Implementation Of Artificial Intelligence Research Articles

AI-Powered Healthcare: Transforming Patient Outcomes with Machine Learning

AI and ML have flooded the healthcare industry with new technological approaches to affect patient experiences through smart approaches towards predictability, treatment, and diagnosis. The following paper focuses on exploring the effects caused by the implementation of artificial intelligence technologies in the sphere of healthcare. This research explores different case studies to prove that early diagnosis, treatment customization, and organizational effectiveness are all driven by AI. The paper is concerned with the approaches used in the implementation of artificial intelligence in the health sector, together with the consequent enhancement of patients’ experiences. This paper also looks at some of the issues of ethics and the future of artificial intelligence in the health sector while focusing on proactively securing the patient and the progress of the technology. Keywords: AI in Healthcare, Machine Learning, Patient Outcomes, Predictive Analytics, Healthcare

Revolutionizing Sourcing with AI: Harnessing Technology for Unprecedented Efficiency and Savings

Artificial Intelligence (AI) is transforming the landscape of sourcing, providing unprecedented efficiency and cost savings. This paper explores how AI technologies such as machine learning, natural language processing, and predictive analytics are revolutionizing sourcing processes. We examine case studies across various industries, highlighting the impacts of AI on supply chain optimization, supplier selection, and procurement processes. The findings indicate significant improvements in operational efficiency, decision-making accuracy, and financial performance. For instance, a leading automotive manufacturer reduced procurement costs by 20% using AI-driven sourcing (Smith et al., 2020), while a global retail giant increased supplier quality by 25% through AI-enhanced evaluation (Johnson et al., 2019). Additionally, a healthcare provider optimized inventory management, resulting in a 10% reduction in inventory costs (Williams and Brown, 2021). Future research directions and practical implications for AI-driven sourcing are also discussed, emphasizing the potential for continued innovation and growth in this field. The challenges and opportunities associated with AI implementation in sourcing are addressed, highlighting the need for quality data, integration with existing systems, and skilled personnel to manage AI technologies.

AI-driven cybersecurity solutions for real-time threat detection in critical infrastructure

Cyber-security as a concept relates to the protection of critical infrastructures that are significant to the security of a nation. Because many security threats have targeted computer-based critical infrastructures, nations have considered it necessary to enhance their security to detect and predict cyber threats accurately. This paper aimed to discuss the role of artificial intelligence (AI) in increasing real-time threat detection to critical infrastructures such as water facilities and transport systems. Through the implementation and application of AI, it is easier to not only detect the threat but also counter it promptly. Thus, the paper discussed the latest AI technologies, the approaches for their use and the cases, for example, the Colonial Pipeline ransomware attack, to demonstrate AI’s capabilities and limitations in this area. Other strategies such as measures and formulation of policies were also considered to ensure sound protection and improvement policies and regulations framework for cybersecurity.

Analysis of the use of artificial intelligence in the digital financial environment in Ukraine

The integration of artificial intelligence (AI) in the digital financial environment of Ukraine presents numerous opportunities and challenges. The purpose of the paper is to analyze the current state and future prospects of AI applications in the Ukrainian financial sector. By examining successful case studies, the study will evaluate the effectiveness of AI implementations in banks and financial institutions. Key success factors and barriers to AI adoption will be identified, providing insights into best practices and common pitfalls. The regulatory landscape is crucial for fostering innovation while ensuring security and compliance. This research will explore the existing regulatory framework in Ukraine, identifying gaps and proposing recommendations to enhance the legislative support for AI advancements in the financial sector. Understanding how regulations can balance innovation with consumer protection is essential for sustainable growth. Data protection and cybersecurity are critical issues in the digital financial environment. The study will investigate the impact of AI on these areas, assessing how AI technologies can both bolster and threaten security measures. By analyzing current strategies and technologies used to protect financial data, the research will offer insights into improving cybersecurity in AI-driven financial services. Additionally, the social and economic implications of AI adoption will be explored. The research will examine how AI affects employment in the financial sector, considering both the potential for job displacement and the creation of new roles. The changing skill requirements and the need for workforce retraining will be discussed, emphasizing the importance of education and training programs to prepare for an AI-driven economy. Furthermore, the study will delve into the ethical considerations surrounding AI use in finance. Issues such as algorithmic bias, transparency, and accountability will be analyzed to ensure that AI applications are fair and equitable. By addressing these ethical concerns, the research aims to contribute to the development of responsible AI practices in the financial industry. In conclusion, this research will provide a comprehensive analysis of AI in the digital financial environment of Ukraine. By addressing the technological, regulatory, security, social, and ethical dimensions, the study aims to offer a holistic view of AI’s impact and potential in the Ukrainian financial sector. The findings will be valuable for policymakers, financial institutions, and technology developers, guiding them towards effective and responsible AI integration.

