Taking defence rights seriously. The need for explainable Artificial Intelligence in criminal proceedings – A European perspective

ORTHO AI : The Dawn Of A New Era: Artificial Intelligence In Orthopaedics

The rapid advancement of technology, particularly artificial intelligence (AI), is transforming the world of management. This chapter delves into the futuristic trends in management, focusing on the integration of AI into various managerial aspects, its potential benefits, challenges, and strategies for successful adoption. AI-driven decision-making aids managers in data-driven processes, identifying patterns, trends, and insights through AI algorithms. AI's impact on human resources management includes AI-enabled talent acquisition and recruitment, enhanced employee experience through AI-driven content, and AI-driven operations management. AI-driven supply chain optimization, process automation, and customer relationship management (CRM) are also explored. However, ethical implications of AI in management include addressing biases and fairness concerns, ensuring transparency and accountability, and navigating privacy and data security challenges. Managing the human-AI collaboration involves building a culture that embraces AI while valuing human expertise, fostering a learning mindset, encouraging continuous skill development, and mitigating potential job displacement and promoting AI-human synergy. The chapter emphasizes the importance of fostering a learning mindset, encouraging continuous skill development, and mitigating potential job displacement. AI's integration into management practices has the potential to revolutionize various aspects of organizations, including data management, resource allocation, personalization, risk assessment, supply chain management, and employee productivity. AI-driven tools enable efficient data management, identification of trends, correlations, and actionable insights from complex datasets. They can optimize financial resources, human capital, or physical assets, enhance operational efficiency, and provide personalized customer experiences. AI-driven decision-making aids managers in making informed decisions by leveraging capabilities such as data processing, pattern recognition, real-time insights, and predictive analytics. These capabilities help managers segment customers, analyze market trends, and predict future demand. AI also enhances predictive and prescriptive analytics by providing recommendations for optimal performance. AI's impact on human resources management includes AI-enabled talent acquisition and recruitment. AI-powered tools can streamline the traditional recruitment process by scanning online platforms, screening resumes, and conducting assessments and skill evaluations. AI helps mitigate bias in the hiring process through blind hiring, objective evaluation, and data-driven decisions. However, challenges and ethical considerations include data privacy and security, transparency and explainability, algorithmic bias, and candidate experience. In conclusion, AI's integration into management practices has the potential to revolutionize various areas, including data management, resource allocation, personalization, risk assessment, supply chain management, and employee performance. However, HR professionals must address ethical concerns such as data privacy, transparency, algorithmic bias, and candidate experience to ensure the success and competitiveness of AI-based recruitment. AI can significantly enhance the employee experience by providing personalized learning and development programs, enhancing performance evaluations, building employee engagement strategies, and predicting potential attrition risks. AI-powered tools can assess employees' existing skills, knowledge gaps, and learning preferences, enabling the creation of personalized development plans. AI-driven learning platforms can adjust the difficulty and content of training materials based on individual progress, fostering a culture of continuous learning. AI-based performance evaluations can provide valuable insights to enhance the performance evaluation process, offering real-time tracking and 360-degree feedback analysis. AI-driven employee engagement strategies can bolster employee satisfaction, tailoring benefits, rewards, and recognition programs to individual needs. Predictive attrition analysis and chatbots for employee support can also help retain valuable employees. AI can also optimize supply chain operations by accurately predicting demand and managing inventory. AI-driven systems can dynamically adjust inventory levels based on real-time demand fluctuations and lead times, maintaining optimal resource utilization and minimizing excess inventory. AI-powered logistics and route optimization can revolutionize logistics and transportation management by optimizing routes and enhancing overall efficiency. AI-driven process automation can transform various business operations by identifying suitable processes, analyzing high-volume data processing, streamlining workflow and resource allocation, and addressing workforce concerns and upskilling needs amid automation. By embracing AI in operations management, businesses can achieve unprecedented levels of efficiency, resilience, and responsiveness to market demands. In conclusion, AI can transform the employee experience, drive productivity, and foster a dynamic workforce. However, careful consideration of ethical principles and continuous monitoring of AI systems are essential for responsible implementation. AI can significantly improve customer satisfaction and loyalty through real-time feedback analysis, chat sentiment analysis, and personalized loyalty programs. AI-powered customer support includes chatbots and virtual assistants, which provide instant and round-the-clock assistance. AI platforms, such as Natural Language Processing (NLP), enable chatbots to understand and respond to customer queries in a conversational manner. However, AI integration also presents challenges, such as addressing biases and fairness concerns in AI algorithms, ensuring transparency and accountability in AI systems, and managing privacy and data security challenges. Adhering to data protection regulations and implementing robust data governance frameworks are essential for safeguarding customer and organizational data. Managing the human-AI collaboration involves building a culture that embraces AI while valuing human expertise. This can be achieved through change management and training, promoting a collaborative environment, fostering a learning mindset, upskilling and reskilling initiatives, emphasizing creativity and critical thinking, and mitigating potential job displacement and promoting AI-human synergy. In conclusion, embracing AI in management offers numerous opportunities to enhance organizational efficiency, productivity, and competitiveness. It is crucial for management professionals and corporate leaders to understand and harness the power of AI responsibly. By being proactive in addressing challenges and aligning AI initiatives with organizational values, businesses can leverage futuristic trends in management to thrive in the dynamic and ever-evolving landscape.

