Implementation Of Artificial Intelligence Research Articles

AI in Scientific Research: Empowering Researchers with Intelligent Tools

This article explores the transformative impact of artificial intelligence (AI) on scientific research across various disciplines. It examines how AI-driven tools are revolutionizing data analysis, simulation, and hypothesis generation, particularly in fields such as genomics, climate science, and materials science. The article discusses the acceleration of discovery processes through AI, highlighting its role in enabling sophisticated analysis of complex datasets, developing predictive models, and facilitating automated experimentation. Ethical considerations, including the need for transparency and reproducibility in AI-assisted research, are addressed. The synergy between human creativity and AI capabilities is explored, emphasizing how AI augments human ingenuity and fosters interdisciplinary collaboration. Case studies illustrate successful implementations of AI in scientific inquiry, demonstrating its potential to enhance research methodologies and outcomes. The article also looks ahead to the prospects of AI in scientific research, considering emerging technologies and the evolving role of AI in the scientific process. By providing a comprehensive overview of AI's current applications and future potential in scientific research, this article underscores the pivotal role of AI in advancing scientific knowledge and addressing complex global challenges.

Exploring the Implementation of Artificial Intelligence (AI) Writing Tools in Teaching and Learning: Faculty and Students’ Perspectives in Higher Education

Artificial Intelligence (AI) has emerged as a transformative tool in the field of education, with its potential to revolutionize teaching and learning in higher education institutions. People are exploring ways to connect its power and reform traditional educational practices. This paper explores the implementation of five different AI writing tools in teaching and learning in Higher Education, focusing on the perspectives of faculty and students. By examining their perspectives and identifying the challenges that can be encountered, the study seeks to gain a better understanding of the benefits and limitations of integrating AI writing tools in higher education. There are limited studies that seek to determine the perspectives of faculty and students in higher education regarding AI writing tools. The method used for the study is literature review, particularly hand-searching journal, and books approach. The study's findings have revealed that AI writing tools perform a multitude of functions, yet they also possess certain limitations

Regulatory impact of a governmental approach for artificial intelligence technology implementation in Vietnam

This study assesses Vietnam’s state-level implementation of artificial intelligence (AI) technology and analyses the government’s efforts to encourage AI implementation by focusing on the National Strategy on AI Development Program. This study emphasizes the possibility of implementing AI at the state level in Vietnam and the importance of conducting continuous reviews and enhancements to achieve sustainable and inclusive AI growth. Impact evaluations were conducted in public organizations alone, and implication evaluations were considered optional. AI impact assessments were constrained by societal norms that necessitated establishing relationships among findings. There is a lack of official information regarding the positive impact of Vietnam’s AI policy on the development of AI infrastructure, research, and talent pools. The study’s findings highlight the necessity of facilitating extensive AI legislation, and strengthening international cooperation. The study concludes with the following recommendations for improving Vietnam’s AI policy: implementing a strong AI governance structure and supporting AI education and awareness.

Artificial Intelligence: Contributions to Efficiency and Sustainability in the Court of Justice of The State of Goiás

Objective: To present analyses related to the integration between Artificial Intelligence (AI) and Sustainable Regional Development (SRD) regarding the expansion of access to justice, based on the experience of the Court of Justice of the State of Goiás (TJGO). Theoretical Framework: Advanced technologies in the Judicial System and their contribution to global sustainability, as well as implications for the formulation of public policies for the responsible integration of AI in judicial processes. Methodology: Qualitative and quantitative analysis of data acquired from research, bibliographical and documentary from Reports and Resolutions of the Court of Justice of Goiás – TJGO and National Council of Justice – CNJ, and literature related to AI and Sustainable Regional Development – SRD, about the relationship between sustainability, SRD and AI; the implementation of AI in Brazilian courts, with emphasis on the guidelines of the CNJ (TJGO); the evolution and impact of AI regulations in the Brazilian Judiciary (TJGO); and the effective use of AI in the context of TJGO and other Brazilian courts. Results and Discussion: As a result of this research, we have identified an improvement in procedural efficiency in the judicial system (in access to justice), based on the implementation of AI, especially for vulnerable groups. Implications of the research: The functionality and dynamism of the social and environmental spheres require studies and analyses that assist in the integration for development based on the balanced alignment between both. In this sense, it is important to direct efforts to identify how and to what extent technological advances can be useful for social development, in this case, based on the practices developed by TJGO. Originality/Value: This study presents recent information and analyses on strategies and practices of the Brazilian Judiciary for the integration of Artificial Intelligence and Sustainable Regional Development regarding the expansion of access to justice, based on the experience of the Court of Justice of the State of Goiás (TJGO).

