemorrhage to be 0·89 (95% CI: 0·83-0·93), 0·99 (0·98-1·00), 0·96 (0·91-0·98) and 0·97 (0·96-0·98) respectively, and for infarct these were 0·84 (0·79-0·89), 0·81 (0·77-0·84), 0·58 (0·52-0·63), and 0·94 (0·92-0·96), respectively. AI-based NCCT interpretation supported faster abnormality detection with high accuracy, resulting in persons with acute stroke receiving significantly earlier treatment. Our results suggest that AI-based NCCT interpretation can potentially improve uptake of lifesaving interventions for acute stroke in resource-limited settings.

Artificial Intelligence Integration in Sustainable Business Practices: A Text Mining Analysis of USA Firms

Artificial Intelligence (AI) is transforming sustainable business strategies globally, yet its specific applications within American enterprises remain underexplored. This study examines the integration of AI in sustainability efforts across various industries in the USA from 2014 to 2022. By analyzing 263 sustainability reports from 41 leading Nasdaq-listed firms using advanced text mining techniques, we uncover nuanced insights into how AI is employed to address environmental and social challenges. Our findings reveal a strategic deployment of AI not only to enhance operational efficiency, but also to drive significant environmental improvements, such as optimizing renewable energy usage and mitigating emissions. Additionally, AI’s impact extends to fostering workplace safety, enhancing diversity, and bolstering community initiatives. This research highlights the critical role of AI as a catalyst in advancing sustainable practices, providing a blueprint for other regions and industries aiming to leverage technology for greater sustainability.

Future of IT Jobs in the Era of Artificial Intelligence

The rapid advancement of Artificial Intelligence (AI) technologies is set to reshape IT jobs in the coming years. This paper investigates AI's evolving role in the sector and its implications for employment trends, drawing from a review of secondary sources including various research journal papers, and reports of White House, World Economic Forum, and McKinsey, etc. It analyses the current AI applications in IT, focusing on areas where automation and augmentation will likely impact traditional roles. The study views AI as complementing rather than replacing human labour, foreseeing potential for new job creation and transformations in existing positions, especially in cybersecurity, software development, and IT infrastructure management. Policy recommendations and strategic initiatives from various stakeholders are examined to maximize AI benefits while addressing employment risks. Insights from government and industry reports stress the need for workforce preparation through reskilling and upskilling. Ethical considerations around AI deployment in IT underscore responsible innovation and inclusive growth. Ultimately, this paper aims to offer a comprehensive overview of AI's future impact on IT jobs, providing insights for policymakers, industry leaders, and educators navigating the evolving digital economy. By synthesizing diverse perspectives and empirical evidence, it informs decision-making for a sustainable and equitable future of work in IT.

AI'S Impact on Vocational Training and Employability: Innovation, Challenges, and Perspectives

This integrative literature review (ILR) examines the influence of artificial intelligence (AI) on vocational training, specifically focusing on unequal access to AI-powered resources and the ensuing inequities in education. The study seeks to analyze the impact of AI on vocational training and employability, offering insights into the advantages and difficulties related to integrating AI technology into educational institutions. The study's conceptual framework is grounded in three primary pillars: AI-driven innovation, challenges, and perspectives. This research is essential for providing valuable insights that can guide strategic planning and policy-making to improve vocational training programs and ensure that they remain effective in preparing students for the changing job market. The ILR methodology entailed integrating theoretical and empirical literature and collecting and evaluating relevant scholarly materials to provide a thorough comprehension of AI's function in vocational training. The results emphasize the capacity of AI to enhance educational achievements using tailored learning, adaptable platforms, immediate feedback, and simulations. However, there is a risk of widening educational inequalities due to biased algorithms. The study highlights the necessity of making significant investments in infrastructure and providing ongoing professional development for educators to incorporate AI successfully. It also suggests the establishment of distinct positions inside vocational training institutions, such as Vocational AI Curriculum Developer (VACD), Vocational AI Data Protection Specialist (VAIDPS), and Vocational AI Sustainability Facilitator (VAISF), to tackle these difficulties effectively. The conclusions highlight the revolutionary capacity of AI in vocational training, soliciting strategic investments and evoking the creation of specialized positions to promote fair and efficient deployment of AI. The study's findings underscore the significance of continuous research and improvements in practice to promote positive societal change and better educational fairness.

The Impact of Artificial Intelligence on Corporate Governance

The advent of artificial intelligence (AI) marks a pivotal shift in the landscape of corporate governance, catalyzing a reevaluation of traditional frameworks and necessitating a forward-looking approach to decision-making, risk management, and ethical considerations. This study explores the multifaceted impact of AI on corporate governance, offering a nuanced analysis of how AI technologies are transforming the operational, strategic, and ethical dimensions of organizations. The research underscores the potential of AI to enhance decision-making processes, optimize operational efficiencies, and foster innovation by providing advanced analytical capabilities and predictive insights. However, it concurrently highlights the emergence of unprecedented challenges, including data privacy concerns, algorithmic bias, and the need for robust regulatory frameworks to mitigate risks associated with AI deployment. The article advocates for a proactive stance in redefining corporate governance models to accommodate the disruptive nature of AI, emphasizing the integration of ethical considerations and transparency in AI applications. It calls for a collaborative effort among corporate leaders, policymakers, and stakeholders to develop governance structures that not only leverage AI’s potential but also safeguard against its inherent risks. The study’s recommendations include the establishment of ethical AI guidelines, the adoption of transparent AI practices, and the continuous monitoring of AI systems to ensure their alignment with corporate governance objectives and societal values. However, it is important to note that the approach and methods used in this study are based on a qualitativeliterature review and, therefore, the generalization of the findings across different sectors and corporate governance frameworks may be limited. Additionally, the rapidly evolving nature of AI technologies poses inherent challenges to keeping up with emerging trends and potential risks.

Impacts of artificial intelligence on carbon emissions in China: in terms of artificial intelligence type and regional differences

The influence of artificial intelligence (AI) on carbon emissions has long been contentious, and accurately clarifying this impact is crucial for the realization of sustainable smart cities. This article investigates the impact of AI on carbon emissions, distinguishing between applied and innovative AI types. Analyzing data from 267 Chinese cities between 2008 and 2019, the study reveals that applied AI reduces carbon dioxide emissions by an average of 40,100 tons per unit increase. In contrast, innovative AI initially leads to emissions increases before ultimately decreasing, illustrating an inverted U-shaped relationship. Regional variations in AI's impact on emissions are influenced by each region's developmental focus and stage of AI deployment. Specifically, differences arise due to the continuous carbon reduction (-0.024) associated with applied AI and the initial emission increase (0.129) attributed to innovative AI. The study underscores the significance of tailored regional strategies in AI development for fostering sustainable smart cities. These findings contribute to the ongoing discourse on AI's environmental impact, offering insights to inform targeted approaches in green, sustainable urban planning.