Artificial Intelligence (AI) has emerged as a transformative force in reshaping customer-centric marketing strategies. This paper explores the pivotal role of AI in leveraging data-driven insights to enhance customer engagement, optimize personalization, and drive decision-making in marketing. By integrating technologies such as machine learning, natural language processing, and predictive analytics, organizations can better understand consumer behavior, preferences, and trends. These AI-driven tools enable businesses to craft targeted marketing campaigns, anticipate customer needs, and deliver superior customer experiences in real time. The rise of AI has enabled marketers to process vast amounts of data from multiple sources, such as social media, customer feedback, and purchase history, with unprecedented speed and accuracy. AI algorithms can identify patterns and predict future behaviors, empowering companies to make informed marketing decisions. This leads to a significant reduction in marketing waste, enhanced return on investment (ROI), and improved customer loyalty. Furthermore, AI applications such as chatbots, recommendation engines, and automated content creation have revolutionized customer interaction, making it more seamless and tailored. For instance, AI-powered chatbots provide 24/7 support, while recommendation engines offer personalized product suggestions, boosting conversion rates. AI also plays a crucial role in predictive analytics, enabling marketers to anticipate market trends and customer needs, thereby staying ahead of the competition. The paper also highlights challenges associated with adopting AI in marketing, including ethical considerations, data privacy concerns, and the need for skilled professionals to manage AI systems effectively. Despite these challenges, the integration of AI into customer-centric marketing strategies has proven to be a game-changer, transforming traditional marketing practices and paving the way for innovative approaches to customer engagement. In conclusion, AI is revolutionizing marketing by enhancing personalization, efficiency, and effectiveness in customer engagement. Its ability to process and analyze vast data sets, predict trends, and deliver targeted content has established AI as an indispensable tool in modern marketing strategies. As technology continues to evolve, AI’s role in driving customer-centric marketing is expected to grow, further redefining how businesses connect with their audiences.

