AI Applications Research Articles

Trend Analysis and Research Opportunities in Artificial Intelligence Ethics for Fraud Detection

This study provides a bibliometric analysis of ethical considerations in AI applications for fraud detection based on data from ScienceDirect spanning 2020 to 2024. The analysis identifies “artificial intelligence” as a core focus in the literature, alongside a marked increase in attention to ethical concerns, including data privacy, transparency, and accountability. Additionally, the study reveals progress in applying advanced technologies like blockchain, ChatGPT, and fintech within fraud detection frameworks, which increasingly demand ethical scrutiny. Key findings emphasize the necessity for comprehensive ethical frameworks to ensure transparency, accountability, and public trust in AI-driven fraud detection systems. Practical implications suggest that organizations should prioritize ethical dimensions within AI strategies, enhancing both trust and the effectiveness of detection mechanisms. By using bibliometric analysis, this study finds new trends and gaps in the ethical aspects of using AI to find fraud, which adds new information that hasn’t been fully explored in other studies.

Developing and implementing AI-driven models for demand forecasting in US supply chains: A comprehensive approach to enhancing predictive accuracy

Demand forecasting has long been a critical challenge in the US supply chain operations, plagued by disruptions, fluctuating demand, and price volatility. Developing and implementing AI models that can accurately predict demand is essential in response to these issues. This study aimed to investigate the feasibility of applying machine learning techniques to demand forecasting, particularly in supply chain operations. A comprehensive analysis was conducted using historical data from a logistics company in the USA, which was used to train five traditional demand forecasting methods: Linear Regression, ElasticNet, Random Forest, MLPRegressorn, and XGBoost. Additionally, feature selection, data normalization, and dimensionality reduction techniques were employed to improve the accuracy of these models. Strategic metrics were used to evaluate the model's performance: Random Mean Squared Error, Mean Absolute Error, and R-squared score. The results of this study indicate that AI models have shown significant promise in predicting target sales in supply chains with Linear Regression emerging as the most effective model with the lowest RMSE, MAE, and an R-squared score close to 1. Practical implications of implementing such AI models in the US supply chain include optimized inventory management, reduced costs, and enhanced customer satisfaction. This research contributes to the existing body of knowledge on AI applications in demand forecasting, highlights the importance of traditional methods being supplemented by machine learning techniques, and provides practical recommendations for businesses seeking to improve their supply chain operations.

From Prediction to Explanation: Using Explainable AI to Understand Satellite-Based Riot Forecasting Models

This study investigates the application of explainable AI (XAI) techniques to understand the deep learning models used for predicting urban conflict from satellite imagery. First, a ResNet18 convolutional neural network achieved 89% accuracy in distinguishing riot and non-riot urban areas. Using the Score-CAM technique, regions critical to the model’s predictions were identified, and masking these areas caused a 20.9% drop in the classification accuracy, highlighting their importance. However, Score-CAM’s ability to consistently localize key features was found to be limited, particularly in complex, multi-object urban environments. Analysis revealed minimal alignment between the model-identified features and traditional land use metrics, suggesting that deep learning captures unique patterns not represented in existing GIS datasets. These findings underscore the potential of deep learning to uncover previously unrecognized socio-spatial dynamics while revealing the need for improved interpretability methods. This work sets the stage for future research to enhance explainable AI techniques, bridging the gap between model performance and interpretability and advancing our understanding of urban conflict drivers.

Ethical Guidelines for the Application of Generative AI in German Journalism

Generative Artificial Intelligence (genAI) holds immense potential in revolutionizing journalism and media production processes. By harnessing genAI, journalists can streamline various tasks, including content creation, curation, and dissemination. Through genAI, journalists already automate the generation of diverse news articles, ranging from sports updates and financial reports to weather forecasts. However, this raises ethical questions of high relevance for media organizations and societies especially when genAI is used for more sensitive topics and at larger scale. To not jeopardize trustworthiness in journalistic organizations, it is important that the use of genAI in journalism is guided by moral principles. We therefore conducted 18 interviews with researchers and practitioners with expertise in AI-based technologies, journalism, and ethics from a German perspective in order to identify guidelines for the ethical use of genAI in media organizations. We derived requirements for the ethical introduction of genAI and actionable guidelines which explain how decision makers in media organizations should address ethical principles for the use of AI in the news production life cycle, in order to contribute to trustworthiness of journalistic organizations and products.