Exploring the Educational Landscape of ChatGPT: A Topic Modeling Approach on Twitter Data

In the rapidly evolving landscape of Artificial Intelligence (AI), platforms like ChatGPT are reshaping the educational domain, prompting deeper explorations into the nature and depth of this intersection. This study aimed to systematically uncover the prevailing sentiments, concerns and discussions on Twitter surrounding ChatGPT’s role in education. Through an extensive data collection process, over 3.8 million tweets were initially gathered, followed by rigorous refining processes that included expert-driven tweet labelling and subsequent classification using Machine Learning (ML) and deep learning models. The cleaned dataset underwent a series of preprocessing steps and feature extraction and was ultimately subjected to Latent Dirichlet Allocation (LDA) for topic modelling. Our findings unveiled 15 distinct topics that broadly spanned common discussions, AI implementation, and its potential impacts. The data’s visualisation using t-distributed stochastic neighbour embedding (t-SNE) showcased a dense central clustering of these topics. In conclusion, our research underscores the multi-faceted dialogues on AI, particularly ChatGPT, in education, emphasising the pressing need for continued discourse and research as AI tools further integrate into our educational paradigms.

Elevating STEM Learning: Unleashing the Power of AI in Open Distance eLearning

The integration of Artificial Intelligence (AI) into Open Distance eLearning (ODeL) represents a significant evolution in STEM education, offering transformative benefits in teaching, learning and administrative processes. This conceptual paper explores how AI-driven platforms are revolutionising ODeL by providing personalised learning experiences, real-time feedback, adaptive assessments and immersive simulations through technologies like Augmented Reality (AR) and Virtual Reality (VR). These advancements not only enhance student engagement and understanding but also support the development of critical thinking and problem-solving skills. The paper highlights successful case studies from institutions like Carnegie Mellon University and Stanford Online High School, showcasing practical implementations of AI in STEM education. However, the integration of AI also brings forth challenges, particularly concerning data privacy, security and the need for equitable access to technology. As AI continues to advance, it promises to play an important role in lifelong learning, offering modular and just-in-time educational opportunities. The paper concludes with a call for robust ethical frameworks to guide the responsible use of AI in education, ensuring that the benefits are maximized while potential risks are mitigated.

Sacred Meets Synthetic: A Multi-Method Study on the First AI Church Service

Artificial Intelligence (AI) is transforming society, including religious practices and experiences of religious communities. The first large-scale AI church service took place in 2023 in Germany as a Protestant Christian worship event within a biannual religious celebration. Studying this religious service illustrates how AI was developed as a religious agent and provides insights into the experiences of attendees. This research note presents data from a quantitative survey and two qualitative questionnaires of participants conducted at the AI worship service. Findings show diversity of opinion and a range of attendee experiences. By presenting the results of qualitative and quantitative analysis, this research note highlights the possibilities and limitations of incorporating AI into the religious sphere. While most of the responses were skeptical, participants also reported having spiritual experiences. Nevertheless, attendees also reported conflicting feelings regarding experiences of community within an artificial setting. The main concerns were technological limitations, fear of replacing humans, biases in the theology of the underlying large language model, and lack of personality and emotion. Age-based differences include younger attendees finding the AI service more attractive while older attendees found the AI service more stimulating. Implications are drawn for practical theology and AI implementation within religious settings.