The integration of Artificial Intelligence (AI) into Information Systems (IS) is ushering in a transformative era of data-driven decision-making. This research paper presents a comprehensive exploration of AI's applications, benefits, challenges, and future directions within IS. AI is revolutionizing data management through techniques such as automated data integration, natural language processing, and enhanced data quality, while also providing sophisticated decision support systems with predictive analytics and recommendation engines. Businesses benefit from streamlined processes, real-time analytics, and improved cybersecurity measures. However, challenges such as data quality, AI skill shortages, ethical concerns, and integration complexities must be addressed. The paper envisions future directions where Explainable AI (XAI) offers transparent decision rationales, ethics and governance frameworks ensure responsible AI adoption, augmented intelligence fosters human-AI collaboration, AI extends to edge computing for real-time processing, and AI fortifies cybersecurity measures. As AI technologies continue to mature, organizations must invest in research and development while formulating robust AI adoption strategies to harness the potential of AI in IS. The fusion of AI and IS is poised to redefine information management, facilitating more intelligent, efficient, and secure operations in the evolving digital landscape. Key Words: Artificial Intelligence, Information Systems, Machine Learning, Data Analytics, Natural Language Processing, Automation, Decision Support, Big Data.

This review delves into the profound impact of artificial intelligence (AI) integration on contemporary business paradigms. The paper meticulously explores diverse AI applications, including machine learning, natural language processing, and predictive analytics, illustrating how these technologies can revolutionize operational processes, augment decision-making capabilities, and foster unparalleled innovation within organizations. Drawing from case studies and industry examples across various sectors such as finance, healthcare, retail, and manufacturing, the study elucidates successful AI implementation strategies. It examines the importance of robust data governance frameworks to ensure quality and integrity, the acquisition of AI talent, and the imperative of fostering a culture of innovation and adaptability within organizations undergoing AI transformation. Furthermore, the paper addresses the nuanced challenges and risks inherent in AI adoption, spanning ethical considerations surrounding data privacy and bias mitigation, cybersecurity vulnerabilities, and the potential impact on the workforce. By providing a comprehensive overview of the opportunities and challenges associated with AI integration in business models, the study equips organizational leaders, policymakers, and stakeholders with invaluable insights to navigate the evolving landscape of AI-driven innovation. It underscores the significance of strategic foresight, cross-functional collaboration, and continuous learning in harnessing the full potential of AI technologies to drive sustainable growth and competitive advantage in the digital era.
 Keywords: AI, Business, Models, Strategies, Efficiency, Innovation.

Part One 1. The Enduring Legacy of 'Inquisitorial' and 'Accusatorial' Procedural Forms in the Debate on Comparative Criminal Procedure AThe Enduring Legacy of the Inquisitorial/Accusatorial Divide BThe Connection to Legal Nationalism CDeveloping a New Approach for Analysing European Criminal Procedure Law 2. The Origins of the European Criminal Procedural Tradition AIntroduction: The Importance of the Developments of the Nineteenth Century BThe Development of the 'Accusatorial Trinity' CJudicial Impartiality (i)The Separation of the Functions of 'Judging' and Prosecuting in France and Germany (ii) Impassivity or Activity: The Role of the English Judge in the Examination of the Evidence (iii) Institutional Impartiality DThe Public Hearing Requirement EImmediate and Oral Proceedings (i) Immediate and Oral Examination of Evidence at Trial (ii) Consideration at Trial of Evidence Collected before the Trial and Submitted in Writing (iii)Immediate and Oral Proceedings as Fundamental to the Accusatorial System FConclusions 3. The Rights of the Defence: Lessons from the Nineteenth Century AThe Institutional Nature of the 'Rights of the Accused' BThe Rights of the Defence at Trial (i)The Presence of the Accused (ii) Participatory Rights of the Accused (a) The Developing Conception of the Accused as a Party (b) Understanding the Nature of the Accused's Participatory Rights: The English Reforms of the Late Nineteenth Century (c) The Assistance of Counsel CThe Role of the Defence in the Pre-trial Phase (i) The Pre-trial Phase as 'Investigative' (a) The Questioning of the Accused (b) The Examination of Evidence (ii)The Determinative Reality of the Investigation DConclusions Part Two 4. Defining Fairness in Article 6(1) ECHR AIntroduction BIdentifying Vargha's 'Accusatorial Trinity' CThe Role of the 'Equality of Arms' Doctrine DThe Relationship between the Adversarial Procedure Requirement and the Equality of Arms EThe Court's Interpretation of the Adversarial Procedure Requirement in Criminal Proceedings (i)The Right to be Present at Trial (ii)Knowledge of the Other Side's Submissions (iii)Opportunity to Comment on the Other Side's Submissions FThe Relationship between the Defence and the Prosecution GFairness and Implied Procedural Forms 5. The Structure of the 'Trial' in Article 6 ECHR AIntroduction BThe Defence's Right to Challenge Witness Evidence CWitness Evidence in Europe: An Overview DRegulating Witness Evidence: Article 6(3)(d) (i)What is an Adequate and Proper Opportunity to Challenge Witnesses? (a) The Identity of the Witness (b) The Importance of the Witness (ii)When Should Witnesses be Examined? EThe Importance of the Trial as the Forum for Confronting Witness Evidence FReconciling Examination of Witnesses in the Investigation Phase with the 'Accusatorial Trinity' (i)The Presence of Counsel during Pre-trial Examination of Witnesses (ii)The Presence of an Impartial Supervisory Authority during the Examination of Witnesses (iii)Immediacy G The Privilege Against Self-incrimination (i) Improper Compulsion (ii) Indirect 'Acceptable' Compulsion (iii) The Relationship between Compulsion and the Assistance of Counsel (iv) The Privilege against Self-incrimination as a Substitute for the Refusal to Insist on Adversarial Principles in the Investigation Phase HThe Root of the Problem: Defining the 'Trial' (i)The Investigation Phase Lacuna (ii)Explaining the Investigation Phase Lacuna: Les Travaux Preparatoires (iii)Resolving the Fairness Deficit: Acknowledging the European Procedural Tradition 6. Reassessing Fairness in European Criminal Law: Procedural Fairness, Defence Rights and Institutional Forms AProcedural Fairness as Individual Rights BProcedural Rights and Institutional Forms CArticle 6 ECHR and the European Criminal Procedural Tradition DTowards an Institutional Understanding of Fairness in Criminal Proceedings