Barriers to and Facilitators of Artificial Intelligence Adoption in Health Care: Scoping Review.

Artificial intelligence (AI) use cases in health care are on the rise, with the potential to improve operational efficiency and care outcomes. However, the translation of AI into practical, everyday use has been limited, as its effectiveness relies on successful implementation and adoption by clinicians, patients, and other health care stakeholders. As adoption is a key factor in the successful proliferation of an innovation, this scoping review aimed at presenting an overview of the barriers to and facilitators of AI adoption in health care. A scoping review was conducted using the guidance provided by the Joanna Briggs Institute and the framework proposed by Arksey and O'Malley. MEDLINE, IEEE Xplore, and ScienceDirect databases were searched to identify publications in English that reported on the barriers to or facilitators of AI adoption in health care. This review focused on articles published between January 2011 and December 2023. The review did not have any limitations regarding the health care setting (hospital or community) or the population (patients, clinicians, physicians, or health care administrators). A thematic analysis was conducted on the selected articles to map factors associated with the barriers to and facilitators of AI adoption in health care. A total of 2514 articles were identified in the initial search. After title and abstract reviews, 50 (1.99%) articles were included in the final analysis. These articles were reviewed for the barriers to and facilitators of AI adoption in health care. Most articles were empirical studies, literature reviews, reports, and thought articles. Approximately 18 categories of barriers and facilitators were identified. These were organized sequentially to provide considerations for AI development, implementation, and the overall structure needed to facilitate adoption. The literature review revealed that trust is a significant catalyst of adoption, and it was found to be impacted by several barriers identified in this review. A governance structure can be a key facilitator, among others, in ensuring all the elements identified as barriers are addressed appropriately. The findings demonstrate that the implementation of AI in health care is still, in many ways, dependent on the establishment of regulatory and legal frameworks. Further research into a combination of governance and implementation frameworks, models, or theories to enhance trust that would specifically enable adoption is needed to provide the necessary guidance to those translating AI research into practice. Future research could also be expanded to include attempts at understanding patients' perspectives on complex, high-risk AI use cases and how the use of AI applications affects clinical practice and patient care, including sociotechnical considerations, as more algorithms are implemented in actual clinical environments.

The use of artificial intelligence (AI) in student learning process in the digital era

Purpose:Artificial intelligence (AI) has become one of the most influential technologies in the digital era, and its potential to revolutionize the learning process is enormous. This article discusses the role of AI in improving the quality of education in the digital era. This research aims to provide an understanding of the role of AI in the learning process in the digital era. Method:The Method section is required to outline the study design (whether qualitative, quantitative, or a combination of both), along with general procedures, including details on participant characteristics, numbers, and data collection methods. If the study doesn't involve primary data collection, please provide information on the methods used to summarize previously reported data, such as narrative systematic review, or meta-analysis. Result::The research method used is a literature review, which includes an analysis of various sources and views related to the use of AI in higher education. The data used in this article is information from various literary sources, including research results, scientific articles, and news related to the implementation of AI in education. Examples of the role of AI include several main aspects, including personalization, interactivity, feedback, accessibility, and efficiency. Conclusion: By applying AI to the learning process, we can create a more personalized, interactive, and effective learning experience for all students.