Perceptions and attitudes toward artificial intelligence among frontline physicians and physicians' assistants in Kansas: a cross-sectional survey.

This survey aims to understand frontline healthcare professionals' perceptions of artificial intelligence (AI) in healthcare and assess how AI familiarity influences these perceptions. We conducted a survey from February to March 2023 of physicians and physician assistants registered with the Kansas State Board of Healing Arts. Participants rated their perceptions toward AI-related domains and constructs on a 5-point Likert scale, with higher scores indicating stronger agreement. Two sub-groups were created for analysis to assess the impact of participants' familiarity and experience with AI on the survey results. From 532 respondents, key concerns were Perceived Communication Barriers (median = 4.0, IQR = 2.8-4.8), Unregulated Standards (median = 4.0, IQR = 3.6-4.8), and Liability Issues (median = 4.0, IQR = 3.5-4.8). Lower levels of agreement were noted for Trust in AI Mechanisms (median = 3.0, IQR = 2.2-3.4), Perceived Risks of AI (median = 3.2, IQR = 2.6-4.0), and Privacy Concerns (median = 3.3, IQR = 2.3-4.0). Positive correlations existed between Intention to use AI and Perceived Benefits (r = 0.825) and Trust in AI Mechanisms (r = 0.777). Perceived risk negatively correlated with Intention to Use AI (r = -0.718). There was no difference in perceptions between AI experienced and AI naïve subgroups. The findings suggest that perceptions of benefits, trust, risks, communication barriers, regulation, and liability issues influence healthcare professionals' intention to use AI, regardless of their AI familiarity. The study highlights key factors affecting AI adoption in healthcare from the frontline healthcare professionals' perspective. These insights can guide strategies for successful AI implementation in healthcare.

Artificial Intelligence in Organizations in Colombia

The implementation of Artificial Intelligence (AI) in organizations in Colombia has been increasing in recent years, and it is expected to continue growing in the near future. Companies in Colombia have begun to use AI in different areas, such as data analysis, customer service, process automation, and decision-making. Among the strategies applied for the implementation of AI in organizations is the adoption of cloud technologies, the use of machine learning algorithms, collaboration with technology providers and the formation of specialized teams. However, the implementation of AI in organizations has also raised concerns regarding the impact on the employability of people. Although AI is expected to improve efficiency and productivity, some tasks and jobs are also expected to be damaged by automation. It is necessary that companies in Colombia adopt policies and strategies that allow a fair transition towards the implementation of AI, to minimize the negative impact on employability and to take advantage of the benefits that this technology can offer.

Implementation of Artificial Intelligence in Quality Management in SMEs: Benefits and Challenges

The adoption of artificial intelligence (AI) in small and medium-sized enterprises (SMBs) has emerged as a key strategy for improving quality management. This article discusses the main benefits and challenges of implementing AI in this context. A systematic review of the academic literature and relevant business reports was carried out, in order to collect and analyze empirical evidence on the application of AI in the quality management of SMEs, obtaining as results that the main benefits of the implementation of AI in the quality management of SMEs include: Improved data-driven decision-making, process automation and error reduction, early detection of quality issues, customization of products and services, and resource optimization and cost reduction. On the other hand, the main challenges were: Lack of knowledge and skills in AI, high implementation costs, concerns about data security and privacy, resistance to cultural change, and integration of AI with existing systems and processes, concluding that the implementation of AI in SMB quality management offers significant benefits, but it also entails challenges that must be addressed strategically. SMEs that manage to overcome these challenges will be able to take advantage of the competitive advantages offered by AI, improving their efficiency, quality and adaptability in an increasingly dynamic business environment.

Advancing Patient Safety: The Future of Artificial Intelligence in Mitigating Healthcare-Associated Infections: A Systematic Review.