The rapid evolution of Artificial Intelligence (AI) since its inception in the mid-20th century has significantly influenced the field of Natural Language Processing (NLP), transforming it from a rule-based system to a dynamic and adaptive model capable of understanding the complexities of human language. This paper aims to offer a comprehensive review of the various applications and methodologies of AI in NLP, serving as a detailed guide for future research and practical applications. In the early sections, the paper elucidates the indispensable role of AI in NLP, highlighting its transition from symbolic reasoning to a focus on machine learning and deep learning, and its extensive applications in sectors such as healthcare, transportation, and finance. It emphasizes the symbiotic relationship between AI and NLP, facilitated by platforms like AllenNLP, which aid in the development of advanced language understanding models. Further, the paper explores specific AI techniques employed in NLP, including machine learning, Naive Bayes, and Support Vector Machines, and identifies pressing challenges and avenues for future research. It delves into the applications of AI in NLP, showcasing its transformative potential in tasks such as machine translation, facilitated by deep learning methods, and the development of chatbots and virtual assistants that have revolutionized human-technology interaction. The paper also highlights other fields impacted by AI techniques, including text summarization, sentiment analysis, and named entity recognition, emphasizing the efficiency and accuracy brought about by the integration of AI in these areas. In conclusion, the paper summarizes the remarkable advancements and persistent challenges in NLP, such as language ambiguity and contextual understanding, and underscores the need for diverse and representative labeled data for training. Looking forward, it identifies promising research avenues including Explainable AI, Few-shot and Zero-shot Learning, and the integration of NLP with other data modalities, aiming for a holistic understanding of multimodal data. The paper calls for enhanced robustness and security in NLP systems, especially in sensitive applications like content moderation and fake news detection, to foster trust and reliability in AI technologies. It advocates for continual learning in NLP models to adapt over time without losing previously acquired knowledge, paving the way for a future where AI and NLP work synergistically to understand and generate human language more effectively and efficiently.

Artificial Intelligence (AI) and academic publishing in psychiatry.

