AI Applications Research Articles

Artificial Intelligence in Emergency Medicine: A Literature Review

IntroductionArtificial intelligence (AI) is rapidly transforming medical fields, particularly emergency medicine (EM), where timely decision-making is crucial. AI offers potential benefits in diagnostic accuracy, patient care optimization, and workflow efficiency within emergency departments (EDs). Purpose of WorkThis review aims to synthesize recent advancements in AI applications within emergency medicine, focusing on diagnostic support, patient triage, clinical decision support systems (CDSS), and workflow optimization. Additionally, we highlight the potential benefits, challenges, and future directions for AI in EM. Material and MethodsA comprehensive literature search was conducted using PubMed and Google Scholar databases. We reviewed peer-reviewed articles from 2008 to 2024, focusing on AI-driven solutions in EDs. Keywords included "artificial intelligence," "emergency medicine," "machine learning," and "clinical decision support." Studies were selected based on their relevance to AI applications in EM, diagnostic improvements, and operational efficiency. The results highlight the promising role of AI in improving diagnostic accuracy, reducing overcrowding, optimizing triage processes, and addressing clinician workload. However, challenges like ethical concerns, data bias, and the need for clinical validation remain. Further research is necessary to integrate AI more effectively in clinical practice.

Exploring the application of LLM-based AI in UX design: an empirical case study of ChatGPT

Exploring the application of LLM-based AI in UX design: an empirical case study of ChatGPT

The Impact of AI on the Development of Manufacturing, Logistics, and Financial Company from Capital Market Perspectives

Abstract: This paper explores the impact of AI technology on enterprises, especially in finance and capital markets. With the rapid development of AI technology, enterprises hope to improve productivity, financial management, and market competitiveness through AI, and ultimately achieve sustainable development. The article aims to analyze the application cases of AI in head enterprises and their effects and put forward corresponding suggestions and insights. By analyzing case studies of enterprises such as Tesla, Amazon and JP Morgan, it elaborates on the application of AI in various areas such as enterprise manufacturing, supply chain management, automated trading, risk management and customer service. It reveals the great potential of AI in improving enterprise operational efficiency, reducing costs and optimizing decision-making. By analyzing the above cases, it is suggested that enterprises should strengthen data privacy and security management and improve technology transparency and interpretability when introducing AI technology. The study reveals that AI technology has potential but also faces challenges, and enterprises need to keep overcoming obstacles to achieve the wide application and deep integration of AI technology.

The evolution of creativity: how generative AI is reshaping the hospitality landscape

ABSTRACT This study examines the application of generative AI (GEN-AI) in the hotel industry, using the TOE model to analyze how technological, organizational, and environmental factors influence GEN-AI adoption. Findings reveal that GEN-AI enhances room design, service quality, and marketing innovation, while increasing organizational flexibility and adapting employee roles.

Artificial Intelligence Research in Radiation Oncology: A Practical Guide for the Clinician on Concepts and Methods

Abstract The use of AI holds great promise for radiation oncology, with many applications being reported in the literature, including some of which are already in clinical use. These are mainly in areas where AI provides benefits in efficiency (such as automatic segmentation and treatment planning). Prediction models that directly impact patient decision-making are far less mature in terms of their application in clinical practice. Part of the limited clinical uptake of these models may be explained by the need for broader knowledge, among practising clinicians within the medical community, about the processes of AI development. This lack of understanding could lead to low commitment to AI research, widespread scepticism, and low levels of trust. This attitude towards AI may be further negatively impacted by the perception that deep learning is a “black box” with inherently low transparency. Thus, there is an unmet need to train current and future clinicians in the development and application of AI in medicine. Improving clinicians' AI-related knowledge and skills is necessary to enhance multidisciplinary collaboration between data scientists and physicians, ie, involving a clinician in the loop during AI development. Increased knowledge may also positively affect the acceptance and trust of AI. This paper describes the necessary steps involved in AI research and development, and thus identifies the possibilities, limitations, challenges, and opportunities, as seen from the perspective of a practising radiation oncologist. It offers the clinician with limited knowledge and experience in AI valuable tools to evaluate research papers related to an AI model application.

Advanced Cyber Threats and Cybersecurity Innovation - Strategic Approaches and Emerging Solutions

Given that the size and complexity of digital ecosystems are constantly growing, cybersecurity is constantly under attack by increasingly complex threats ranging from ransomware attacks to cyber-attacks based on artificial intelligence. Thus, this paper focuses on these new generation cyber threats and assesses the old and advanced security technologies that address them. This study addresses the future of advanced threats and emerging solutions deep diving into the application of AI, blockchain and the use of Zero Trust architectural concepts in mitigating cybersecurity risks. Moreover, a number of strategic measures – such as dynamic threat identification, multiple-tier protection systems, and sound data security programs – are discussed to enhance the organizational defense against possible weaknesses. Results show that it is crucial for companies to adopt an amalgamated model that incorporates innovative technologies together with active cybersecurity measures. In turn, this paper suggests directions for further research and policy implications for enhancing a current and relevant cybersecurity framework.

