ARTIFICIAL INTELLIGENCE AND ROAD SAFETY: REVOLUTIONIZING TRANSPORTATION

Objective: Medical tourism presents unique opportunities for delivering patient-centered healthcare, yet the role of artificial intelligence (AI) in enhancing management processes within the patient journey remains underexplored. This study aims to investigate AI’s potential to optimize patient experience and operational efficiency in medical tourism by proposing a comprehensive conceptual framework. Methods: A structured literature review was conducted, analyzing a total of 100 publications (95 peer-reviewed articles and 5 industry reports) published between 2007 and 2023. Thematic analysis was employed to synthesize findings and develop the AI-Enhanced Medical Tourism Patient Journey Model (AI-MTPJM), integrating AI applications across key stages of the patient journey. Results: The AI-MTPJM framework outlines five main stages of the medical tourism patient journey: (1) Information Search, (2) Planning and Reservation, (3) Travel and Treatment, (4) Post-treatment Follow-up, and (5) Feedback and Loyalty. Various AI technologies such as virtual assistants, predictive analytics, real-time monitoring, telemedicine, and sentiment analysis are mapped to these stages. These tools contribute to personalized services, improved operational workflows, and enhanced patient satisfaction in international medical tourism destinations. Conclusion: The AI-MTPJM serves as a strategic bridge between patient-centered service design and data-driven healthcare management. While it holds significant potential for strengthening the competitiveness of medical tourism providers, challenges such as data privacy, cost, and infrastructure requirements must be carefully managed. This framework offers practical insights for stakeholders and lays the groundwork for future empirical research on AI integration in medical tourism management.

Artificial Intelligence (AI) is increasingly transforming higher education across the globe by reshaping teaching, learning, research, and institutional management. In developing countries such as Tanzania, the integration of AI into Higher Education Institutions (HEIs) presents both significant opportunities and complex challenges. Despite the presence of infrastructural and resource limitations, AI has the potential to revolutionize the educational landscape by improving access to quality learning materials, supporting data-driven decision-making, and enhancing administrative efficiency. This study explores the multifaceted impacts of AI on Tanzanian HEIs, with a specific focus on its current applications, benefits, challenges, and policy implications. The research draws upon recent empirical studies and secondary data to analyze how AI technologies such as intelligent tutoring systems, predictive analytics, and automated assessment tools contribute to personalized learning experiences and improved academic outcomes. Moreover, the study identifies the major barriers to AI adoption, including inadequate technological infrastructure, limited institutional capacity, insufficient AI-related policies, and persistent ethical concerns surrounding data privacy and algorithmic bias. Findings reveal that although AI adoption in Tanzanian HEIs is on the rise, the implementation remains fragmented and uneven across institutions. To fully harness AI’s transformative potential, the study recommends the development of comprehensive national AI policies, investments in digital infrastructure, and targeted capacity-building programs for educators and administrators. Additionally, the research emphasizes the need for ethical frameworks that promote fairness, inclusivity, and transparency in AI utilization. Overall, this paper underscores the necessity of a strategic and policy-driven approach to ensure that AI contributes effectively and equitably to the advancement of higher education in Tanzania.

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.

The rapid advancements in Artificial Intelligence (AI) and Big Data Analytics have significantly transformed financial markets, enabling more precise, data-driven, and strategic decision-making. This study explores the profound impact of AI-driven analytics in financial decision-making, focusing on how machine learning algorithms, predictive analytics, and automation enhance market efficiency, risk assessment, and investment strategies. The research highlights how AI-powered Big Data Analytics processes vast amounts of structured and unstructured financial data to identify patterns, trends, and anomalies that influence market movements. It further examines how financial institutions leverage AI for fraud detection, sentiment analysis, and algorithmic trading, leading to more informed and efficient investment decisions. Through a survey-based approach, this study collects data from financial professionals, analysts, and investors to evaluate their perspectives on AI’s role in improving data accuracy, reducing uncertainty, and optimizing financial strategies. The findings provide valuable insights into how AI-driven analytics contributes to minimizing risks, enhancing forecasting accuracy, and supporting regulatory compliance in financial markets. The study concludes that the integration of AI in Big Data Analytics is revolutionizing financial decision-making by improving efficiency, reducing human biases, and enabling real-time strategic actions. However, challenges such as data privacy concerns, ethical considerations, and the need for advanced AI governance frameworks remain crucial areas for future exploration. Keywords—Artificial Intelligence, Big Data Analytics, Financial Markets, Strategic Decision-Making, Algorithmic Trading, Risk Assessment, Predictive Analytics.

