The Impact of Artificial Intelligence on the Legal System: Benefits, Risks, Legal, and Ethical Challenges

This article addresses one of the pressing issues regarding the role of artificial intelligence (AI) in international relations and international law. The research question revolves around defining the theoretical and methodological approaches applicable to the strategic analysis of AI utilization in these fields. In the contemporary world, there is a demand at both interstate and societal levels to define the role of AI in the political and legal spheres. This is because AI development affects crucial areas of state relations such as security, international law, ethical norms, and dependencies. The prospective use of AI technologies without corresponding legal regulation may disrupt the already fragile balance of the world order, which could be exacerbated by state competition in AI technologies and AI applications in the military domain, a grey area in international law. Analyzing this issue from the perspective of international relations and international law theory allows for examining AI's impact on state interactions and developing new application strategies. Similarly, it helps understand how international law regulates state relations, including aspects related to AI applications. By examining various theoretical concepts and methodological approaches necessary for understanding AI's impact on global affairs, including its influence on diplomacy, security, and governance structures, as well as legal and ethical issues, this article contributes to Kazakhstan's evolving discourse on AI governance and its implications for state actors.

Interface of Law and Psychiatric Problems in the Elderly.

This article explores the transformation of digital culture through the in fluence of artificial intelligence (AI), focusing on how these technologies are changing the way individuals and communities interact with media, technology, and social policy. AI, increasingly integrated in various aspects of everyday life, affects forms of social interaction, information distribution, as well as identity and cultural formation. Through the analysis of various current concepts, theories, and case studies. This article identifies AI's key role in creating more per sonal and directed digital experiences, while exploring emerging ethical and social challenges. The research combines theories of social construction of technology, posthumanism, and social criticism of technology to provide a deeper understanding of AI's impact on contemporary society and culture. Key findings suggest that while AI offers great potential in improving accessibility and efficiency, the use of these technologies can also worsen social inequality and increase control of surveillance. This article suggests the need for more rigorous regulations to regulate AI use and optimize its positive impact, as well as the need to continue to develop an understanding of the interaction between humans and machines in social and cultural contexts. This research contributes to critical thinking about AI's role in shaping digital culture and provides direction for furthers research as well as more inclusive and ethical policies in the development of these technologies..

Purpose: This paper examines the evolving impact of artificial intelligence (AI) on management consulting through both bibliometric and systematic review. Most of the authors have used either bibliometric or SLR in the past. It aims to provide insights into AI’s influence on consulting practices, highlighting key themes such as operational efficiency, human-AI collaboration, and ethical challenges. Design/Methodology/Approach: A bibliometric analysis of 195 scholarly publications (2010– 2024) was conducted using VOSviewer to map research trends, thematic clusters, and collaborative networks. A systematic literature review was also performed on a subset of 37 studies to extract more profound insights into AI's role in consulting. Findings: The analysis identifies a significant increase in AI-related consulting research post 2018. Dominant themes include AI-driven decision-making, consultant client dynamics, and sustainability. Emerging trends emphasize the integration of AI into strategic decision-making and the development of frameworks for ethical AI governance. Originality/Value: This work combines systematic review methods with bibliometric visualization to provide a new synthesis of research on AI in management consulting. It fills critical knowledge gaps by exploring AI's long-term strategic influence on consulting practices, its role in promoting sustainability, and the development of consultant competencies. Additionally, this study addresses underexplored areas such as the ethical challenges of AI integration, interdisciplinary approaches to understanding human-AI collaboration, and regional disparities in AI adoption, particularly in emerging markets. By bridging these gaps, the paper offers actionable insights for practitioners and a robust foundation for future academic inquiry. Practical and Social Implications: The findings provide consulting professionals with actionable strategies for adopting AI responsibly, fostering innovation, and addressing ethical and societal challenges. The study underscores the need for sustainable AI practices that align with global goals.

Revisting Health Information Technology Ethical, Legal, and Social Issues and Evaluation: Telehealth/Telemedicine and COVID-19

Revolutionizing diagnostic pathology: The emergence and impact of artificial intelligence—what doesn't kill you makes you stronger?