A INTELIGÊNCIA ARTIFICAL COMO FERRAMENTA DE EFICIÊNCIA NA ADMINISTRAÇÃO PÚBLICA

The article “Artificial Intelligence as a Tool for Efficiency in Public Administration” explores the growing role of AI in optimizing government operations and improving the delivery of public services. Focusing on AI’s capabilities to process and analyze large volumes of data, the article discusses how this technology can be used to enhance efficiency, transparency, and responsiveness in government services. The text highlights several key applications of AI in public administration, including large-scale data analysis and processing, automation of bureaucratic processes, and customization of services to citizens. These technologies, equipped with machine learning and natural language processing, enable better resource management, faster citizen service, and more efficient operations less susceptible to human errors. Beyond the benefits, the article also addresses the challenges associated with implementing AI in the public sector, such as technical, ethical, and privacy issues. Concerns about algorithmic bias, data quality, and integration with legacy systems are discussed in detail. The article emphasizes the need for robust policies and adequate training for public employees as essential for successful AI integration. Finally, the discussion concludes with the view that while AI has the potential to significantly transform public administration for the better, a careful and regulated approach is vital. The success of this integration depends not only on technological advancements but also on a commitment to ethical practices and the development of public policies that guide its responsible use.

Cyber Attacks Detection And Mitigation Using Machine Learning In Smart Grid Systems

The increasing complexity and interconnectedness of smart grid systems have heightened their vulnerability to cyber threats, necessitating advanced solutions for securing these critical infrastructures. This study aims to explore the potential of AI-driven predictive analytics in enhancing the cybersecurity and operational efficiency of smart grids. By leveraging the systematic approach of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a comprehensive review of 1,526 initial articles was conducted, which was subsequently narrowed down through rigorous screening and evaluation to a final set of 127 high-quality studies. The review reveals that machine learning models, such as neural networks and time series forecasting, significantly enhance the early detection and mitigation of cyber threats, allowing grid operators to take proactive measures to safeguard against disruptions. Additionally, the integration of AI with real-time data analytics was found to optimize load forecasting, predict equipment failures, and improve overall grid resilience, leading to reduced downtime and operational costs. However, significant challenges related to data privacy, scalability, and integration with legacy systems persist. The findings suggest that techniques like federated learning and blockchain integration could address these challenges, though further research is needed to enhance model robustness and efficiency. This review provides critical insights into the practical applications of AI in smart grid cybersecurity, highlighting both the benefits and the barriers that must be overcome to achieve widespread adoption.

AI assistance in enterprise UX design workflows: enhancing design brief creation for designers

The study explores the impact of AI tools on the daily tasks of designers in corporate environments, with a focus on the creation and evaluation processes of design briefs. Given ChatGPT’s advanced natural language processing capabilities and its potential to meet the complex communication and analysis needs of design work, this tool was selected to investigate its application in designers’ workflows. Through expert interviews, experimental testing, and third-party expert evaluations, we collected and analyzed data to understand the impact of AI on work processes. The findings indicate that AI tools significantly enhance both operational experience and subjective perceptions across most tasks. Additionally, the study provides a visual comparison of the testing process through a user experience map, highlighting AI’s positive influence on work efficiency, information retrieval, verification, analysis, communication, and decision-making. However, challenges remain in ensuring information authenticity, protecting content copyright, and maintaining professional identity. The primary objective is to gain a comprehensive understanding of the current state of AI application in business contexts and its impact on designers’ roles. By analyzing real-world feedback, the research aims to identify the strengths and weaknesses of AI solutions in enterprises and offer practical recommendations. The study underscores the importance of integrating AI thinking into workflows and adopting a human-centric approach for the future development of corporate work environments.

Deep Learning and Knowledge

This paper considers the question of what kind of knowledge is produced by deep learning. Ryle’s concept of knowledge how is examined and is contrasted with knowledge with a rationale. It is then argued that deep neural networks do produce knowledge how, but, because of their opacity, they do not in general, though there may be some special cases to the contrary, produce knowledge with a rationale. It is concluded that the distinction between knowledge how and knowledge with a rationale is a useful one for judging whether a particular application of deep learning AI is appropriate.