ANALYSIS OF CHALLENGES AND PERSPECTIVES IN THE ACCEPTANCE OF ARTIFICIAL INTELLIGENCE IN RADIOLOGY BY HEALTHCARE WORKERS

Aim: The research aims to explore healthcare workers' attitudes towards and acceptance of artificial intelligence in radiology, focusing on its potential to enhance diagnostic accuracy, reduce waiting times, increase service efficiency, and improve patient care. It seeks to assess their willingness to adopt artificial intelligence, along with their concerns, expectations, and educational needs to effectively utilize this technology in radiological practice.Methods: A prospective study surveyed 50 healthcare workers, including radiologists, technicians, nurses, and others using various radiological techniques for diagnosing diseases. Using a quantitative approach, numerical data from distributed surveys assessed their attitudes and knowledge regarding artificial intelligence in radiology. Conducted at Public Health Institution "Zdravstveni centar Brcko" in Bosnia and Herzegovina's Brcko District over 60 days, the research adhered to ethical principles and received approval from the center's Ethics Committee.Results: The majority of respondents recognize the potential of artificial intelligence to improve the efficiency of radiological services and treatment processes, but at the same time express the need for additional education and training in order to optimally use this technology. Despite the positive perception, part of the respondents are still not sure about the use of artificial intelligence, which emphasizes the importance of continuous information and education of healthcare workers.Conclusion: Ultimately, the research results indicate the importance of further steps in the implementation of artificial intelligence in radiology in order to improve the quality of health care and optimize treatment processes.Keywords: Artificial intelligence, radiological diagnostics, challenges, perspectives, quality of health care.

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.

Beyond BiCIKL: Towards Building an AI-Assisted “Biodiversity Supergraph”

BiCIKL (Biodiversity Community Integrated Knowledge Library) is a European Union (EU) Horizon 2020 project (2021–2024) building a new community of research infrastructures (RIs), researchers and other stakeholders, through improved access to interlinked, open and FAIR (Findable, Accessible, Interoperable, Reusable) biodiversity data along the biodiversity research cycle (specimens, sequences, taxon names, publications) (Penev et al. 2022). The project’s 14 partners developed or substantially improved 16 tools and services currently in process of onboarding to the European Open Science Cloud (EOSC), presented in the FAIR Data Place (FDP) of BiCIKL’s flagship product, the Biodiversity Knowledge Hub (BKH). The tools and data were used in Open Call projects, performed by research groups worldwide. A key achievement of BiCIKL is the establishment of several new bi-directional links between the participating RIs through shared and interoperable data standards and web services. The sustainability of the BiCIKL services and especially of the strong collaborative spirit developed through the project will be ensured by a membership agreement for the BKH maintenance and further development. The results of BiCIKL are diverse and tackle various aspects of the implementation of open science practices in biodiversity research. The project partners and external collaborators from the Open Call projects published more than 80 papers and conference abstracts (see the article collections in Penev et al. 2022a and Thessen et al. 2023), two policy briefs (Penev et al. 2024, Agosti et al. 2024), three Biodiversity Information Science (TDWG) symposia (2021, 2023, 2024), several videos and factsheets and other training materials, guidelines and best practice recommendations, and so on. In the special focus of BiCIKL was the extraction and liberation of data from the PDFs of several thousands of published biodiversity articles making it accessible and re-usable. The new BiCIKL community proved to be successful in both technological innovation and long-lasting spirit of collaboration between biodiversity and genomics researchers, data repositories, RIs, publishers and other stakeholders. Beyond BiCIKL, we envisage our work towards further integration and interoperability between data domains by embracing human-in-the-loop collaborations, enhanced by Artificial Intelligence (AI). The implementation of AI and Large Language Models (LLM) should be possible when considering an important condition: to understand the complexity of past, recent and future changes in biodiversity and natural environments the use of AI tools should be based on аdequately curated, semantically structured and interlinked biodiversity data. We see this radical new step as a concerted community effort towards building a “Biodiversity Supergraph” (Fig. 1), understood here as a two-component ecosystem consisting of: centrally orchestrated system of tools and services, and distributed sources of transformed, semantically enhanced FAIR Linked Open Data, supplied by the partnering RIs. centrally orchestrated system of tools and services, and distributed sources of transformed, semantically enhanced FAIR Linked Open Data, supplied by the partnering RIs. The “Biodiversity Supergraph'' will provide integration of the biodiversity data on a scale and operational level that has never been attempted before. It is key for the next decade, to enable a baseline of global, biodiversity-related information serving organisations, academia, industry and society.

