Deployment Of Artificial Intelligence Research Articles

THE ROLE OF AI IN HEALTHCARE INDUSTRY

This article analyses the present condition of technological applications based on artificial intelligence (AI) and their influence on the healthcare sector. This work conducted a comprehensive literature research and examined certain real-world instances of AI implementations in the healthcare sector. Undoubtedly, the fast progress of artificial intelligence (AI) and associated technologies will enable healthcare providers to provide fresh value for their patients and enhance the effectiveness of their internal operations. However, successful deployment of AI will always pose distinct problems and the adoption of specific approaches to revolutionize the whole care service and operations in order to fully utilize the advantages of future technology. The analysis is derived on an examination of several academic sources, encompassing research from ScienceDirect, MDPI, Elsevier, and the Journal of Consortium. These sources address studies and data published from recent years till 2024.

Evolution of radiology staff perspectives during artificial intelligence (AI) implementation for expedited lung cancer triage

To investigate radiology staff perceptions of an AI tool for chest radiography triage, flagging findings suspicious for lung cancer to expedite same-day CT chest examination studies. Surveys were distributed to all radiology staff at three time points: at pre-implementation, one month and also seven months post-implementation of artificial intelligence (AI). Survey questions captured feedback on AI use and patient impact. Survey response rates at the three time periods were 23.1% (45/195), 14.9% (29/195) and 27.2% (53/195), respectively. Most respondents initially anticipated AI to be time-saving for the department and patient (50.8%), but this shifted to faster follow-up care for patients after AI implementation (51.7%). From the free text comments, early apprehension about job role changes evolved into frustration regarding technical integration challenges after implementation. This later transitioned to a more balanced view of recognised patient benefits versus minor ongoing logistical issues by the late post-implementation stage. There was majority disagreement across all survey periods that AI could be considered to be used autonomously (53.3-72.5%), yet acceptance grew for personal AI usage if staff were to be patients themselves (from 31.1% pre-implementation to 47.2% post-implementation). Successful AI integration in radiology demands active staff engagement, addressing concerns to transform initial mixed excitement and resistance into constructive adaptation. Continual feedback is vital for refining AI deployment strategies, ensuring its beneficial and sustainable incorporation into clinical care pathways.

Examining the applicability of the Protection of Personal Information Act in AI-driven environments

Background: Technological advancements have heightened the importance of safeguarding individual privacy and data. In response to these challenges, South Africa introduced the Protection of Personal Information (POPI) Act. This legislation established robust legal frameworks aimed at protecting confidential information and upholding individuals’ right to anonymity. However, there is a significant research gap regarding the POPI Act’s direct implications and effectiveness in the context of artificial intelligence (AI) adoption and utilisation. Understanding the interplay between the POPI Act and AI technologies is crucial for ensuring regulatory compliance, safeguarding personal data and fostering responsible AI deployment in South Africa.Objectives: This study investigates the POPI Act’s applicability in addressing privacy issues related to AI adoption in various sectors.Method: The research uses a document review methodology to analyse the documents and synthesise the results. This approach offers efficiency, accessibility, cost-effectiveness and non-intrusiveness benefits, making it a valuable tool for qualitative research across various disciplines.Results: Despite the POPI Act’s guiding principles aligning with key concepts of personal information protection, there are several gaps in its applicability to AI advancements across various sectors.Conclusion: The study emphasises the need for a dynamic legal framework that evolves with AI advancements, advocating for the incorporation of more stringent measures to address emerging privacy concerns.Contribution: The research contributes to the ongoing discourse on data protection and AI by highlighting the need for a forward-thinking legal framework that balances innovation and privacy, ensuring that the POPI Act remains effective in the face of evolving technologies.

Revolutionizing construction: A cutting-edge decision-making model for artificial intelligence implementation in sustainable building projects

This study examines how certain artificial intelligence (AI) drivers affect the industry's adoption of this technology in the construction industry. The research methods comprised a comprehensive analysis of previous studies to pinpoint the primary factors influencing AI adoption in the construction industry. Data collection was carried out through a well-structured survey involving relevant stakeholders in the building construction sector. The three main constructs of technological devices, advancement, and knowledge were found from the set of drivers with the technique of exploratory factor analysis. The deployment of AI in construction has the potential to improve health and safety and expedite project completion, as this research has evaluated. To figure out how these factors relate to the adoption of AI in the construction industry, partial least squares structural equation modeling was used. The study's conclusions showed that the influence of AI installation in the construction industry is reasonably significant thanks to the technology, advancement, and knowledge, contributing around 15 % of the effects that have been directly witnessed. The practical implications of AI for policy makers, engineers, and construction stakeholders are extensive and provide valuable insights for customized strategies aimed at using AI's potential to improve projects, promote sustainability, and elevate health and safety standards.

Exploring the Impact of Personalization in Artificial Intelligence on Digital Marketing: Performance Evaluation

With the expediential growth of the digital marketing landscape, the amalgamation of artificial intelligence (AI) technology stands pivotal in catalyzing transformative shifts within the market. Notably, in the realm of tailored marketing endeavors, AI has ushered in an epoch of unprecedented comprehension and prognostication of consumer behavior through the judicious employment of deep learning algorithms, semantic interpretation capabilities, and exhaustive data analytics. Yet, the intricate challenge of precisely gauging the efficacy of AI-empowered personalized marketing strategies remains an area requiring meticulous investigation. This scholarly paper endeavors to delve into the personalized deployment of AI within the digital marketing sector, employing a stringent framework to rigorously scrutinize its impact.

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.

Enabling AI in Radiology: Evaluation of an AI Deployment Process.

Artificial intelligence (AI) is expected to transform healthcare systems and make them more sustainable. Despite the increased availability of AI tools for disease detection, evidence of their impact on healthcare organisations and patient care remains limited. Drawing on previous research underscoring the need for comprehensive evaluations of real-world AI deployments, this paper explores the challenges and opportunities encountered while procuring and implementing AI solutions for radiology. The paper aims to contribute to a better understanding of the complexities surrounding AI deployments in real-world clinical settings through a process evaluation study.

Artificial intelligence research in Canadian hospitals: The development of metropolitan competencies.

This study explores the deployment of Artificial Intelligence (AI) in Canadian hospitals from 2000 to 2021, focusing on metropolitan areas. We investigate how local public and private research ecosystems and links to national and international AI hubs influence the adoption of AI in healthcare. Our analysis shows that AI research outputs from public institutions have a significant impact on AI competences in hospitals. In addition, collaborations between hospitals are critical to the successful integration of AI. Metropolitan areas such as Toronto, Montreal, and Vancouver are leading the way in AI deployment. These findings highlight the importance of local AI research capabilities and international hospital collaborations and provide guidance to policy-makers and health leaders to drive the diffusion of AI technology in healthcare.

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.

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.