Healthcare-associated infections are infections that patients acquire during hospitalization or while receiving healthcare in other facilities. They represent the most frequent negative outcome in healthcare, can be entirely prevented, and pose a burden in terms of financial and human costs. With the development of new AI and ML algorithms, hospitals could develop new and automated surveillance and prevention models for HAIs, leading to improved patient safety. The aim of this review is to systematically retrieve, collect, and summarize all available information on the application and impact of AI in HAI surveillance and/or prevention. We conducted a systematic review of the literature using PubMed and Scopus to find articles related to the implementation of artificial intelligence in the surveillance and/or prevention of HAIs. We identified a total of 218 articles, of which only 35 were included in the review. Most studies were conducted in the US (n = 10, 28.6%) and China (n = 5; 14.3%) and were published between 2021 and 2023 (26 articles, 74.3%) with an increasing trend over time. Most focused on the development of ML algorithms for the identification/prevention of surgical site infections (n = 18; 51%), followed by HAIs in general (n = 9; 26%), hospital-acquired urinary tract infections (n = 5; 9%), and healthcare-associated pneumonia (n = 3; 9%). Only one study focused on the proper use of personal protective equipment (PPE) and included healthcare workers as the study population. Overall, the trend indicates that several AI/ML models can effectively assist clinicians in everyday decisions, by identifying HAIs early or preventing them through personalized risk factors with good performance. However, only a few studies have reported an actual implementation of these models, which proved highly successful. In one case, manual workload was reduced by nearly 85%, while another study observed a decrease in the local hospital's HAI incidence from 1.31% to 0.58%. AI has significant potential to improve the prevention, diagnosis, and management of healthcare-associated infections, offering benefits such as increased accuracy, reduced workloads, and cost savings. Although some AI applications have already been tested and validated, adoption in healthcare is hindered by barriers such as high implementation costs, technological limitations, and resistance from healthcare workers. Overcoming these challenges could allow AI to be more widely and cost-effectively integrated, ultimately improving patient care and infection management.

AI in Surgery: Navigating Trends and Managerial Implications Through Bibliometric and Text Mining Odyssey.

Background:This research employs bibliometric and text-mining analysis to explore artificial intelligence (AI) advancements within surgical procedures. The growing significance of AI in healthcare underscores the need for healthcare managers to prioritize investments in this technology. Purpose:To assess the increasing impact of AI on surgical practices through a comprehensive analysis of scientific literature, providing insights that can guide managerial decision-making in adopting AI solutions.Research Design:The study analyzes over 6000 scientific articles published since 1990 to evaluate trends and contributions in the field, informing managers about the current landscape of AI in surgery.Study Sample:The research focuses on publications from various influential publishers across North America, Northern Asia, and Eastern & Western Europe, highlighting key markets for AI implementation in surgical settings.Data Collection and Analysis:A bibliometric approach was utilized to identify key contributors and influential journals. At the same time, text-mining techniques highlighted significant keywords related to AI in surgery, aiding managers in recognizing essential areas for further exploration and investment.Results:The year 2022 marked a significant upsurge in publications, indicating widespread AI integration in healthcare. The U.S. emerged as the foremost contributor, followed by China, the UK, Germany, Italy, the Netherlands, and India. Key journals, such as Annals of Surgery and Spine Journal, play a crucial role in disseminating research findings, serving as valuable resources for managers seeking to stay informed.Conclusions:The findings underscore AI's pivotal role in enhancing diagnostic precision, predicting treatment outcomes, and improving operational efficiency in surgical practices. This progress represents a significant milestone in modern medical science, paving the way for intelligent healthcare solutions and further advancements in the field. Healthcare managers should leverage these insights to foster innovation and improve patient care standards.