Adoption of artificial intelligence to enhance records management practices at Gauteng Department of Education in South Africa

Purpose This study aims to examine the adoption of artificial intelligence (AI) to enhance records management practices at the Gauteng Department of Education (GDE) in South Africa. Design/methodology/approach The study used a convergent mixed-methods research approach, employing interviews and questionnaires for data collection. Analysis of the collected data involved both verbatim and statistical methods, and the results were presented through tables and figures. Findings The study revealed that AI can proficiently and effectively execute all records management practices across the entire records lifecycle. Originality/value Consequently, the study proposed a framework that can be used as a guiding tool for the implementation and use of AI. This framework aims to enhance records management processes, not only within the GDE but also within the broader archives and records management industry.

Minimally invasive surgery approaches in major liver resection for hepatocellular carcinoma

Over the past two decades, minimally invasive liver resection (MILR) has progressively become the standard for minor resection of hepatocellular carcinoma (HCC). In contrast, its implementation in major hepatectomy has been slower due to its higher technical complexity and morbidity. However, studies published in recent years have demonstrated that minimally invasive major hepatectomy (MIMH) is safe and feasible with less blood loss, fewer complications, earlier recovery, decreased hospital stay, and improved cosmetic outcomes compared with open surgery. This review examines the current status of MIMH, focusing on the respective strengths and weaknesses of the laparoscopic and robotic approaches, as well as assessing their cost-effectiveness and possible future directions. The earlier introduction of laparoscopic liver resection has led to its widespread utilization as a safe and accurate alternative to open hepatectomy. Of note is that the robotic approach has also been increasingly utilized, demonstrating better perioperative outcomes and decreased need for conversion to open hepatectomy vs. the laparoscopic approach. Advantages associated with the robotic approach include the three-dimensional large visual field and instrumental flexibility, allowing for improved bleeding control. Even though evidence has been slowly accumulating regarding long-term outcomes, most studies report no notable differences between open, robotic, and laparoscopic resection for HCC. Regarding cost-effectiveness, MIMH has been characterized by higher operating room and anesthesia costs, especially for robotic procedures that require highly expensive equipment. The increase in cost is often offset by the lower morbidity and, thus, shorter hospital stays associated with MIMH. Future developments, such as the implementation of artificial intelligence and augmented reality, may potentially maximize the safety and applicability of MIMH and, in time, lead to improved postoperative and oncological outcomes.

AI and the Future of Teaching: Preservice Teachers’ Reflections on the Use of Artificial Intelligence in Open and Distributed Learning

The rapid advancement of artificial intelligence (AI) in education underscores transformative prospects for open and distributed learning, encompassing distance, hybrid, and blended learning environments. This qualitative study, grounded in narrative inquiry, investigates the experiences and perceptions of 141 preservice teachers engaged with AI, mainly through ChatGPT, over a 3-week implementation on Zoom to understand its influence on their evolving professional identities and instructional methodologies. Employing Strauss and Corbin’s methodological approach of open, axial, and selective coding to analyze reflective narratives, the study unveils significant themes that underscore the dual nature of AI in education. Key findings reveal ChatGPT’s role in enhancing educational effectiveness and accessibility while raising ethical concerns regarding academic integrity and balanced usage. Specifically, ChatGPT was found to empower personalized learning and streamline procedures, yet challenges involving information accuracy and data security remained. The study significantly contributes to teacher education discourse by revealing AI’s complex educational impacts, highlighting an urgent need for comprehensive ethical AI literacy in teacher training curricula. However, critical ethical considerations and practical challenges involving academic integrity, information accuracy, and balanced AI use are also brought to light. The research also spotlights the need for responsible AI implementation in open and distributed learning to optimize educational outcomes while addressing potential risks. The study’s insights advocate for future-focused AI literacy frameworks that integrate technological adeptness with ethical considerations, preparing teacher candidates for an intelligent digital educational landscape.