The construction industry, particularly in housing projects, faces numerous challenges, including ensuring regulatory compliance, maintaining high-quality standards, and managing risks such as delays, cost overruns, and safety concerns. This paper explores the integration of Artificial Intelligence (AI) into construction to address these issues through a smart, AI-driven framework for compliance, quality assurance, and risk management. By reviewing existing literature on AI applications in construction, the paper highlights how AI technologies, such as predictive analytics, natural language processing, and machine learning, can significantly improve construction outcomes by streamlining compliance processes, enhancing quality control, and mitigating risks in real-time. The paper also discusses the key design principles for creating an AI framework tailored to construction, examining how AI can monitor regulatory adherence, ensure quality standards, and predict potential project risks. While AI presents vast opportunities, the paper identifies challenges such as technological integration, financial constraints, and regulatory hurdles that may hinder its widespread adoption. Proposed solutions, including AI-based decision-support tools and cloud-based platforms, aim to overcome these challenges. Finally, the paper emphasizes the need for future research, particularly in areas like the integration of AI with Building Information Modeling (BIM), the development of universal standards for AI in construction, and the validation of AI frameworks through case studies. This research provides valuable insights into the potential of AI to transform the construction industry, offering recommendations for further development and implementation in housing projects.

Focus issue: Artificial intelligence in medical physics.

E-Government's incorporation of Artificial Intelligence (AI) has transformed governance models by increasing public accountability, efficiency, and transparency. The political communication process in India has changed as a result of AI-driven initiatives, which have made governance more open, data-driven, and citizen-focused. Natural Language Processing (NLP), machine learning, and predictive analytics are examples of AI applications that have revolutionized public service delivery, automated administrative tasks, and enhanced parliamentary decision-making. With an emphasis on India's digital governance transformation, this research paper examines how AI integration has improved transparency in e-government. AI powered chatbots, automated legal research tools, and digital grievance redressal mechanisms have streamlined citizen-government interactions, reducing bureaucratic inefficiencies and fostering public trust. Initiatives like the Supreme Court’s AI powered SUPACE system, the National Informatics Centre’s AI-driven data analytics, and Umang’s AI-based citizen services exemplify how AI has enabled greater transparency in governance. The impact of AI on governance is critically examined in this paper, with particular attention paid to how it can reduce corruption, enhance public access to information, and guarantee real-time monitoring of governmental operations. In order to show how AI integration in Indian e-government has set an example for digital democracy, this study analyzes secondary data from government reports, policy documents, and international assessments. It also discusses challenges such as data privacy concerns, algorithmic biases, and the digital divide, which may affect AI’s equitable adoption. The results highlight how India's AI-enabled e-government reforms have improved administrative effectiveness while also bolstering democratic engagement by increasing the accessibility and transparency of political communication. The study comes to the conclusion that India's model provides a path for the adoption of AI in parliamentary procedures worldwide, guaranteeing inclusive, accountable, and technologically advanced governance.

