Advancing Biomedical Engineering With Artificial Intelligence and Machine Learning: A Systematic Review

This research explores the integration and impact of artificial intelligence (AI) within Emirati media organizations, focusing on adoption determinants, obstacles, and interventions. Utilizing a mixed-methods approach, the study incorporates qualitative data from 21 in-depth interviews with media professionals and a thematic analysis of 30 scholarly articles published between 2019 and 2024. The research aims to provide a nuanced understanding of AI's transformative effects on the UAE media landscape. Key findings reveal that AI is significantly enhancing content creation, distribution, and audience engagement processes within Emirati media organizations. AI technologies such as machine learning, natural language processing, and predictive analytics are being leveraged to improve operational efficiency, streamline workflows, and deliver personalized content to audiences. These advancements are driving competitive advantages and elevating the overall quality of media output. Despite these benefits, several challenges hinder widespread AI adoption. Technological barriers, including limited access to advanced AI tools and infrastructure, pose significant obstacles. Additionally, there is a pronounced shortage of skilled personnel capable of implementing and managing AI systems, which exacerbates concerns over job displacement and workforce anxiety. Ethical considerations, such as ensuring unbiased AI algorithms and protecting user privacy, are also critical issues that require attention. The study underscores the importance of continuous training and upskilling for media professionals to adapt to the evolving technological landscape. It highlights the role of robust ethical guidelines and governance frameworks in promoting responsible AI use. Government support, through targeted policies, funding initiatives, and incentives, is identified as a crucial factor in facilitating AI integration and addressing adoption barriers. The research concludes by providing strategic recommendations for media organizations and policymakers. It advocates for the development of comprehensive AI adoption roadmaps that include capacity-building initiatives, ethical oversight, and collaborative efforts between industry stakeholders. By addressing these areas, Emirati media organizations can harness the full potential of AI to drive innovation, improve audience engagement, and maintain competitiveness in a rapidly digitizing global media environment. This study contributes to the growing body of knowledge on AI in media, offering valuable insights into the specific context of the UAE. It provides a foundation for future research on AI's role in media and serves as a guide for organizations seeking to navigate the complexities of AI adoption.

Artificial Intelligence (AI) and Big Data Analytics (BDA) are increasingly recognized as transformative forces that are reshaping industries, governance, and human society in profound ways. AI, with its foundations in machine learning, deep learning, natural language processing, and predictive modelling, requires vast amounts of data to train algorithms and generate reliable insights. Complementing this, BDA provides the scale, diversity, and velocity of data processing required to uncover hidden patterns, correlations, and trends within complex datasets. Together, AI and BDA form a synergistic paradigm in which Big Data serves as the fuel and AI operates as the engine, driving intelligent, data-driven innovation across multiple domains. Globally, AI-BDA applications are enabling predictive diagnostics, telemedicine platforms, genomic-driven personalized treatments, and advanced medical imaging, thereby enhancing both the accessibility and quality of care. In India, where the healthcare system must serve more than 1.4 billion citizens with limited resources, these technologies can play a pivotal role in addressing systemic challenges such as inadequate rural access, shortages of medical professionals, real-time disease surveillance, and hospital resource optimization. International best practices, including the NHS AI Lab in the United Kingdom, Mayo Clinic initiatives in the United States, and Ping a Good Doctor in China, provide models that India can adapt and scale to its unique demographic and infrastructural context. Despite their vast potential, the adoption of AI and BDA faces several barriers. Data privacy and cybersecurity remain critical concerns, particularly with the emergence of new regulations and the sensitive nature of health-related information. Additionally, algorithmic bias and the uneven availability of digital infrastructure could exacerbate existing inequities if not adequately addressed. Looking forward, advancements in Edge AI, Explainable AI (XAI), and quantum-enhanced analytics are expected to extend the scope and reliability of AI-BDA systems. This paper argues that the integration of AI and BDA is not merely a technological advancement but a paradigm shifts in how societies innovate, make decisions, and deliver essential services. Specifically, it highlights the need for a human-centric, ethical, and affordable approach to healthcare innovation in India in order to unlock the full transformative potential of these technologies. Keywords: Artificial Intelligence, Big Data Analytics, Healthcare Innovation, Predictive Diagnostics, Ethical AI, Edge AI.