The Convergence of Artificial Intelligence and Human Marketing: A Framework for Enhanced Customer Insights and Personalization

This article examines the transformative integration of artificial intelligence and human expertise in modern marketing analytics, focusing on customer insight generation and personalization strategies. We present a comprehensive framework for human-AI collaboration in marketing operations through a mixed-methods approach combining quantitative analysis of customer data from 127 retail organizations and qualitative interviews with 34 marketing professionals. The findings reveal that organizations implementing AI-enhanced customer analytics in conjunction with human-driven creative strategy achieved a 47% improvement in customer engagement metrics and a 31% increase in conversion rates compared to traditional approaches. The article demonstrates that while AI excels at real-time pattern recognition and predictive modeling of customer behavior, human marketers provide crucial emotional intelligence and contextual interpretation that significantly enhance the effectiveness of personalization efforts. The article introduces the Dual Intelligence Marketing Framework (DIMF), which outlines optimal integration points between AI capabilities and human expertise throughout the customer journey. The framework addresses critical challenges in implementation, including technical infrastructure requirements, talent adaptation, and ethical considerations. This article contributes to both theoretical understanding and practical application of AI in marketing, offering actionable insights for organizations seeking to leverage the complementary strengths of artificial and human intelligence in their marketing operations.

Optimizing pharmaceutical supply chain management through AI-driven predictive analytics: A conceptual framework

The pharmaceutical supply chain is a complex, multi-layered system that faces unique challenges, including fluctuating demand, stringent regulatory requirements, and logistical constraints. This paper explores the role of AI-driven predictive analytics in optimizing pharmaceutical supply chain management, presenting a conceptual framework that enables companies to leverage advanced data analytics for improved decision-making, risk management, and operational efficiency. Key AI techniques, such as machine learning, data mining, and predictive demand forecasting, are discussed as tools for addressing critical supply chain issues, including inventory optimization, demand variability, and regulatory compliance. The proposed framework details essential components—data collection, processing, integration with existing systems, and real-time decision-making—and outlines critical success factors for effective implementation. By offering insights into the transformative potential of predictive analytics, this paper provides actionable recommendations for pharmaceutical companies seeking to build resilient and responsive supply chains. Future research directions are also proposed, emphasizing the need for adaptable predictive models and the ethical implications of AI applications in pharmaceuticals. Keywords: Pharmaceutical Supply Chain, AI-driven Predictive Analytics, Machine Learning, Demand Forecasting, Inventory Optimization, Regulatory Compliance.

The EU's Artificial Intelligence Act and copyright

AbstractThe European Union's (EU's) Artificial Intelligence Act (AI Act), published on 12 July 2024, seeks to establish a consistent legal framework for AI systems within the EU, promoting trustworthy and human‐centric AI while safeguarding various fundamental rights. The Act classifies AI applications into three risk categories: unacceptable risk, high risk, general purpose AI models with systemic risk and low or no risk, each with corresponding regulatory measures. Although initially not focused on copyright issues, the rise of generative AI led to specific provisions addressing general purpose AI models. These provisions include transparency obligations, particularly regarding the technical documentation and content used for training AI models, and policies to respect EU copyright laws. The Act aims to balance the interests of copyright holders and AI developers, ensuring compliance while fostering innovation and protecting rights.

Building Trustworthy AI: The Role of Patient and Public Involvement in Healthcare AI Development

AI is helping researchers make great strides in healthcare. However, there is a trust deficit in AI when applied to critical areas like healthcare. Hence communicating the beneficial medical applications of AI and engaging the public with healthcare AI research is critical. One way to achieve this is by getting the community involved in co-designing better AI systems in healthcare projects. We argue that AI algorithms for healthcare should be co-designed with patients and healthcare workers, so that they are useful and trustworthy. We suggest a roadmap for this collaborative approach in AI model building. This will involve actively including patients with lived experience of a disease, as well as creating a research advisory group to walk patients through the process of AI model building. We suggest formulating and scoping a problem, and then generating a hypothesis that patients and scientists agree on. The road to building trustworthy AI systems may become easier if all stakeholders are involved in co-creating AI models.

Integrating AI into Agile Workflows: Opportunities and Challenges

Abstract: Over the past few years, Agile development approaches have become an increasingly popular methodology for software engineering, focused on iterative progress, continuous feedback, and teamwork cooperation. However, the raising difficulties of projects and more demand for faster and more efficient workflows have challenged the situation further. The introduction of Artificial Intelligence into the Agile processes paves the way to optimize decision-making, automate routine tasks, and boost team productivity. This review summarizes the innovations and challenges created by the integration of AI into Agile development practices. Using AI technologies like machine learning, predictive analysis, and natural language processing, Agile teams can enhance sprint planning, resource coordination, and risk management. Also, it presents some risks such as data privacy, workforce skills need, and possible over-dependence of AI. The paper emphasizes on providing a full overview of the innovations and challenges to the application of AI in Agile workflows, providing thoughts for future research and practices.

