Artificial Intelligence and News: Opportunities, Trends and Challenges — A Systematic Literature Review

Purpose This study aims to provide a systematic literature review on the intersection of artificial intelligence (AI) and justice, analysing the evolution of AI driven innovation in the legal sector within the Justice 5.0 paradigm. The research classifies existing literature into three main discussion topics – predictive justice, human–machine combination and robot judges – through a multidisciplinary approach that includes technological, ethical and legal perspectives. By exploring AI’s transformative role, this study highlights the innovative integration of technology in legal decision-making and policy development. Design/methodology/approach The research follows the PRISMA methodology to systematically review 140 relevant papers from the Scopus database. It combines qualitative and quantitative analyses, including bibliometric mapping, visualization techniques and bibliographic coupling. A theory-building approach is adopted to identify key trends, challenges and opportunities in AI-driven innovation, emphasizing its impact on modern judicial systems. Findings The review highlights the increasing innovation in legal processes through AI applications, offering enhanced efficiency and predictive capabilities while raising ethical concerns regarding bias, transparency and human oversight. The findings categorize AI-based innovations in justice into three key areas: (1) predictive justice, where AI tools analyse jurisprudential data to support legal decision-making; (2) human–machine collaboration, where AI enhances legal professionals’ efficiency in case management and legal research and (3) the concept of robot judges, which explores the potential and limitations of fully automated legal decisions. The study also emphasizes the transition from Justice 4.0 to Justice 5.0, promoting human-centred AI innovation in judicial systems. Research limitations/implications While this study comprehensively maps AI-driven innovations in justice, the rapid evolution of AI technologies may introduce new developments beyond the scope of this review. Future research should focus on empirical studies to assess the real-world effectiveness and fairness of AI-driven legal innovation. Practical implications The findings offer valuable insights for policymakers, legal practitioners and AI developers, guiding the responsible implementation of AI innovations in justice systems. Understanding the interplay between technological innovation and law is crucial for ensuring transparent and equitable legal decision-making. Social implications The integration of AI-based innovations in justice potentially improves legal accessibility and efficiency while also posing risks related to algorithmic bias and the erosion of human judicial discretion. Addressing these concerns is vital for fostering trust in AI-assisted legal frameworks. Originality/value This work contributes to the literature by offering a systematic classification of AI-based innovations in justice, providing a structured overview of technological advancements and ethical concerns. It establishes a foundational reference for future research and policymaking, highlighting critical challenges and opportunities in AI-enhanced legal innovation.

Globally, the use of artificial intelligence (AI) is expanding exponentially. The issue of managing intellectual property in AI is raised by this surge. Discussions and moderating have taken place, but no resolution has been reached. The issue of whether the work created by an AI should get a special status still exists. When it comes to the control of IPR in artificial intelligence, there are a few oddities. The ownership of patents and copyrights is in doubt, and there are serious worries about the consequences of violation. With the development of technology, there is no certainty on the law, despite existing international accords and conventions. In the absence of artificial intelligence (AI), intellectual property (IP) rules have, up to this point, treated IP as a product of human cognition. The current boom in AI, which has seen the development of IP from the "intelligence" of software, has upended this base. But the non availability of law on works created by artificial intelligence the law is left far behind to deal with the anomalies created by the intersection of “Artificial intelligence and intellectual property rights”. This research paper examines the role of intellectual property theories in addressing AI anomalies, focusing on copyright, patent, and trademark laws. It analyzes utilitarianism, labour theory and personality theory, examining authorship and ownership in AI-generated works and potential conflicts between human creators and machine-generated content. In this research paper researcher(s) will- Analyse various theories of Intellectual properties to tackle the anomolities created by the intersection of IPR and AI. Criticize and suggest changes in the theories to make them helpful for emerging frameworks on AI and IPR.

Artificial Intelligence (AI) and Machine Learning (ML) have transformed the landscape of project management and contributed significantly to improving efficiency, decision-making, and optimizing resource allocation. Although there exists a number of research examining the integration and utilization of AI and ML into project management, the fragmented literature highlights the need for a systematic literature review to consolidate current knowledge, identify emerging trends, and examine AI’s role in project management. This study aims to critically analyze the existing literature to identify opportunities for, enablers of, and barriers to AI adoption, providing a comprehensive framework to guide future research and practice. A systematic literature review (SLR) following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines revealed three key themes: The Knowledge Ecosystem in Project Management: In the Era of AI, The Intersection of AI and Humanity in Project Management, and Integrating AI into Project Management and Landscaping. The findings highlight AI’s transformative effects on forecasting accuracy, risk mitigation, stakeholder collaboration, and safety management while addressing challenges such as integration with legacy systems, data quality issues, and resistance to change. The research presents valuable insights for both researchers and practitioners, facilitating the navigation of adoption barriers, capitalizing on enablers, and unlocking AI’s potential to reshape project management practices across industries.