Generative Artificial Intelligence Use in Healthcare: Opportunities for Clinical Excellence and Administrative Efficiency

Generative Artificial Intelligence (Gen AI) has transformative potential in healthcare to enhance patient care, personalize treatment options, train healthcare professionals, and advance medical research. This paper examines various clinical and non-clinical applications of Gen AI. In clinical settings, Gen AI supports the creation of customized treatment plans, generation of synthetic data, analysis of medical images, nursing workflow management, risk prediction, pandemic preparedness, and population health management. By automating administrative tasks such as medical documentations, Gen AI has the potential to reduce clinician burnout, freeing more time for direct patient care. Furthermore, application of Gen AI may enhance surgical outcomes by providing real-time feedback and automation of certain tasks in operating rooms. The generation of synthetic data opens new avenues for model training for diseases and simulation, enhancing research capabilities and improving predictive accuracy. In non-clinical contexts, Gen AI improves medical education, public relations, revenue cycle management, healthcare marketing etc. Its capacity for continuous learning and adaptation enables it to drive ongoing improvements in clinical and operational efficiencies, making healthcare delivery more proactive, predictive, and precise.

Applications of AI and machine learning in mining: digitization and future directions

Applications of AI and machine learning in mining: digitization and future directions

Impact of Artificial Intelligence on Comparative Advantages in the Electronics Manufacturing Industry: A Study of China and Vietnam

In recent years, the electronics manufacturing industry has undergone rapid changes, and part of the electronics manufacturing industry is shifting from China to Vietnam. With the development of artificial intelligence, its influence in various manufacturing industries around the world is gradually increasing, especially in the electronics manufacturing industry. This study explores the application of AI in the electronics manufacturing industry in China and Vietnam and how it affects the transformation of the electronics manufacturing industry in China and Vietnam and leads to a shift in the industry chain. The study uses data analysis including sources such as the WTO and Trade Map, and analytical method of RCA index calculation. This article focuses on the research question of the impact of the application of artificial intelligence in the electronics manufacturing industry on the production and export of technology-intensive and labor-intensive electronic products in China and Vietnam and how the impact has facilitated the shifting of electronics manufacturing from China to Vietnam. The results show that AI technologies such as predictive maintenance and quality control for high-tech electronics have given China an advantage in technology-intensive manufacturing, while Vietnam has enhanced its comparative advantage in labor-intensive manufacturing by optimizing its assembly and inventory management through AI technologies.

Value‐sensitive design of chatbots in environmental education: Supporting identity, connectedness, well‐being and sustainability

While offering the potential to support learning interactions, emerging AI applications like Large Language Models (LLMs) come with ethical concerns. Grounding technology design in human values can address AI ethics and ensure adoption. To this end, we apply Value‐Sensitive Design—involving empirical, conceptual and technical investigations—to centre human values in the development and evaluation of LLM‐based chatbots within a high school environmental science curriculum. Representing multiple perspectives and expertise, the chatbots help students refine their causal models of climate change's impact on local marine ecosystems, communities and individuals. We first perform an empirical investigation leveraging participatory design to explore the values that motivate students and educators to engage with the chatbots. Then, we conceptualize the values that emerge from the empirical investigation by grounding them in research in ethical AI design, human values, human‐AI interactions and environmental education. Findings illuminate considerations for the chatbots to support students' identity development, well‐being, human–chatbot relationships and environmental sustainability. We further map the values onto design principles and illustrate how these principles can guide the development and evaluation of the chatbots. Our research demonstrates how to conduct contextual, value‐sensitive inquiries of emergent AI technologies in educational settings. Practitioner notesWhat is already known about this topic Generative artificial intelligence (GenAI) technologies like Large Language Models (LLMs) can not only support learning, but also raise ethical concerns such as transparency, trust and accountability. Value‐sensitive design (VSD) presents a systematic approach to centring human values in technology design. What this paper adds We apply VSD to design LLM‐based chatbots in environmental education and identify values central to supporting students' learning. We map the values emerging from the VSD investigations to several stages of GenAI technology development: conceptualization, development and evaluation. Implications for practice and/or policy Identity development, well‐being, human–AI relationships and environmental sustainability are key values for designing LLM‐based chatbots in environmental education. Using educational stakeholders' values to generate design principles and evaluation metrics for learning technologies can promote technology adoption and engagement.