Abstract: The chapter "NextGen Realities: AI, AR, and Data Science Integration" explores the transformative convergence of Artificial Intelligence (AI), Augmented Reality (AR), and Data Science, highlighting their combined potential to revolutionize various industries. It delves into the core technologies, their synergistic integration, and practical applications in fields such as finance, engineering, healthcare, and manufacturing. The chapter examines the mathematical foundations underlying these technologies, discusses the development of intelligent conversational agents, and investigates the unified frontier of AI and VR. By addressing technical challenges, ethical considerations, and future trends, this chapter provides a comprehensive overview of how the integration of AI, AR, and Data Science is shaping the future of innovation and digital transformation. Keywords: Artificial Intelligence (AI),Augmented Reality (AR),Data Science,Big Data,Virtual Reality (VR),Machine Learning (ML),Predictive Analytics,Generative Design,Conversational Agents,Intelligent Automation,Financial Technology (FinTech),Engineering Design,Healthcare Technology,Manufacturing Processes,Mathematical Foundations,Real-Time Data Processing,Smart Systems, Digital Transformation,Future Trends and Ethical AI.

The aim of the paper is twofold. First to examine the role of the board of directors in facilitating the adoption of AI and ML in Saudi Arabian banking sector. Second, to explore the effectiveness of artificial intelligence and machine learning in protection of Saudi Arabian banking sector from cyberattacks. A qualitative research approach was applied using in-depth interviews with 17 board of directors from prominent Saudi Arabian banks. The present study highlights both the opportunities and challenges of integrating artificial intelligence and machine learning advanced technologies in this highly regulated industry. Findings reveal that advanced artificial intelligence and machine learning technologies offer substantial benefits, particularly in areas like threat detection, fraud prevention, and process automation, enabling banks to meet regulatory standards and mitigate cyber threats efficiently. However, the research also identifies significant barriers, including limited technological infrastructure, a lack of cohesive artificial intelligence strategies, and ethical concerns around data privacy and algorithmic bias. Interviewees emphasized the board of directors’ critical role in providing strategic direction, securing resources, and fostering partnerships with artificial intelligence technology providers. The study further highlights the importance of aligning artificial intelligence and machine learning initiatives with national development goals, such as Saudi Vision 2030, to ensure sustained growth and competitiveness. The findings from the present study offer valuable implications for policymakers in banking in navigating the complexities of artificial intelligence and machine learning adoption in financial services, particularly in emerging markets.

Artificial intelligence (AI) has transformed pharmacological research through machine learning, deep learning, and natural language processing. These advancements have greatly influenced drug discovery, development, and precision medicine. AI algorithms analyze vast biomedical data identifying potential drug targets, predicting efficacy, and optimizing lead compounds. AI has diverse applications in pharmacological research, including target identification, drug repurposing, virtual screening, de novo drug design, toxicity prediction, and personalized medicine. AI improves patient selection, trial design, and real-time data analysis in clinical trials, leading to enhanced safety and efficacy outcomes. Post-marketing surveillance utilizes AI-based systems to monitor adverse events, detect drug interactions, and support pharmacovigilance efforts.Machine learning models extract patterns from complex datasets, enabling accurate predictions and informed decision-making, thus accelerating drug discovery. Deep learning, specifically convolutional neural networks (CNN), excels in image analysis, aiding biomarker identification and optimizing drug formulation. Natural language processing facilitates the mining and analysis of scientific literature, unlocking valuable insights and information.However, the adoption of AI in pharmacological research raises ethical considerations. Ensuring data privacy and security, addressing algorithm bias and transparency, obtaining informed consent, and maintaining human oversight in decision-making are crucial ethical concerns. The responsible deployment of AI necessitates robust frameworks and regulations.The future of AI in pharmacological research is promising, with integration with emerging technologies like genomics, proteomics, and metabolomics offering the potential for personalized medicine and targeted therapies. Collaboration among academia, industry, and regulatory bodies is essential for the ethical implementation of AI in drug discovery and development. Continuous research and development in AI techniques and comprehensive training programs will empower scientists and healthcare professionals to fully exploit AI's potential, leading to improved patient outcomes and innovative pharmacological interventions.