Neural networks utilization in the video game industry

The scope of the video game industry and the use of neural networks in it have been studied. The impact of this technology on the computer games development process has been analyzed, the efficiency of its application in this area noted. The generalized scheme of video games development with identification of the stages most favorable for neural networks application has been considered. The possibilities of artificial intelligence in the video game characters’ visual image development, starting from the idea stage, touching the concept creation and ending with the realization stage, have been studied. The possibility of optimizing this process by implementing neural network technology has been shown. The neural networks application in the processes of writing game scripts and implementing dialog voicing in a game has been analyzed. The difference between the variability of plots and conversations of game characters created with the use of artificial intelligence model has been shown. The future concept of using neural networks as a digital twin of a video game developer has been considered. Estimates of the video game industry development through the generative artificial intelligence implementation have been formulated. As the study result, the positive influence of neural networks application on the computer games development up to the possible full automation of the industry under consideration due to the active use of this technology in the near future has been proved.

Challenges and Opportunities in the Implementation of AI in Manufacturing: A Bibliometric Analysis

This study explores the evolution and impact of research on the challenges and opportunities in the implementation of artificial intelligence (AI) in manufacturing between 2019 and August 2024. By addressing the growing integration of AI technologies in the manufacturing sector, the research seeks to provide a comprehensive view of how AI applications are transforming production processes, improving efficiency, and opening new business opportunities. A bibliometric analysis was conducted, examining global scientific production, influential authors, key sources, and thematic trends. Data were collected from Scopus, and a detailed review of key publications was carried out to identify knowledge gaps and unresolved research questions. The results reveal a steady increase in research related to AI in manufacturing, with a strong focus on automation, predictive maintenance, and supply chain optimization. The study also highlights the dominance of certain institutions and key authors driving this field of research. Despite the progress, significant challenges remain, particularly regarding the scalability of AI solutions and ethical considerations. The findings suggest that while AI holds considerable potential for the manufacturing industry, more interdisciplinary research is needed to address existing gaps and maximize its benefits.

Мониторинг готовности будущих экономистов к использованию искусственного интеллекта в банковской сфере

In the context of the digital transformation of modern society, the study of the problem of introducing artificial intelligence into all spheres of life, including the sphere of economics and finance, is extremely relevant. The purpose of this article is an attempt to analyze the readiness of future economists in the school-university system to use artificial intelligence in the context of developing digital financial literacy. The authors studied the nature of the influence of artificial intelligence on the formation of basic competencies necessary in the era of digitalization of economic education, through the prism of the views of the younger generation. The importance of various aspects of the digital transformation of society, which are in the focus of attention of global cooperation organizations (UNESCO, World Bank, etc.) in the interests of sustainable development, is noted. Using Google-Forms tools, a sociological survey was conducted among students of the Financial University under the Government of the Russian Federation (Omsk branch) and students of specialized economic classes at Gymnasium 19 in the city of Omsk on the topic “The attitude of future economists to the use of artificial intelligence in the banking sector.” The results of the analysis of the obtained empirical data made it possible to state the complex and ambiguous nature of the influence of artificial intelligence on the young generation of future economists in the conditions of total digitalization. Along with the positive attitude of young people towards the prospects for the introduction of artificial intelligence, the risks of a decrease in natural intelligence against the backdrop of digitalization reaching all spheres of life have been identified. This, in turn, requires the formation of value guidelines and the development of a strategy for the continuous development of future economists in the digital society in the context of the active implementation of artificial intelligence.

LSTM Gate Disclosure as an Embedded AI Methodology for Wearable Fall-Detection Sensors

In this paper, the concept of symmetry is used to design the efficient inference of a fall-detection algorithm for elderly people on embedded processors—that is, there is a symmetric relation between the model’s structure and the memory footprint on the embedded processor. Artificial intelligence (AI) and, more particularly, Long Short-Term Memory (LSTM) neural networks are commonly used in the detection of falls in the elderly population based on acceleration measures. Nevertheless, embedded systems that may be utilized on wearable or wireless sensor networks have a hurdle due to the customarily massive dimensions of those networks. Because of this, the algorithms’ most popular implementation relies on edge or cloud computing, which raises privacy concerns and presents challenges since a lot of data need to be sent via a communication channel. The current work proposes a memory occupancy model for LSTM-type networks to pave the way to more efficient embedded implementations. Also, it offers a sensitivity analysis of the network hyper-parameters through a grid search procedure to refine the LSTM topology network under scrutiny. Lastly, it proposes a new methodology that acts over the quantization granularity for the embedded AI implementation on wearable devices. The extensive simulation results demonstrate the effectiveness and feasibility of the proposed methodology. For the embedded implementation of the LSTM for the fall-detection problem on a wearable platform, one can see that an STM8L low-power processor could support a 40-hidden-cell LSTM network with an accuracy of 96.52%.