Artificial Intelligence (AI) is revolutionizing the manufacturing industry by optimizing processes, enhancing productivity, and reducing operating costs. This report explores the use of AI in manufacturing, focusing on its application in predictive maintenance, quality control, robotics, and process optimization. AI technologies such as machine learning, computer vision, and data analytics allow manufacturers to automate processes, detect anomalies, and make data-based decisions with unprecedented accuracy. These developments drive the industry towards more efficiency, sustainability, and competitiveness. Predictive maintenance, arguably the most impactful application of AI, uses real-time information to forecast machine breakdowns in advance, decreasing downtime and lowering repair costs. Unlike traditional reactive or preventive maintenance approaches, predictive maintenance using AI leverages machine learning models to analyze patterns and outliers in equipment operations. This forward-looking approach enhances working efficiency, extends the lifespan of machines, and reduces unnecessary labor costs. AI is also transforming quality control with advanced machine vision systems. By integrating neural networks and deep learning, AI can detect slight defects in products more precisely and faster than human inspectors or traditional methods. This ensures consistent product quality, reduces waste, and increases customer satisfaction. AI also enhances root cause analysis (RCA) by identifying the causes of defects in real-time, enabling manufacturers to fix issues before they become significant problems. In robotics, AI makes machines smarter and more responsive, allowing them to perform complex tasks with minimal human intervention. AI-driven robots can learn from their environment, evolve with changes in production conditions, and operate safely with human workers. This not only increases efficiency but also enhances workplace safety by detecting risks and preventing accidents. Process optimization is another aspect that AI improves. Through analyzing vast amounts of real-time data, AI has the power to identify bottlenecks, optimize processes, and optimize the allocation of resources. Predictive analytics also enables manufacturers to forecast future market conditions and plan production accordingly, thus minimizing overproduction or underproduction risks. Although AI’s integration in manufacturing carries numerous benefits, it also poses some challenges. Data quality challenges, a large initial capital investment, and the need for skilled professionals represent significant barriers. The acquisition of accurate and labeled data is crucial to the implementation of AI. Furthermore, the initial level of investment incurred to install AI may be too high for smaller manufacturers. Additionally, the fusion of AI and Computer-Aided Design (CAD) is opening a new frontier in engineering, with data-driven insights and machine learning algorithms transforming the way we design, evolve, and innovate. AI in product and manufacturing engineering is a new and very fast-growing technology in CAD, driven by machine learning algorithms that process large amounts of data to find patterns and make predictions, enabling automation of repetitive tasks. This technology helps minimize manual processes and increases efficiency by making complex geometries and optimized structures previously difficult to produce. AI significantly impacts CAD through generative design, producing numerous design iterations based on parameters such as material usage, structural integrity, and novelty. Industries like aerospace, automotive, and robotics benefit from AI-driven CAD tools enhancing precision through real-time feedback and iterative optimization. In dentistry, a 3D-CNN (Convolutional Neural Network) model automates partial dental crown design with 60% validation accuracy, democratizing CAD workflows for minimally invasive care. NLP (Natural Language Processing) and computer vision technologies also make CAD tools accessible to non-experts, fostering inclusiveness in engineering and design capabilities. AI is likewise revolutionizing the world of design by breaking barriers of creativity, efficiency, and innovation. AI plays an interactive role in creativity generation, decision-making, and optimizing design workflows. AI tools enable designers to produce hundreds of design iterations quickly, fostering exploration and solution-based thinking. Tools like Adobe Firefly, Autodesk’s Generative Design, and AI-powered VR platforms are transforming fields from graphic design to urban planning. Real-world applications like Tesla's automotive design and Singapore's urban planning demonstrate the observable benefits of AI integration into the creative domain, enhancing workflows and helping designers rapidly realize novel ideas. However, integrating AI into design raises ethical and practical challenges, including algorithmic bias, employment displacement, and concerns over human creativity loss. The expense and required technical expertise further complicate widespread adoption. Emerging trends such as explainable AI (XAI), sustainable design, and the integration of AI with immersive technologies like VR and AR offer promising developments for addressing global issues such as sustainability and urbanization. In summary, AI is revolutionizing manufacturing, CAD, and design by offering innovative solutions to age-old problems, optimizing efficiency, enhancing creativity, and transforming entire workflows. Despite barriers, AI’s expanding role promises to unlock unprecedented potentials for productivity and innovation in the future.