This study examines the transformative potential of Artificial Intelligence (AI) in international trade, highlighting its significant roles in optimizing trade operations, enhancing trade finance, and improving market access. In trade optimization, through advanced machine learning and predictive analytics, AI helps in demand forecasting, route optimization, and customs processes, ensuring efficient logistics and inventory management. In trade finance, AI automates document processing and risk assessment, democratizing access to finance and enhancing transactional transparency through integration with blockchain technology. In improving market access, AI-driven analytics identify consumer trends and competitive dynamics, enabling personalized marketing and overcoming linguistic and cultural barriers. The study includes case studies of leading companies such as Alibaba, DHL, and Maersk, showcasing how they leverage AI to enhance their trade operations, improve customer service, and achieve greater efficiency. These real-world examples demonstrate AI's practical applications and significant benefits in the global trade landscape. However, the adoption of AI in international trade is not without challenges. These include data quality, ethical considerations, technological complexity, and public perception. Ensuring high-quality, accessible data, fostering ethical AI usage, and overcoming technological and regulatory barriers are crucial for successful AI integration. Policy recommendations emphasize the need for robust data infrastructure, ethical AI guidelines, and international cooperation to harmonize data protection regulations. These measures aim to create an environment conducive to AI-driven sustainable economic growth and innovation in global trade.

Objective. This paper discusses the capabilities and implications of Artificial Intelligence (AI) in Nigerian academic libraries. The study emphasizes the importance of libraries using new technologies to improve their operations and services, especially in developing countries like Nigeria. Methods. The research conducted a literature review to examine the capabilities of AI in libraries and its impact on academic libraries. Results. Various AI tools such as natural language recognition, robotics, big data, and machine learning were identified. AI can revolutionize library services, improve information quality, increase productivity, and provide virtual assistance. However, there are challenges to the adoption of AI in Nigerian academic libraries, including high costs, resistance to change, poor network connectivity, privacy and ethical implications, and a lack of supportive cultures. To fully exploit the benefits of AI, libraries must develop plans and policies, train librarians with the necessary skills, and address the challenges associated with AI adoption. Conclusions. AI holds great advantages to enhancing library services in Nigeria, but careful planning and preparation are needed.

This paper presents a comprehensive review of Artificial Intelligence (AI) and Machine Learning (ML), exploring foundational concepts, emerging trends, and diverse applications. AI and ML have rapidly evolved, becoming pivotal in numerous fields including healthcare, finance, manufacturing, and autonomous systems. The review begins by outlining key concepts, including the distinctions between AI, ML, and deep learning, and delves into various learning paradigms such as supervised, unsupervised, and reinforcement learning. It highlights significant advancements, such as neural networks, natural language processing, and generative models, emphasizing their impact on industry and research. The paper also examines current trends, including the rise of ethical AI, explain ability, and the integration of AI with Internet of Things (IoT) and edge computing, which are shaping the future landscape of AI applications. Additionally, it addresses the challenges and limitations associated with AI and ML, such as data privacy concerns, model interpretability, and the need for sustainable computing solutions. By synthesizing insights from recent literature, this review provides a holistic understanding of the AI and ML domains, offering perspectives on future directions and innovations. this review aims to provide a holistic understanding of AI and ML, offering perspectives on future directions and innovations.

The rapid rise of artificial intelligence (AI) is revolutionizing the way that companies view data, technology and process automation across all industries from marketing to financial decisions. Through this quantitative study the essay will examine how AI predictive analytics may help to enhance marketing performance and financial strategy outcomes for SMEs. To develop data, 350 management and analytics professionals working for organizations that had implemented AI-driven predictive tools were surveyed in a cross-sectional survey design. This paper aimed to measure the influence of artificial intelligence (AI) and machine learning (ML) applications in marketing KPIs and financial performance KPIs. As a whole, our findings suggest that the adoption of AI is quite positively related to advancements in marketing and financial performance, leaving those not adopting AI behind. Regression results suggest that the depth of AI implementation is a key factor in the enhancement of performance. We then included the important moderators as data quality, zero-momentum and non-generation. These are the takeaways: Businesses can get huge strategic advantages from moving to AI, only if they have good data governance, digital infrastructure and people trained in use of the technology. The variance analysis also reflects that some industries are more receptive to the integration of artificial intelligence, such as finance and service industries, due to a possibility for stronger data capacity and better data-processing approaches. The results from this investigation reveal that today’s AI-powered predictive analytics can function as a strategic asset to businesses by boosting operational effectiveness, strengthening financial wellbeing and directing better marketing choices. But companies must also spend on the basics, like quality of data, skills of staff and governance around ethical use of AI, to make the most from predictive capabilities. This research contributes to the growing literature on AI strategy and arm business leaders with actionable information that can help them be more competitive in a data-driven global economy.