This study aims to explore and map the knowledge structure and research trends on the application of Artificial Intelligence (AI) for good governance in universities using a bibliometric analysis approach. A total of 373 scientific articles published between 2010 and 2025 were retrieved from the Web of Science database and analyzed through bibliographic coupling and co-word analysis techniques. This research focuses on key topics such as digital governance, corporate finance and social responsibility, corporate governance, and the application of machine learning for financial distress and bankruptcy prediction. The analysis highlights how AI is increasingly seen as a tool to enhance transparency, accountability, financial management, and institutional resilience within higher education institutions. Moreover, AI applications in data analytics, decision-making, and performance monitoring are shown to provide universities with actionable insights that can improve governance processes and optimize resource allocation. Additionally, the integration of AI into strategic planning and decision-making supports informed leadership and effective policy formulation, fostering better institutional performance. However, the study also identifies critical challenges, including ethical concerns, data privacy issues, and the digital divide, which could hinder the effective implementation of AI technologies in university governance. These concerns emphasize the need for a well-regulated framework to ensure that AI is deployed responsibly, ensuring inclusivity and equity in its use. The findings provide valuable insights for policymakers, university leaders, and researchers in formulating strategic, data-driven governance solutions that can address current challenges while advancing institutional goals in the digital era. This research contributes to the growing body of knowledge on the intersection of AI, governance, and higher education, offering a foundation for future investigations.

Purpose This study presents a systematic literature review of halal food (HF) customer behaviour using the Scopus and Web of Science (WoS) databases. This study aims to identify trends, methodologies, theoretical developments and determinants of HF customer behaviour in Indonesia and Malaysia, as well as potential future research directions. Design/methodology/approach This study extends the bibliometric and systematic literature review covering all variables in customers purchasing HF by extracting the Scopus and WoS databases. We used the SLR method via the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) model. A total of 43 samples related to customer behaviour towards HF articles were identified and reviewed. Findings This study found 43 articles discussing variables or factors that influence consumers’ behaviour towards HF in Indonesia and Malaysia. Quantitative methods and extended TPB theory are most widely used to measure the determinants of customer behaviour towards HFs. Religiosity, attitude, halal awareness and trust are the most dominant factors that significantly influence customer behaviour towards HF. This study suggests several potential future research directions. Research limitations/implications The HF industry, marketers and governments in Indonesia and Malaysia must ensure that their products meet consumer expectations by implementing halalan thayyiban and strengthening HF marketing strategies based on halal-labelled food. Originality/value This study is the first to examine the factors determining customer behaviour towards HF in Indonesia and Malaysia using a systematic literature review method from Scopus and the WoS database.

The intersection of artificial intelligence (AI) and industrial ecology (IE) is gaining significant attention due to AI's potential to enhance the sustainability of production and consumption systems. Understanding the current state of research in this field can highlight covered topics, identify trends, and reveal understudied topics warranting future research. However, few studies have systematically reviewed this intersection. In this study, we analyze 1068 publications within the IE–AI domain using trend factor analysis, word2vec modeling, and top2vec modeling. These methods uncover patterns of topic interconnections and evolutionary trends. Our results identify 71 trending terms within the selected publications, 69 of which, such as “deep learning,” have emerged in the past 8 years. The word2vec analysis shows that the application of various AI techniques is increasingly integrated into life cycle assessment and the circular economy. The top2vec analysis suggests that employing AI to predict and optimize indicators related to products, waste, processes, and their environmental impacts is an emerging trend. Lastly, we propose that fine‐tuning large language models to better understand and process data specific to IE, along with deploying real‐time data collection technologies such as sensors, computer vision, and robotics, could effectively address the challenges of data‐driven decision‐making in this domain.

Artificial Intelligence (AI) is increasingly adopted by organizations to innovate, and this is ever more reflected in scholarly work. To illustrate, assess and map research at the intersection of AI and innovation, we performed a Systematic Literature Review (SLR) of published work indexed in the Clarivate Web of Science (WOS) and Elsevier Scopus databases (the final sample includes 1448 articles). A bibliometric analysis was deployed to map the focal field in terms of dominant topics and their evolution over time. By deploying keyword co-occurrences, and bibliographic coupling techniques, we generate insights on the literature at the intersection of AI and innovation research. We leverage the SLR findings to provide an updated synopsis of extant scientific work on the focal research area and to develop an interpretive framework which sheds light on the drivers and outcomes of AI adoption for innovation. We identify economic, technological, and social factors of AI adoption in firms willing to innovate. We also uncover firms' economic, competitive and organizational, and innovation factors as key outcomes of AI deployment. We conclude this paper by developing an agenda for future research.