AI-Enhanced Design and Application of High School Geography Field Studies in China: A Case Study of the Yellow (Bohai) Sea Migratory Bird Habitat Curriculum

China’s Yellow (Bohai) Sea bird habitat is an important ecological region. Its unique ecology and challenges provide rich resources for research and study. Our course design concept is supported by AI technology, and improves students’ abilities through innovative functions such as dynamic data support, personalized learning paths, immersive research and study experience, and diversified evaluation mechanisms. The course content revolves around the “human–land coordination concept”, including pre-trip thinking, research and study during the trip, and post-trip exhibition learning, covering regional cognition, remote sensing image analysis, field investigation, and protection plan display activities. ERNIE Bot participates in optimizing the learning path throughout the process. The course evaluation system starts from the three dimensions of “land to people”, “people to land”, and the “coordination of the human–land relationship”, adopts processes and final evaluation, and uses ERNIE Bot to achieve real-time monitoring, data analysis, personalized reports, and dynamic feedback, improving the objectivity and efficiency of evaluation, and helping students and teachers optimize learning and teaching. However, AI has limitations in geographical research and study, such as insufficient technical adaptability, the influence of students’ abilities and habits, and the adaptation of teachers’ role changes. To this end, optimization strategies such as improving data quality and technical platforms, strengthening student technical training, enhancing teachers’ AI application capabilities, and enriching AI functions and teaching scenarios are proposed to enhance the application effect of AI in geographical research and promote innovation in educational models and student capacity building.

Novel Strategies for the Treatment of Lung Cancer: An In-depth Analysis of the Use of Immunotherapy, Precision Medicine, and Artificial Intelligence to Improve Prognoses.

Therapeutic hurdles persist in the fight against lung cancer, although it is a leading cause of cancer-related deaths worldwide. Results are still not up to par, even with the best efforts of conventional medicine, thus new avenues of investigation are required. Examining how immunotherapy, precision medicine, and AI are being used to manage lung cancer, this review shows how these tools can change the game for patients and increase their chances of survival. In the fight against cancer, immunotherapy has demonstrated encouraging results, especially in cases of small cell lung cancer [SCLC] and non-small cell lung cancer [NSCLC]. A key component in improving T cell responses against tumours is the use of immune checkpoint inhibitors, which include PD-1/PD-L1 and CTLA-4 blockers. Cancer vaccines and CAR T-cell therapy are two examples of adoptive cell therapies that might be used to boost the immune system's ability to eliminate tumours. In order to improve surgical results and decrease recurrence, neoadjuvant immunotherapy is being investigated for its ability to preoperatively reduce tumours. Precision medicine tailors treatment based on individual genetic profiles and tumour features, boosting therapeutic efficacy and avoiding unwanted effects. For certain types of non-small cell lung cancer [NSCLC], targeted treatments based on mutations in genes including EGFR, ALK, and ROS1 have shown excellent results. When it comes to optimizing treatment regimens, biomarker-driven approaches guarantee that the patients most likely to benefit from particular medicines are selected. Artificial intelligence [AI] is revolutionizing lung cancer care through increased diagnostic accuracy, prognostic assessments, and therapy planning. Machine learning algorithms examine enormous information to detect trends and forecast outcomes, permitting individualized treatment techniques. AI-driven imaging tools enable early diagnosis and monitoring of disease progression, while predictive models assist in evaluating therapy responses and potential toxicity. The convergence of these advanced technologies holds promise for overcoming the constraints of conventional therapy. Combining immunotherapy with targeted treatments and utilizing AI for precision medicine delivers a multimodal approach that tackles the heterogeneous and dynamic nature of lung cancer. The incorporation of these new tactics into clinical practice demands cross-disciplinary collaboration and continuing study to develop and confirm their effectiveness. The synergistic application of immunotherapy, precision medicine, and AI constitutes a paradigm shift in lung cancer management. These discoveries provide a robust basis for individualized and adaptable therapy, potentially altering the prognosis for lung cancer patients. Ongoing research and clinical studies are vital to unlocking the full potential of these technologies, paving the way for enhanced therapeutic outcomes and improved quality of life for people battling this tough disease.