Implementation of Artificial Intelligence on Personnel Management in a Dynamic Society

The bedrock of this study is Implementation of Artificial Intelligence (AI) on Personnel Management in a Dynamic Society.. The rapid development and advancement of the digital environment, the increase in computing power, the processing of huge amounts of data and advanced algorithms have not only made AI exceptional and indispensable, but also emerged as a key tool in talent management. Methodologically, the study used secondary data collection sources such as textbooks, magazines, newspapers, journals, and periodical reports. The study found, among other things, that the development of AI leads to errors caused by machines due to programming errors and persistence of discrimination/preferential treatment in recruitment. Therefore, AI is not able to recognize corporate culture and values when evaluating applicants. The paper recommends that companies should focus on updating and training their current employees to avoid job losses due to the emergence of AI and adapt to the current framework conditions, etc. Thus, the paper concludes that the increasing use of AI will have a social and economic impact in this millennium, as it aims at digitalization and development by utilizing AI-inspired decision-making and integrating advanced information systems into multidimensional areas of activity, with a significant impact on various sectors around the world.

Implementation of Artificial Intelligence in Retinopathy of Prematurity Care: Challenges and Opportunities.

The diagnosis of retinopathy of prematurity (ROP) is primarily image-based and suitable for implementation of artificial intelligence (AI) systems. Increasing incidence of ROP, especially in low and middle-income countries, has also put tremendous stress on health care systems. Barriers to the implementation of AI include infrastructure, regulatory, legal, cost, sustainability, and scalability. This review describes currently available AI and imaging systems, how a stable telemedicine infrastructure is crucial to AI implementation, and how successful ROP programs have been run in both low and middle-income countries and high-income countries. More work is needed in terms of validating AI systems with different populations with various low-cost imaging devices that have recently been developed. A sustainable and cost-effective ROP screening program is crucial in the prevention of childhood blindness.

Strengths, Weaknesses, Opportunities, and Threats Analysis of Integrating ChatGPT Into Nursing Education

Artificial intelligence (AI) is driving global change, and the implementation of generative AI in higher education is inevitable. AI language models such as the chat generative pre-trained transformer (ChatGPT) hold the potential to revolutionize the delivery of nursing education in the future. Nurse educators play a crucial role in preparing nursing students for a future technology-integrated healthcare system. While the technology has limitations and potential biases, the emergence of ChatGPT presents both opportunities and challenges. It is critical for faculty to be familiar with the capabilities and limitations of this model to foster effective, ethical, and responsible utilization of AI technology while preparing students in advance for the dynamic and rapidly advancing landscape of nursing and healthcare. Therefore, this article was written to present a strengths, weaknesses, opportunities, and threats (SWOT) analysis of integrating ChatGPT into nursing education, providing a guide for implementing ChatGPT in nursing education and offering a well-rounded assessment to help nurse educators make informed decisions.

AI In Enhancing Human-Machine Interaction In Manufacturing

This paper discusses the impact of Artificial Intelligence (AI) on human-machine interaction (HMI) in manufacturing while covering various AI technologies, such as Augmented Reality (AR) and Machine Learning (ML), also their role in improving Operational efficiency, Error reduction and Cost savings. Along with detailed analysis of quantitative data, it addresses comparative review of AI technologies, ethical considerations, implementation challenges and practical case studies to show real-world applications as well. It concludes with future directions and some recommendations for further research