This study conducts a bibliometric analysis of global trends in 'artificial intelligence studies in veterinary medicine'. The analysis aims to summarise the publications of researchers from various disciplines related to artificial intelligence in veterinary medicine, thereby predicting future trends of AI in this field. The primary objective of the study is to investigate publications pertaining to artificial intelligence in veterinary medicine worldwide and to analyse trends and future developments in this area. This bibliometric study examines artificial intelligence research in veterinary medicine conducted worldwide from 1990 to 2024. To achieve this, a search using the keywords 'artificial intelligence' and 'veterinary medicine' was performed in the Web of Science (WOS) database, resulting in the identification of 1497 studies. After excluding irrelevant publications and those outside the scope of articles, a total of 1400 articles were included in the analysis. The data collection process utilised titles, author names, publication years, journal names, and citation counts. All textual data were analysed using VOSviewer software to ensure accuracy and reliability. In this study, analyses conducted through text mining and data visualisation techniques (e.g., bubble maps) facilitated a clearer understanding of the results. This study presents information about 1400 articles obtained from the WOS database and a total of 44,700 citations for these articles. The average number of citations per article is 32, with an H-index of 74. A rapid increase in both the number of articles and citations has been observed since 2019. The majority of the articles (30%) were published in the fields of veterinary sciences, artificial intelligence, and computer sciences. The United States, Taiwan and the United Kingdom are the leading countries, accounting for 84% of the published articles in this field. Additionally, 12% of the articles were published in the area of veterinary sciences, and 85% of the articles fall within the SCI-Expanded category. The findings of our study indicate that there are numerous active researchers in the field of artificial intelligence in veterinary medicine and that research in this area is steadily increasing. This bibliometric analysis highlights global trends and significant works in artificial intelligence within veterinary medicine, providing valuable insights into the future directions of research in this field. As the analysis aims solely to identify trends and patterns in the literature, it does not intend to evaluate the applicability of the subject matter. Analysis of Global Trends: This study comprehensively analyses the global trends and effects of research on artificial intelligence in veterinary medicine. In this context, it contributes to the identification of significant changes and developments in the literature. Rapidly Spreading Research: Research on artificial intelligence in veterinary medicine has rapidly expanded in recent years, and this trend is expected to continue. The increase in studies indicates an expansion of knowledge and applications in this field. Diagnostic and Therapeutic Tools: Artificial intelligence research serves as a valuable tool in veterinary medicine, particularly in improving the diagnosis and treatment processes for various diseases. This contributes to the development of more effective methods for animal health and care. Increasing Number of Publications: The number of studies on artificial intelligence in veterinary medicine worldwide is increasing each year. Notably, after the Covid-19 pandemic, there has been a significant rise in publications in this field. This indicates that the importance of artificial intelligence in both human and animal health has grown, with the pandemic intensifying research interest. Prominent Countries: Among the countries examined in the study, the United States, Taiwan, England, and Germany emerged as leaders in this research area. Conversely, it was noted that some countries have very few or no academic publications in the field of artificial intelligence in veterinary medicine.

This study explores how artificial intelligence (AI) affects communication practices and social adaptation among residents in ancient Chinese villages, particularly through its role in tourism development and cultural heritage preservation. It seeks to understand the dual impact of AI as a driver of rural transformation and a disruptor of cultural continuity. A systematic literature review was conducted using five academic databases: JSTOR, Google Scholar, Web of Science, Scopus, and ProQuest. Studies published between 2021 and 2024 were selected based on strict inclusion criteria focusing on rural tourism in China, AI technologies, and resident adaptation. Boolean logic was applied to combine keywords and refine search precision. AI supports tourism through smart management platforms, virtual reality heritage tours, and intelligent visitor services. These technologies enhance cultural engagement and economic development. Translation tools improve communication between locals and tourists, while AI-based hospitality systems strengthen rural infrastructure. However, findings also reveal that older residents struggle to adapt to AI, and overuse of AI marketing risks cultural commodification. Traditional storytelling practices shift due to generational differences in AI use. AI serves as a powerful force for rural economic and cultural advancement, but it also introduces risks of community disconnection and cultural erosion. Sustainable integration of AI requires balancing innovation with preservation of traditional values. The study recommends inclusive AI education programs, ethical frameworks for AI deployment, and culturally sensitive tourism governance policies. Efforts should focus on protecting intangible heritage while enabling digital growth. This study is original in its specific focus on the intersection of AI, tourism, and social adaptation in ancient Chinese villages a topic largely underrepresented in current literature. It addresses the gap in understanding rural responses to digital transformation. Academic contributions include theoretical insights on AI-driven cultural adaptation. Practically and politically, the research informs policymakers, tourism planners, and technology developers working at the intersection of heritage and innovation.