Health data science serves as a transformative bridge between healthcare and technology, enabling data‐driven decision‐making, personalised medicine, and more effective public health interventions. This study presents a comprehensive investigation into advanced techniques such as machine learning (ML), natural language processing (NLP), predictive analytics, and data visualisation, emphasising their applications in oncology, diabetes management, radiology, cardiology, and public health. High‐quality datasets—sourced from electronic health records (EHRs), national health surveys, and clinical trial databases—were rigorously preprocessed to ensure accuracy and reliability. The interdisciplinary approach integrates expertise from computer science, statistics, biomedical engineering, and clinical medicine to foster cross‐sector collaboration. Real‐world case studies demonstrate measurable benefits, including up to a 20% improvement in early cancer detection accuracy using predictive models, a 15% reduction in diagnostic errors via AI‐assisted radiology, and enhanced personalised treatment pathways for chronic disease management. The findings underscore Health Data Science's role in evidence‐based policy‐making, illustrated by data‐driven strategies for pandemic response planning. Ethical and security considerations are addressed through compliance with the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA), alongside emerging concerns over cyber risks, transparency, fairness, and accountability in AI systems. Limitations such as data integration challenges and institutional resistance are discussed, with proposed solutions. Future research directions include real‐time data processing, improved interoperability with EHR systems, and broader deployment of predictive models to enhance patient outcomes and healthcare efficiency.

Purpose: The study explores the transformative role of Artificial Intelligence (AI) in enhancing fraud detection and authenticity verification within global supply chains. It focuses on high-stakes industries such as pharmaceuticals, electric vehicles, consumer electronics, and agriculture, where counterfeiting poses significant risks to safety and consumer trust. By integrating AI technologies like machine learning, neural networks, and natural language processing (NLP), the study highlights how AI enhances supplier data analysis, physical verification through computer vision, and predictive analytics for fraud prevention. Methodology: The methodology combines case studies, industry reports, and the Gartner Five Levels of Supply Chain Maturity framework to examine AI's potential across siloed management, cross-functional integration, and ecosystem orchestration. Real-world use cases, including Siemens and IBM, demonstrate AI's success in fraud detection, cost savings, and improved product safety. Findings: The findings reveal that AI, in synergy with blockchain, ensures supply chain transparency while addressing ethical challenges like data privacy and environmental concerns. Unique Contribution to Theory, Practice and Policy: Recommendations emphasize ethical AI deployment, strategic investment in feasible technologies like IoT and biometric verification, and prioritization of regulatory compliance. The study concludes that AI adoption is critical for building resilient and trustworthy supply chains, offering strategic advantages in combating fraud and fostering consumer confidence.

“The rapid advancement of Artificial Intelligence (AI) has transformed the recruitment landscape, enabling more efficient, data-driven, and adaptive job search mechanisms. Traditional hiring methods often suffer from inefficiencies such as lengthy hiring cycles, unconscious bias, and poor candidate-job matching. AI-driven recruitment systems address these challenges by automating resume screening, analyzing candidate behavior, and providing personalized job recommendations. This paper explores the role of AI in recruitment, focusing on machine learning (ML), natural language processing (NLP), and deep learning techniques used to enhance candidate-job matching. AI-powered tools such as resume parsers, chatbots, and predictive analytics streamline the hiring process, reducing human effort and improving decision-making accuracy. Moreover, adaptive AI algorithms continuously learn from hiring patterns to refine recruitment strategies, increasing efficiency for both employers and job seekers. Despite these advancements, AI-based recruitment presents challenges, including algorithmic bias, ethical concerns, and data privacy issues. This paper discusses potential solutions, such as bias mitigation techniques, explainable AI (XAI), and ethical AI frameworks, to ensure fairness and transparency in hiring. Through case studies of AI-driven recruitment platforms like LinkedIn, HireVue, and IBM Watson, we analyze real-world applications and their impact on job placement success rates. The findings show that AI improves hiring efficiency but requires ethical oversight. The paper concludes by identifying future research directions in AI-powered recruitment, such as blockchain integration and AI-driven psychometric testing, to further improve hiring fairness and effectiveness." Index Terms- Artificial Intelligence, AI recruitment, job matching, machine learning, NLP, automation, bias in hiring, ethical AI.