Old Threats, New Name? Generative AI and Visual Journalism

ABSTRACT Generative AI applications have been hailed as “transformative,” “disruptive,” and posing a “threat to human journalists and media professionals.” Much of this discourse reflects longstanding concerns about the impact of technological change on both the production and consumption ends of journalism. Perhaps nowhere is this felt more strongly than in visual journalism, where fears about AI replacing cameras and associated implications are rife. These concerns resemble earlier debates about visual technologies, from the smartphone camera to social media, and intersect with fundamental debates about journalism’s boundaries and norms: what news “is,” how it is produced, and what we expect it to achieve. Amidst this hype and anxiety, we offer an analysis of AI’s risks to visual journalism that contextualises this technology against journalism’s existing tensions. Our study asks: How unique are the threats that generative AI poses to visual news? Specifically, we look across academic disciplines to interrogate three threats that are especially prevalent in the literature. Our conceptual evaluation is benefited empirically by dozens of industry perspectives spanning three continents, and allows us to identify exactly which threats, if any, that generative AI poses to visual journalism are new, and which are extant threats folded into more longstanding discourses.

Generative AI and Mobile Learning in Higher Education: Comparing Student and Faculty Perspectives on Employability Impact

Globally, the production of moral citizens and professionals depends heavily on higher education. The advent of ChatGPT and other generative artificial intelligence (GenAI) tools has presented both opportunities and problems for the established educational model. The future of higher education, however, is a topic of little inquiry; instead, discussions these days are mostly focused on policy creation and evaluation. The purpose of this paper is to examine how AI is affecting international students’ education. It uses a research approach, as opposed to a standard literature review, to look at possible applications of AI and talk about related issues. The paper examines several AI applications, including chatbots for research and learning, adaptive testing, personalized learning, and statistical analysis. This study clarifies how AI may enhance learning effectiveness and offer tailored educational support by examining its role in education for international students. It also points out important dangers and constraints that must be properly addressed, such as privacy issues, cultural differences, language barriers, and ethical considerations. Predicting future advancements in AI, however, will be exceedingly challenging.

Application of AI in the whole process of WEEE recycling and reuse

Application of AI in the whole process of WEEE recycling and reuse

Decentralized Federated Learning with Prototype Exchange

As AI applications become increasingly integrated into daily life, protecting user privacy while enabling collaborative model training has become a crucial challenge, especially in decentralized edge computing environments. Traditional federated learning (FL) approaches, which rely on centralized model aggregation, struggle in such settings due to bandwidth limitations, data heterogeneity, and varying device capabilities among edge nodes. To address these issues, we propose PearFL, a decentralized FL framework that enhances collaboration and model generalization by introducing prototype exchange mechanisms. PearFL allows each client to share lightweight prototype information with its neighbors, minimizing communication overhead and improving model consistency across distributed devices. Experimental evaluations on benchmark datasets, including MNIST, CIFAR-10, and CIFAR-100, demonstrate that PearFL achieves superior communication efficiency, convergence speed, and accuracy compared to conventional FL methods. These results confirm PearFL’s efficacy as a scalable solution for decentralized learning in heterogeneous and resource-constrained environments.