Artificial intelligence (AI) has emerged as an effective solution to alleviate excessive carbon emissions in sustainable building projects. Although there are numerous applications of AI, there is no state-of-the-art review of how AI applications can reduce net-zero carbon emissions (NZCEs) for sustainable building projects. Therefore, this review study aims to conduct a systematic literature and science mapping review of AI applications in NZCEs for sustainable building projects, thereby expediting the realization of NZCEs in building projects. A mixed-method approach (i.e., systematic literature review and science mapping) consisting of four comprehensive stages was used to retrieve relevant published articles from the Scopus database. A total of 154 published articles were retrieved and used to conduct science mapping analyses and qualitative discussions, including mainstream research topics, gaps, and future research directions. Six mainstream research topics were identified and discussed. These include (1) life cycle assessment and carbon footprint, (2) practical applications of AI technology, (3) multi-objective optimization, (4) energy management and energy efficiency, (5) carbon emissions from buildings, and (6) decision support systems and sustainability. In addition, this review suggests six research gaps and develops a framework depicting future research directions. The findings contribute to advancing AI applications in reducing carbon emissions in sustainable building projects and can help researchers and practitioners to realize its economic and environmental benefits.

Purpose The study aims to identify trends, methodologies, theory development, factors influencing Zakat compliance behaviour, lessons for Zakat institutions and potential future research directions. Design/methodology/approach This study conducted a bibliometric and systematic literature review, along with content analysis, to address the research objectives by extracting data from the Web of Science (WoS) and Scopus databases (2009–2025). The authors used the SLR method through the Preferred Reporting Items for Systematic Reviews and Meta-Analyses model. Based on the established inclusion and exclusion criteria, the authors identified a final set of 34 articles related to the topic, which were subsequently reviewed and analysed. Findings The findings indicate the research trends in Zakat compliance behaviour in the Scopus and WoS databases from 2009–2025, with significant growth observed in 2018. The leading source titles primarily include the Journal of Islamic Accounting and Business Research, the International Journal of Ethics and Systems and the Jurnal Pengurusan. Furthermore, quantitative methods and the extended Theory of Planned Behaviour (TPB) model are mostly used to measure the determinants of Zakat compliance behaviour. The most significant determinants of Zakat compliance behaviour include religiosity, trust, Zakat knowledge, beliefs (perceived taqwa), peer influence, attitudes, subjective norms (SN) and perceived behavioural control (PBC). These findings also offer valuable insights for Zakat institutions to achieve the objectives of an Islamic economic system, social welfare and SDG 1 (no poverty). Research limitations/implications This study teaches Zakat institutions to build good governance and marketing strategies for Zakat in a digital context. The government also needs to provide regulations for the Zakat system to support economic development. The study provides insights into the literature on Zakat compliance behaviour, contributing to the advancement of research on Zakat compliance in Muslim countries. Originality/value This study is the earlier exploration of the factors determining Zakat compliance behaviour using a systematic literature review method and content analysis from the Scopus and WoS databases.

Purpose- This study aims to provide a bibliometric review of publications where the terms 'digital marketing' and 'artificial intelligence' are used together. Leading publications, authors, countries, and institutions in the Web of Science (WoS) database have been examined to achieve this goal. Additionally, this article investigates the combined use of digital marketing and artificial intelligence. Furthermore, it aims to offer insights into artificial intelligence strategies for marketing that businesses can employ. Methodology- The research employs the technique of bibliometric analysis. The Bibliometrix package within R Studio and its web-based component, Biblioshiny, were utilized for analysis. Searches were conducted in the Web of Science database using the keywords 'Digital Marketing' and 'Artificial Intelligence' in the title, abstract, and keywords sections. Findings- As a result of the analysis, a total of 60 publications authored by 140 researchers and distributed across 46 journals between 2017 and 2023 were identified. Examination of the included publications reveals frequent usage of terms such as 'artificial intelligence,' 'creativity,' 'analytics,' 'impact,' 'expertise,' 'social networks,' 'big data,' 'governance,' 'success,' and 'AI.' Upon scrutinizing the authors' countries, India emerged as the leading contributor, followed by Spain and the USA. Moreover, Finland (370), Spain (92), and France (58) had the highest citation counts. Conclusion- This research aims to contribute to researchers interested in working in digital marketing and artificial intelligence by examining its past and present. For this purpose, 60 relevant studies from the literature were systematically reviewed and analyzed across various categories. Additionally, the examined publications' conceptual, intellectual, and social structures were illuminated. Keywords: Digital marketing, artificial intelligence, AI systems, bibliometric analysis, bibliometrix. JEL Codes: M15, M30, M31