Enterprise Resource Planning (ERP) systems play a pivotal role in streamlining business operations and improving decision-making in high-stakes industries such as healthcare, finance, and manufacturing. With the rise of Artificial Intelligence (AI), ERP solutions have undergone a paradigm shift, offering enhanced capabilities for real-time data analysis, predictive analytics, and process automation. This paper explores AI's transformative impact on ERP, with a specific focus on strategies for seamless Software-as-a-Service (SaaS) implementation in industries with critical operational demands. The integration of AI into ERP systems not only optimizes resource utilization but also mitigates risks associated with manual data handling and fragmented workflows. Key challenges in implementing SaaS-based AI-driven ERP solutions include data security, interoperability, scalability, and organizational resistance to change. This study presents a comprehensive framework to address these challenges, emphasizing AI-enabled data migration, adaptive learning algorithms, and robust cybersecurity measures tailored for high-stakes environments. Additionally, it highlights the importance of stakeholder engagement, training programs, and iterative implementation strategies to ensure smooth adoption and maximize ROI. Case studies from healthcare and manufacturing sectors illustrate successful AI-SaaS ERP adoption, showcasing significant improvements in supply chain optimization, financial forecasting, and regulatory compliance. The role of predictive analytics in anticipating operational bottlenecks and machine learning in automating repetitive tasks is emphasized, demonstrating tangible outcomes such as cost reduction, enhanced decision-making, and operational efficiency. This paper concludes by outlining future trends, including the integration of generative AI for custom ERP module development, AI-driven self-healing systems for real-time troubleshooting, and the use of natural language processing (NLP) for intuitive user interfaces. These advancements are poised to redefine ERP systems, empowering enterprises to navigate complex challenges in high-stakes industries with greater agility and precision.

Purpose This study aims to examine the adoption of artificial intelligence (AI) in electronic health record (EHR) systems in developing countries. It also aims to identify key challenges, explore the effectiveness of AI-driven solutions and propose a structured roadmap for optimizing AI integration to enhance health-care information management. Design/methodology/approach A systematic literature review (SLR) was conducted using the theory-context-characteristics-methodology (TCCM) framework to synthesize existing research on AI integration in EHR systems. The study analyzed peer-reviewed journal articles, conference proceedings and academic reports published between 2019 and 2024. The review applied a multi-stage synthesis process, categorizing studies based on theoretical perspectives, contextual barriers, AI technology characteristics and research methodologies. Findings The study identifies several barriers to AI integration in EHR systems in developing countries, including infrastructural limitations, interoperability challenges, workforce shortages, data fragmentation and regulatory gaps. Despite these challenges, AI-powered EHR solutions, such as machine learning, natural language processing (NLP), predictive analytics and computer vision, demonstrate potential for improving health-care workflows, reducing medical errors and enhancing real-time decision-making. The findings emphasize the need for regulatory frameworks, AI literacy programs, public–private partnerships (PPPs) and open-source AI models to support sustainable AI integration. Originality/value This study contributes to the literature by systematically reviewing AI integration in EHR systems within resource-limited settings, an area that remains underexplored. Unlike previous studies focused on AI implementation in high-income countries, this research provides a context-specific analysis tailored to developing countries. The study introduces a structured roadmap for AI-driven health-care transformation, offering policy recommendations and practical insights for health-care providers, AI developers and policymakers. The findings support digital health strategies aimed at enhancing patient outcomes and optimizing health-care information management in low-resource environments.

The integration of Artificial Intelligence (AI) in digital marketing has revolutionized the way businesses interact with customers and optimize their marketing strategies. AI technologies, such as machine learning, natural language processing, and predictive analytics, have enabled marketers to deliver personalized experiences, improve customer engagement, and achieve measurable outcomes. However, this advancement is accompanied by challenges, including ethical concerns, data privacy issues, and the need for skilled professionals. This paper explores the opportunities and challenges presented by AI in digital marketing, providing an in-depth analysis of its potential to reshape the industry. By examining current applications and identifying barriers to implementation, this study aims to contribute to the growing body of knowledge on the intersection of AI and digital marketing. This research paper delves into the integration of artificial intelligence (AI) in digital marketing practices. It scrutinizes the strategies utilized, hurdles encountered, and forthcoming pathways for leveraging AI technologies, including machine learning, natural language processing, and predictive analytics, to refine marketing campaigns and augment customer engagement. By analyzing contemporary trends and emerging innovations, this paper offers insights into the evolving landscape, challenges and opportunities of AI-driven digital marketing.