Robustness in deep learning models for medical diagnostics: security and adversarial challenges towards robust AI applications

The current study investigates the robustness of deep learning models for accurate medical diagnosis systems with a specific focus on their ability to maintain performance in the presence of adversarial or noisy inputs. We examine factors that may influence model reliability, including model complexity, training data quality, and hyperparameters; we also examine security concerns related to adversarial attacks that aim to deceive models along with privacy attacks that seek to extract sensitive information. Researchers have discussed various defenses to these attacks to enhance model robustness, such as adversarial training and input preprocessing, along with mechanisms like data augmentation and uncertainty estimation. Tools and packages that extend the reliability features of deep learning frameworks such as TensorFlow and PyTorch are also being explored and evaluated. Existing evaluation metrics for robustness are additionally being discussed and evaluated. This paper concludes by discussing limitations in the existing literature and possible future research directions to continue enhancing the status of this research topic, particularly in the medical domain, with the aim of ensuring that AI systems are trustworthy, reliable, and stable.

Optimizing LLM Strategies for Playing Mendikot using Prompt Engineering

This study investigates the integration of a large language model (LLM) enhanced by prompt engineering and game theory to effectively engage in the strategic card game Mendikot. By refining complex prompts and leveraging a tailored visual understanding of game dynamics, we significantly bolster the decision-making prowess of the LLM. Our methodology involved the systematic simplification of game prompts to facilitate deeper learning and faster response times, coupled with the implementation of a visual recognition system to interpret and react to game states dynamically. The results illustrate that the adapted LLM outperforms traditional AI approaches in strategic decision-making tasks, underscoring a substantial improvement in both the accuracy and efficiency of game-play. This research not only demonstrates a viable model for enhancing AI interaction in recreational gaming but also opens avenues for deploying advanced AI strategies in complex strategic environments, offering insights into the broader application of AI in leisure and competitive arenas. The findings suggest that AI can transcend conventional gaming roles, potentially transforming strategic gameplay in digital and physical platforms.

Explainable Artificial Intelligence (XAI) in Healthcare: Enhancing Transparency and Trust

Artificial Intelligence has now taken a full-fledged role in healthcare and has started driving innovations not only in diagnostics and treatment planning but also in patient monitoring and operational efficiency. This will enable complex medical data analysis, extracting patterns and insights that no human is capable of. However, most of these models are per se opaque-that is, the so-called "black-box" problem-there are still great challenges in areas such as transparency, trust, and ethical applications in a clinical setting. This lack of interpretability can stand in the way of acceptance or integration for AI technologies when issues of understanding and accountability are relevant. Explainable AI solves these problems by making real artificial intelligence decisions understand the decisions made to humans. XAI techniques offer well-understandable and interpretable explanations of the models with minimum degradations in performance. This review article explains in detail the critical role of XAI in healthcare, underpinning how this field can bring more transparency into AI applications. We explain some of the current methods of XAI: model-agnostic techniques like LIME and SHAP, interpretable models relating to decision trees and linear models, and visualization techniques like saliency maps and mechanisms of attention.

AI in business research

AbstractArtificial intelligence (AI) has emerged as a pivotal force in modern business transformation, garnering widespread attention from both practitioners and academics. With a notable exponential increase in AI‐related studies, we provide a research framework aiming to synthesize the existing literature on AI in the business field. We conduct a comprehensive review of AI research spanning from 2010 to 2023 in 25 leading business journals according to this review framework. Specifically, we review the literature from three research perspectives: (i) AI applications, (ii) human perceptions of AI, and (iii) AI behavior. We also identify five principal research questions and offer suggestions for future research directions.

The Evolution of Identity and Access Management: Integrating Biometric and Behavioral Authentication

This article explores the transformative role of biometric and behavioral authentication in the evolving landscape of Identity and Access Management (IAM). As traditional password-based systems increasingly fail to meet the challenges posed by sophisticated cyber threats, these advanced authentication methods offer a promising solution. The article provides a comprehensive overview of physical and behavioral biometrics, examining their integration into multi-factor authentication systems and their capacity for continuous, passive user verification. It delves into the significant security benefits these technologies offer, including enhanced resistance to phishing, social engineering, and insider threats, while also highlighting the improvements in user experience and convenience. The article critically analyzes the challenges and considerations associated with implementing biometric and behavioral authentication, including privacy concerns, data security, accuracy issues, and scalability. Furthermore, it explores future directions and research opportunities in the field, such as advancements in biometric technologies, the application of machine learning and AI in behavioral analysis, and the development of context-aware authentication systems. By synthesizing current research and industry trends, this article provides valuable insights for cybersecurity professionals, IT decision-makers, and researchers interested in the future of secure and user-friendly authentication methods. It underscores the potential of biometric and behavioral authentication to significantly enhance organizational security postures while improving user satisfaction in an increasingly digital world.