Etika Kecerdasan Artifisial (AI): Sebuah Eksplorasi Metaetika atas Kebijakan Regulasi AI di Indonesia dan Internasional

This article examines the regulation of AI development and usage at the national and international levels using a metaethical approach. The defended thesis is that AI cannot be positioned as a moral agent because it lacks the feature of consciousness (an epistemological problem). The subjective reality formed by human thought and consciousness differs from the reality formed by AI's computational processes (an ontological problem). This article is an articulation of research categorized as a literature study with qualitative data analysis, referring to key documents on AI ethics at the national and international levels. The conclusion drawn is that all AI actions are subordinate to human actions and are therefore never value-free. AI is not a moral agent, and the regulation of AI development, application, and usage requires an ethical framework that can accommodate moral progress in a manner that aligns linearly with the rapid innovation progress of AI over time. In other words, AI regulation must operate within a dialectical framework that allows space for evaluation and re-contextualization to remain relevant to the dynamics of social and cultural realities.

Emotional analysis of operating room nurses in acute care hospitals in Japan: insights using ChatGPT

AimThis study aimed to explore the emotions of operating room nurses in Japan towards perioperative nursing using generative AI and human analysis, and to identify factors contributing to burnout and turnover.MethodsA single-center cross-sectional study was conducted from February 2023 to February 2024, involving semi-structured interviews with 10 operating room nurses from a national hospital in Japan. Interview transcripts were analyzed using generative AI (ChatGPT-4o) and human researchers for thematic, emotional, and subjectivity analysis. A comparison between AI and human analysis was performed, and data visualization techniques, including keyword co-occurrence networks and cluster analysis, were employed to identify patterns and relationships.ResultsKey themes such as patient care, surgical safety, and nursing skills were identified through thematic analysis. Emotional analysis revealed a range of tones, with AI providing an efficient overview and human researchers capturing nuanced emotional insights. High subjectivity scores indicated deeply personal reflections. Keyword co-occurrence networks and cluster analysis highlighted connections between themes and distinct emotional experiences.ConclusionsCombining generative AI with human expertise offered nuanced insights into the emotions of operating room nurses. The findings emphasize the importance of emotional support, effective communication, and safety protocols in improving nurse well-being and job satisfaction. This hybrid approach can help address emotional challenges, reduce burnout, and enhance retention rates. Future research with larger and more diverse samples is needed to validate these findings and explore the broader applications of AI in healthcare.

From Prediction to Precision: Explainable AI-Driven Insights for Targeted Treatment in Equine Colic

Colic is a leading cause of mortality in horses, demanding precise and timely interventions. This study integrates machine learning and explainable artificial intelligence (XAI) to predict survival outcomes in horses with colic, using clinical, procedural, and diagnostic data. Random forest and XGBoost emerged as top-performing models, achieving F1 scores of 85.9% and 86.1%, respectively. SHAP (Shapley additive explanations) was employed to provide interpretable insights, offering both global and local explanations for model predictions. The analysis revealed that key features, such as pulse rate, lesion type, and total protein levels, significantly influenced survival likelihood. Local interpretations highlighted the unique contribution of clinical factors to individual cases, enabling personalized insights that guide targeted treatment strategies. These tailored predictions empower veterinarians to prioritize interventions based on the specific conditions of each horse, moving beyond generalized care protocols. By combining predictive accuracy with interpretability, this study advances precision veterinary medicine, enhancing outcomes for equine colic cases and setting a benchmark for future applications of AI in animal health.