In the digital age, the intersection of artificial intelligence (AI) and higher education (HE) poses novel ethical considerations, necessitating a comprehensive exploration of this multifaceted relationship. This study aims to quantify and characterize the current research trends and critically assess the discourse on ethical AI applications within HE. Employing a mixed-methods design, we integrated quantitative data from the Web of Science, Scopus, and the Lens databases with qualitative insights from selected studies to perform scientometric and content analyses, yielding a nuanced landscape of AI utilization in HE. Our results identified vital research areas through citation bursts, keyword co-occurrence, and thematic clusters. We provided a conceptual model for ethical AI integration in HE, encapsulating dichotomous perspectives on AI’s role in education. Three thematic clusters were identified: ethical frameworks and policy development, academic integrity and content creation, and student interaction with AI. The study concludes that, while AI offers substantial benefits for educational advancement, it also brings challenges that necessitate vigilant governance to uphold academic integrity and ethical standards. The implications extend to policymakers, educators, and AI developers, highlighting the need for ethical guidelines, AI literacy, and human-centered AI tools.

Introduction:Technical burdens and time-intensive review processes limit the practical utility of video capsule endoscopy (VCE). Artificial intelligence (AI) is poised to address these limitations, but the intersection of AI and VCE reveals challenges that must first be overcome. We identified five challenges to address. Challenge #1: VCE data are stochastic and contains significant artifact. Challenge #2: VCE interpretation is cost-intensive. Challenge #3: VCE data are inherently imbalanced. Challenge #4: Existing VCE AIMLT are computationally cumbersome. Challenge #5: Clinicians are hesitant to accept AIMLT that cannot explain their process.Methods:An anatomic landmark detection model was used to test the application of convolutional neural networks (CNNs) to the task of classifying VCE data. We also created a tool that assists in expert annotation of VCE data. We then created more elaborate models using different approaches including a multi-frame approach, a CNN based on graph representation, and a few-shot approach based on meta-learning.Results:When used on full-length VCE footage, CNNs accurately identified anatomic landmarks (99.1%), with gradient weighted-class activation mapping showing the parts of each frame that the CNN used to make its decision. The graph CNN with weakly supervised learning (accuracy 89.9%, sensitivity of 91.1%), the few-shot model (accuracy 90.8%, precision 91.4%, sensitivity 90.9%), and the multi-frame model (accuracy 97.5%, precision 91.5%, sensitivity 94.8%) performed well.Discussion:Each of these five challenges is addressed, in part, by one of our AI-based models. Our goal of producing high performance using lightweight models that aim to improve clinician confidence was achieved.

This review draws on a systematic literature review and bibliometric analysis to examine how Digital Twins (DTs), Extended Reality (XR), and Artificial Intelligence (AI) support the reconfiguration of Cyber–Physical Systems (CPSs) in modern manufacturing. The review aims to provide an updated overview of these technologies’ roles in CPS reconfiguration, summarize best practices, and suggest future research directions. In a two-phase process, we first analyzed related work to assess the current state of assisted manufacturing reconfiguration and identify gaps in existing reviews. Based on these insights, an adapted PRISMA methodology was applied to screen 165 articles from the Scopus and Web of Science databases, focusing on those published between 2019 and 2025 addressing DT, XR, and AI integration in Reconfigurable Manufacturing Systems (RMSs). After applying the exclusion criteria, 38 articles were selected for final analysis. The findings highlight the individual and combined impact of DTs, XR, and AI on reconfiguration processes. DTs notably reduce reconfiguration time and improve system availability, AI enhances decision-making, and XR improves human–machine interactions. Despite these advancements, a research gap exists regarding the combined application of these technologies, indicating potential areas for future exploration. The reviewed studies recognized limitations, especially due to diverse study designs and methodologies that may introduce risks of bias, yet the review offers insight into the current DT, XR, and AI landscape in RMS and suggests areas for future research.