Background: Artificial Intelligence (AI) is increasingly being integrated into the criminal justice system, promising to enhance efficiency, accuracy, and decision-making. However, the use of AI also raises significant ethical and legal challenges, including concerns about bias, fairness, transparency, and accountability. These challenges necessitate a thorough examination of AI's impact on the criminal justice system to ensure its benefits are realized without compromising ethical and legal standards. Objective: This study aims to evaluate the impact of AI on the criminal justice system, focusing on the ethical and legal challenges it presents. The research seeks to understand how AI technologies are being implemented, their effects on decision-making processes, and the extent to which they adhere to ethical and legal principles. The goal is to identify best practices and propose solutions to mitigate potential risks. Methods: A mixed-methods approach was employed, combining quantitative surveys and qualitative interviews. Quantitative data were collected from 250 criminal justice professionals, measuring their perceptions of AI's impact on various aspects of the justice system. Qualitative interviews with 40 key stakeholders provided deeper insights into the ethical and legal challenges associated with AI integration. Data were analyzed using statistical methods for the surveys and thematic analysis for the interviews. Results: Findings indicate that AI can significantly enhance the efficiency and accuracy of the criminal justice system but also presents substantial ethical and legal challenges. Issues such as algorithmic bias, lack of transparency, and accountability were frequently highlighted. Best practices identified include implementing rigorous validation processes, ensuring transparency in AI decision-making, and establishing clear accountability frameworks. Conclusion: While AI holds significant promise for improving the criminal justice system, addressing ethical and legal challenges is crucial for its successful integration. Implementing best practices can mitigate risks and ensure that AI technologies are used responsibly. Future research should focus on developing comprehensive guidelines and policies to govern the use of AI in the criminal justice system.

Guidelines for Adolescent Health Research*1, *2A position paper of the Society for Adolescent Medicine

Emergency medical care effectively embarks on new frontiers to augment the levels of diagnostic accuracy, complexity of treatment options, and rapidity of interventions in the critical period. This will compose an approach that the machine can cater to extensive volumes of data very fast, providing its recommendations almost instantaneously and based on the best research evidence, thus improving patient outcome characteristics during on-call situations. In this respect, integrating AI within this domain raises moral, legal, and social issues; all of which will have to be addressed to ensure that the AI is both safe and efficient. Some of the advantages that may result from the provision of AI included within emergency care are: assisting healthcare workers in fast and accurate decision making, resource allocation and optimization and provision of personalized treatment to every patient. When AI systems make decisions without adequate patient input, it may lead to loss in the process of doctor-patient interaction with increased reliance on technology. The privacy and confidential information emerge to a certain extent in connection to the secure management of sensitive patient information by AI systems. Some of the key issue is that the legal complications in emergencies are precarious in nature because there may not always be time to obtain informed consent, which brings into question the respect for patient autonomy and accountability. It brings out a holistic approach in handling such issues and enables the easy setting of liability frameworks that are responsible and well-balanced.

Current ethical and social issues in epidemiology.

Artificial intelligence (AI) is revolutionizing nursing by enhancing decision-making, patient monitoring, and efficiency. Machine learning, natural language processing (NLP), and predictive analytics claim to improve safety and automate tasks. However, a structured analysis of AI applications is necessary to ensure their effective implementation in nursing practice. This umbrella review aimed to synthesize existing systematic reviews on AI applications in nursing care, providing a comprehensive analysis of its benefits, challenges, and ethical implications. By consolidating findings from multiple sources, this review seeks to offer evidence-based insights to guide the effective and responsible integration of AI in nursing practice. A systematic umbrella review approach was employed following PRISMA guidelines. Multiple databases, including PubMed, CINAHL, Scopus, Web of Science, and IEEE Xplore, were searched for review articles published between 2015 and 2024. Findings were synthesized thematically to identify key trends, benefits, limitations, and research gaps. This review synthesized 13 studies, emphasizing AI's impact on clinical decision support, patient monitoring, nursing education, and workflow optimization. AI enhances early disease detection, minimizes diagnostic errors, and automates documentation, improving efficiency. However, data privacy risks, biases, ethical concerns, and limited AI literacy hinder integration. AI presents significant opportunities for improving nursing care, yet its successful implementation requires addressing ethical, legal, and practical challenges. Adequate AI training, robust data governance frameworks, and policies ensuring responsible AI use are essential for its integration into nursing practice. Future research should explore long-term AI impact, training models for nurses, and strategies to balance AI-driven efficiency with human-centered care.