Cyber Security and Artificial Intelligence in Aviation

Airports are becoming increasingly interconnected and technologically advanced ecosystems. Airports handle sensitive data and depend on interconnected systems, which, while improving efficiency, also heighten vulnerability to cyber threats like data breaches and ransom ware. Additionally, with technological advancements artificial intelligence also plays a huge role, providing a new definition to cyber security in aviation. This paper examines the current state of cyber security in the aviation industry, implementation of artificial intelligence and how we can make the best use of technology, whilst focusing on the vulnerabilities and threats that accompany the adoption of advanced technologies such as Internet of Things (IoT) and Supervisory Control and Data Acquisition (SCADA) systems. The paper also explores the types of cyber attacks that can target smart airport systems, emphasising the need for robust cyber security measures to protect these critical infrastructures.

Stakeholders’ insights on artificial intelligence education: Perspectives of teachers, students, and policymakers

The integration of artificial intelligence (AI) as a subject into K-12 education worldwide is still in its early stages and undoubtedly needs further investigation. There is limited effort on understanding policymakers, teachers and students’ viewpoints on AI learning within the school system. This study gathered the thoughts of key stakeholders, including policymakers, higher education and K-12 teachers, and students in Nigeria, to understand their conceptions, concerns, and dispositions, with the aim of aiding the implementation of AI in schools. We further explored the needs of the diverse stakeholders, how they can be supported and juxtaposed their views to identify their priorities and how their opinions combined could give a holistic approach to the effective implementation of AI education. This research employed a qualitative methodology using semi-structured interviews as the means of data collection. The thematic analysis of the interview data from the 21 participants indicates their conceptions, what they considered the priorities for including AI in the school system, concerns and support needed to implement AI in schools. The findings of this study contribute to the ongoing conversation on how to effectively integrate AI into school curriculum.

Exploring the factors driving AI adoption in production: a systematic literature review and future research agenda

AbstractOur paper analyzes the current state of research on artificial intelligence (AI) adoption from a production perspective. We represent a holistic view on the topic which is necessary to get a first understanding of AI in a production-context and to build a comprehensive view on the different dimensions as well as factors influencing its adoption. We review the scientific literature published between 2010 and May 2024 to analyze the current state of research on AI in production. Following a systematic approach to select relevant studies, our literature review is based on a sample of articles that contribute to production-specific AI adoption. Our results reveal that the topic has been emerging within the last years and that AI adoption research in production is to date still in an early stage. We are able to systematize and explain 35 factors with a significant role for AI adoption in production and classify the results in a framework. Based on the factor analysis, we establish a future research agenda that serves as a basis for future research and addresses open questions. Our paper provides an overview of the current state of the research on the adoption of AI in a production-specific context, which forms a basis for further studies as well as a starting point for a better understanding of the implementation of AI in practice.

Nurses' perspectives on privacy and ethical concerns regarding artificial intelligence adoption in healthcare

BackgroundWith the increasing integration of artificial intelligence (AI) technologies into healthcare systems, there is a growing emphasis on privacy and ethical considerations. Nurses, as frontline healthcare professionals, are pivotal in-patient care and offer valuable insights into the ethical implications of AI adoption. ObjectivesThis study aimed to explore nurses' perspectives on privacy and ethical concerns associated with the implementation of AI in healthcare settings. MethodsWe employed Van Manen's hermeneutic phenomenology as the qualitative research approach. Data were collected through purposive sampling from the December 7, 2023 to the January 15, 2024, with interviews conducted in Bengali. Thematic analysis was utilized following member checking and an audit trail. ResultsSix themes emerged from the research findings: Ethical dimensions of AI integration, highlighting complexities in incorporating AI ethically; Privacy challenges in healthcare AI, revealing concerns about data security and confidentiality; Balancing innovation and ethical practice, indicating a need to reconcile technological advancements with ethical considerations; Human touch vs. technological progress, underscoring tensions between automation and personalized care; Patient-centered care in the AI era, emphasizing the importance of maintaining focus on patients amidst technological advancements; and Ethical preparedness and education, suggesting a need for enhanced training and education on ethical AI use in healthcare. ConclusionsThe findings underscore the importance of addressing privacy and ethical concerns in AI healthcare development. Nurses advocate for patient-centered approaches and collaborate with policymakers and tech developers to ensure responsible AI adoption. Further research is imperative for mitigating ethical challenges and promoting ethical AI in healthcare practice.