AI-Driven Sustainable Marketing in Gulf Cooperation Council Retail: Advancing SDGs Through Smart Channels

This paper explores how AI drives GCC sector retail towards the fulfillment of the UN SDGs. Analyzing a survey conducted on 410 retail executives, using PLS-SEM, this study underlines the role of AI in promoting operational efficiency, waste reduction, and consumer engagement with greener products. Key highlights include that AI-enabled marketing strategies improve the adoption of sustainable practices among consumers; AI-powered smart distribution channels enhance supply chain efficiency, reduce carbon emissions, and optimize logistics. For a retailer, practical applications of AI include the use of AI in demand forecasting to potentially reduce waste, personalized marketing to efficiently promote sustainable products, and deploying smart systems that reduce energy consumption. While these benefits are real, data privacy and algorithmic bias remain valid concerns, thus underlining the need for ethics and transparency in the practice of AI. The following study provides actionable insights for GCC retailers on how to align AI adoption with sustainability goals, fostering competitive advantages and environmental responsibility.

The relationships of personality traits on perceptions and attitudes of dentistry students towards AI

IntroductionArtificial intelligence (AI) has gained significant attention in dentistry due to its potential to revolutionize practice and improve patient outcomes. However, dentists’ views and attitudes toward technology can affect the application of AI. This perception and attitude can be affected by the personality traits of individuals. This study aims to evaluate the perceptions and attitudes of dentistry students toward AI.MethodsThis cross-sectional study was conducted on dental students at Ordu University Faculty of Dentistry, involving a sample of 83 students. The study utilized the Big Five 50 Test to evaluate personality traits and a 5-point Likert scale to gather data on 20 statements regarding AI in dentistry. Data were analyzed using IBM SPSS Statistics software, and a chi-square test was employed to assess the relationship between the personality traits of dental students and their attitudes towards artificial intelligence, as well as the relationship between the gender of dental students and their attitudes towards artificial intelligence. Statistical significance was set at P < 0.05.ResultsThe study involved 83 participants, with 29 male and 54 female participants. The most common personality traits were Openness and Agreeableness, whereas the least common was Extraversion. Participants found AI useful and believed it could help dentists evaluate radiographs. However, the least agreed statement was that they would trust AI more than a dentist in evaluating radiograph results. A statistically significant difference was found between personal traits of dental students and in expressions comparing dentists and AI. Males were more familiar with AI than females.ConclusionThis study found that attitudes towards AI in dentistry vary based on personality traits. Developing educational strategies tailored to these traits can help foster more positive attitudes and improve AI integration into dental practice.

Principle of Responsibility of the Controlling Person as Approach to Eliminating the “Responsibility Gap” for Harm Caused by AI Systems and AI Applications

INTRODUCTION. The use of artificial intelligence technologies (hereinafter referred to as “AI”) is characterized by the mediation of human actions by autonomous processes, which leads, in the case when technical expertise is unable to identify the causer of harm, to a “responsibility gap” is an undesirable legal phenomenon in which the imposition of responsibility for harm caused by the use of AI on a specific person (persons) for the rules of tort liability are impossible.MATERIALS AND METHODS. The research used general scientific and special methods, including the historical method, methods of formal logic, analysis, synthesis, as well as systemic and comparative legal methods.RESEARCH RESULTS. To eliminate the “responsibility gap”, the article proposes a mechanism that allows to fill in the missing elements of a tort committed using AI when the error that led to harm cannot be attributed de lege lata to any participant in the life cycle of an AI system or application. The starting point for the development of this mechanism was the theory of “guidance control” over the use of AI. A legal understanding of the philosophical foundations of the theory of “guidance control” allows us to substantiate the general legal principle of allocating responsibility for harm caused by AI, according to which the legal responsibility is borne by the person obliged to exercise human control over the use of the AI system or application, unless other perpetrators are identified. This principle is gradually being accepted by the international legal doctrine, which is expressed in the designation of the need to control the use of AI in a number of international documents.CONCLUSIONS. Provided that the protocol to the Treaty on the EAEU enshrines the general legal principle of responsibility of the controlling person for harm caused by AI, it can acquire the significance of a regional international legal principle, and thereby become the basis for the formation of regulatory regulation in the EAEU of the distribution of responsibility for harm caused by AI. The proposed toolkit is convenient for legal consolidation through supranational legal regulation.