Reply to Guo and to Chen et al.

This study examines the role of Artificial Intelligence (AI) in enhancing customer service, with a particular focus on personalized services and post-sales support in the banking sector.The research is centered on AU Bank's branches in Indore, India, utilizing a mixed-methods approach.A quantitative survey was conducted with approximately 90 customers, complemented by qualitative interviews with bank managers, to assess AI's impact on customer satisfaction and operational efficiency.Findings of the study indicate that AI accounts for 32.8% of the variance in overall customer experience.The introduction of AI-driven personalized customer service explains 22.9% of this variance, while AI's role in after-sales support contributes only 7%.These results align with prior predictions regarding AI's effectiveness in post-sales services.Further, AU Bank leverages AI by automating routine operational tasks, thereby enhancing service personalization.This allows customers to experience seamless interactions without frequent human intervention.However, challenges such as language barriers and the need for constant AI model updates limit its effectiveness, particularly in post-sales support.AI is poised to revolutionize customer experience in banking.However, to fully realize its potential, sustained investments in AI infrastructure, data security, and regional adaptability are essential.Future research should explore AI's long-term impact on customer loyalty and business performance, along with its adaptability across diverse cultural and linguistic contexts.

The first chapter of the book explores the transformative applications of artificial intelligence (AI) in sensor technology and computer vision, focusing on human activity recognition, health monitoring, medical imaging, and autonomous vehicles within the automotive industry. It highlights the substantial advancements AI brings to these fields, particularly emphasizing the roles of machine learning (ML) and deep learning (DL), a subset of ML. In the field of human activity recognition and health monitoring, AI's ability to enhance accuracy and efficiency is thoroughly examined. The discussion extends to medical imaging, where ML and DL techniques significantly improve diagnostic processes and patient outcomes. The chapter also delves into the automotive industry, showcasing AI's impact on enabling self-driving cars and optimizing manufacturing processes. Each section provides detailed insights into the potential capabilities of ML and DL, illustrating AI's role as a game-changer that revolutionizes traditional methods. The narrative underscores the transformative power of these technologies, driving innovation and creating new opportunities across various domains. Additionally, the chapter addresses the challenges faced in the construction and operation of ML models. It analyzes difficulties such as data quality issues, computational resource demands, and algorithmic training complexities, offering a balanced perspective on the promises and hurdles of AI deployment. The chapter concludes with an in-depth discussion on sensor data collection and processing and case studies to demonstrate AI applications in real life. This section covers methodologies for gathering high-quality sensor data, pre-processing techniques, and integrating this data into AI frameworks, setting the stage for understanding AI's profound impact and technical intricacies.

Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing industries, reshaping the way we interact with technology, and driving innovation across multiple disciplines. Advancements in Artificial Intelligence and Machine Learning is a comprehensive exploration of the latest developments, applications, and challenges in AI and ML, offering insights into cutting-edge research and real-world implementations. This book is a collection of twelve chapters, each exploring a distinct application of Artificial Intelligence (AI) and Machine Learning (ML). It begins with an overview of AI's transformative role in Next-Gen Mechatronics, followed by a comprehensive review of key advancements and trends in the field. The book then examines AI's impact across diverse sectors, including energy, digital communication, and security, with topics such as AI-based aging analysis of power transformer oil, AI in social media management, and AI-driven human detection systems. Further chapters address sentiment analysis, visual analysis for image processing, and the integration of AI in smart grid networks. The volume also covers AI applications in hardware security for wireless sensor networks, drone robotics, and crime prevention systems. The final set of chapters highlight AI's role in healthcare and automation, including an AI-assisted system for women's safety in India and the use of EfficientNet B0 CNN architecture for brain tumor detection and classification. Together, these chapters showcase the versatility and growing influence of AI and ML across critical modern industries. Key features A multidisciplinary approach covering AI applications in robotics, cybersecurity, healthcare, and digital transformation in 12 organized chapters. A focus on contemporary challenges and solutions in AI and ML across industries. Research-driven insights from experts and practitioners in the field. Practical discussions on AI-driven automation, security, and intelligent decision-making systems.