Evaluation of End-User Participation in Artificial Intelligence Nursing Projects.

Artificial Intelligence (AI) projects in healthcare, particularly in nursing, currently gain relevance but encounter challenges in user acceptance. Active participation of end-users in the development and implementation of AI can enhance acceptance. This study proposes a scale to measure the degree of end-user participation in AI development and implementation for nursing on the project level, rated by project managers. It employs the qualitative-analytical COARSE method for scale development and evaluation. The instrument includes 11 items across two sub-scales: activities for active participation of end-users and empowerment activities. It highlights the importance of the measurement's purpose and consequences for interpreting the results of the evaluated degree of end-user participation. The study points to future research opportunities, underscored by the need for psychometric validation, such as reliability and validity.

A Vision on User-Centered Implementation and Evaluation of Explainable AI for Predicting Hospital-Onset Bacteremia.

In recent years, artificial intelligence (AI) has gained momentum in many fields of daily live. In healthcare, AI can be used for diagnosing or predicting illnesses. However, explainable AI (XAI) is needed to ensure that users understand how the algorithm arrives at a decision. In our research project, machine learning methods are used for individual risk prediction of hospital-onset bacteremia (HOB). This paper presents a vision on a step-wise process for implementation and evaluation of user-centered XAI for risk prediction of HOB. An initial requirement analysis revealed first insights on the users' needs of explainability to use and trust such risk prediction applications. The findings were then used to propose step-wise process towards a user-centered evaluation.

Artificial Intelligence in the Saudi Arabian Banking Sector: Role in Customer Satisfaction and Its Implementation Challenges

The study aimed to examine the impact of artificial intelligence on customer satisfaction and the challenges Saudi Arabian banks face in implementing this cross-cutting technology. The study used a survey design and collected responses from 100 participants, mainly bank customers and bank officials. The result revealed that artificial intelligence (AI) is positively and significantly correlated with customer satisfaction (CS). This suggests that customer satisfaction tends to rise in tandem with the application of AI in banking. The mediation analysis result showed that Ease of Use only mediates 9.82% of the relationship between AI and CS, and it is not statistically significant (β=0.0607 (95% Cl: -.0246, .146), z=1.39, p=0.163. The study provides practical insights for Saudi Arabian banks, highlighting the need to enhance the adoption of AI to promote customer satisfaction. It also outlines frameworks for minimizing challenges and barriers against the implementation of AI, including promoting data security and customer privacy.

Environmental, Social, and Governance-Based Artificial Intelligence Governance: Digitalizing Firms’ Leadership and Human Resources Management

The integration of artificial intelligence (AI) with environmental, social, and governance (ESG) factors is impacting the direction of enterprises and society in our swiftly expanding world. This collaboration has significant potential to tackle critical issues such as reducing the impact of climate change, fostering social integration, and improving corporate governance. Nevertheless, the implementation of AI gives rise to intricate matters and apprehensions, as it brings out a distinct array of hazards and ethical quandaries for ESG performance. The objective of the present research is to fill this gap by gathering and offering a contemporary evaluation of the influence of advancing technologies on the strategic leadership’s role in fulfilling the business goal within the context of ESG considerations. We used bibliometric analysis to investigate the study subject using R Studio version 4.2.0 and the bibliometric applications VOSviewer version 1.6.20 and Biblioshiny version 4.2.0. We obtained data from the Scopus database and used the PRISMA approach to suitably choose 205 research publications. The results suggest that it is essential to use AI and ESG to digitize the boardroom. Additionally, it is crucial to guarantee its security using an advanced detection system. Therefore, chief executive officers (CEOs) must give priority to the issues of transparency and cybersecurity to reduce risks and successfully inspire trust in business activities.