The rapid advancement of Artificial Intelligence (AI) has profoundly impacted the manufacturing industry, offering transformative potential to enhance production process efficiency. This paper presents a systematic literature review of AI applications in the manufacturing sector, focusing on key AI technologies such as machine learning, robotics, predictive analytics, and natural language processing. The review highlights how these technologies have improved quality control, resource management, and overall operational performance. However, the adoption of AI also presents challenges, including significant investment costs, the need for a skilled workforce, and concerns over data security and privacy. Despite these challenges, the integration of AI in manufacturing presents numerous opportunities for future research and innovation, particularly in the areas of sustainable manufacturing and the convergence of AI with other emerging technologies. This study concludes that while AI offers substantial benefits for production efficiency, its successful implementation requires careful strategic planning and investment in both technology and human resources.

This research aims to critically examine the ethical integration of artificial intelligence (AI) in education through the perspective of maqāṣid al-sharīʿah, emphasizing the alignment between technological innovation and Islamic moral principles. The methods used are a systematic literature review and thematic content analysis against peer-reviewed publications for the period 2015–2025, which discuss the application of AI in primary, secondary, and higher education. The study identified dominant ethical issues, such as data privacy, algorithmic bias, accountability, human agency, and moral development, which were then mapped to Islamic ethical goals, including ʿadl (justice), amānah (belief), karāmah al-insān (human dignity), and ḥifẓ al-ʿaql (protection of reason). The results of the analysis show that the adoption of AI in education often emphasizes efficiency, personalization, and predictive analytics, but has the potential to reduce learners' autonomy and ethical reasoning. The mapping of maqāṣid al-sharīʿah shows a strong normative conformity, so that Islamic principles can be a moral foundation as well as a practical guide for AI governance. The research contribution is theoretical by bridging the literature on AI ethics and Islamic educational philosophy, as well as practical by offering an integrative framework for AI policymakers, educators, and developers. The integration of maqāṣid al-sharīʿah in AI governance ensures justice, trust, inclusivity, and the development of the whole human being (insān kāmil).

PurposeAs artificial intelligence (AI) and machine learning (ML) technologies continue to revolutionize various industries, understanding their impact on job embeddedness becomes crucial. This study examine the role of AI and ML technologies on job embeddedness, identifying key trends and proposing future research directions. It seeks to understand how these technologies influence employee attachment within organizations.Design/methodology/approachThis study uses bibliometric analysis to assess 890 articles published from 2001 to 2023 on job embeddedness and its relationship with AI and ML. The Scopus database is examined utilizing VOSviewer and Biblioshiny applications to determine themes and research deficiencies. This study visualizes the intellectual landscape of this area.FindingsThis study highlights the growing interest in AI and ML job embeddedness, highlighting the complex relationship between AI adoption and employee attachment, links and fit. It also highlights emerging themes like AI-enabled talent management, remote work implications and ethical considerations in AI-driven workplaces.Research limitations/implicationsThe extent of the Scopus database, the time period and the metadata correctness all provide restrictions on this investigation of how AI and ML affect job embeddedness. Nonetheless, the results underscore the need for empirical study on the effects of AI and ML and provide researchers with useful insights. This study also highlights how technological improvements influenced employee attitudes and actions.Originality/valueThis study presents a comprehensive overview of job embeddedness and AI/ML technologies, utilizing bibliometric techniques to evaluate research publications. It reveals key trends, identifies gaps and suggests future directions in this field.