Artificial Intelligence (AI) is revolutionizing the accounting profession, offering transformative capabilities for automating tasks, enhancing decision-making, and improving financial accuracy. As AI becomes integral to accounting practices, it brings both significant opportunities and notable ethical challenges. This review examines the intersection of AI and ethical accounting, providing insights into how professionals can navigate the evolving landscape. The adoption of AI in accounting introduces opportunities for increased efficiency and accuracy. AI systems can handle repetitive tasks such as data entry, reconciliation, and transaction categorization, freeing accountants to focus on strategic activities. Advanced AI algorithms can analyze large volumes of financial data to identify patterns, detect anomalies, and provide real-time insights, enhancing decision-making and forecasting accuracy. Moreover, AI-driven predictive analytics can aid in risk assessment and management, helping organizations to anticipate and mitigate potential financial threats. However, the integration of AI in accounting also raises significant ethical concerns. One of the primary challenges is ensuring transparency and accountability in AI decision-making processes. As AI systems often operate as "black boxes," understanding and explaining their outputs can be difficult, potentially leading to issues of trust and compliance. Ethical accounting necessitates that AI systems be designed with transparency in mind, providing clear explanations for their decisions and actions. Data privacy and security represent another critical ethical consideration. The extensive use of financial data by AI systems necessitates robust measures to protect sensitive information from breaches and unauthorized access. Accountants must ensure that AI systems comply with data protection regulations and ethical standards, safeguarding the confidentiality and integrity of financial data. Bias and fairness in AI algorithms are also pressing ethical issues. If not properly addressed, biases in AI systems can lead to unfair outcomes, such as biased financial recommendations or discriminatory practices. Ensuring fairness requires ongoing monitoring and evaluation of AI systems to identify and mitigate biases. In conclusion, while AI offers substantial benefits for the accounting profession, it also presents ethical challenges that must be carefully managed. Accountants must navigate these challenges by promoting transparency, ensuring data privacy and security, and addressing biases in AI systems. By doing so, the accounting profession can harness the potential of AI while upholding ethical standards and maintaining public trust. Keywords: AI, Ethical Accounting, Navigating, Challenges, Opportunities.

The aim of the study is to interrogate artificial intelligence and Kogi state governorship election: issues and challenges for 2017. This study investigates the role and impact of artificial intelligence (AI) in the 2017 Kogi State governorship election, focusing on the utilization, challenges, and implications of AI technologies. Utilizing secondary data from academic sources, election reports, and media coverage, the study examines how AI was employed for voter authentication, data analytics, and real-time reporting. The analysis reveals that AI technologies such as Biometric Voter Authentication, Data Analytics, AI-driven Social Media Analysis, and Real-time Reporting Systems were employed with varying frequencies, demonstrating their roles in enhancing electoral processes. However, significant challenges emerged, including technical failures, insufficient training, and resistance from political actors, which impacted AI effectiveness. The study also highlights the mixed influence of socio-political factors on AI, noting that while political tensions and interference had contrasting effects, public trust in technology and institutional capacity played critical roles in shaping outcomes. Additionally, concerns about data privacy, including misuse and inadequate security, were prominent, underscoring the need for improved data management practices. Recommendations include investing in advanced technologies, enhancing training and infrastructure, and fostering transparency and trust to better leverage AI in future elections. This comprehensive examination provides valuable insights into the integration of AI in electoral processes and offers actionable strategies for overcoming identified challenges.

In a rapidly evolving healthcare environment, artificial intelligence (AI) is transforming diagnostic techniques and personalized medicine. This is also seen in osseous biopsies. AI applications in radiomics, histopathology, predictive modelling, biopsy navigation, and interdisciplinary communication are reshaping how bone biopsies are conducted and interpreted. We provide a brief review of AI in image- guided biopsy of bone tumours (primary and secondary) and specimen handling, in the era of personalized medicine. This article explores AI's role in enhancing diagnostic accuracy, improving safety in biopsies, and enabling more precise targeting in bone lesion biopsies, ultimately contributing to better patient outcomes in personalized medicine. We dive into various AI technologies applied to osseous biopsies, such as traditional machine learning, deep learning, radiomics, simulation, and generative models. We explore their roles in tumour-board meetings, communication between clinicians, radiologists, and pathologists. Additionally, we inspect ethical considerations associated with the integration of AI in bone biopsy procedures, technical limitations, and we delve into health equity, generalizability, deployment issues, and reimbursement challenges in AI-powered healthcare. Finally, we explore potential future developments and offer a list of open-source AI tools and algorithms relevant to bone biopsies, which we include to encourage further discussion and research.

Artificial intelligence (AI) has emerged as a promising technology that can revolutionize surgical decision-making (SDM). This comprehensive review aims to explore the current state of AI in SDM and highlight its benefits, challenges, and future directions. The integration of AI in SDM offers numerous advantages. AI algorithms can analyze medical images, such as radiographs, computed tomography scans, and magnetic resonance imaging, to detect abnormalities and assist in pre-operative assessments. By leveraging electronic health records, AI can provide personalized surgical recommendations based on patient-specific data.Additionally, AI can analyze genetic data to assess genetic predispositions and tailor treatment plans accordingly. Intra-operatively, AI can aid in real-time analysis of surgical videos and imaging, helping surgeons identify critical structures and guide precise incisions. AI algorithms can also monitor physiological indicators to detect early signs of complications and predict outcomes, improving intra-operative decision-making. Post-operatively, AI can analyze vital signs, imaging, and patient data to detect complications, provide outcomes analysis, and facilitate personalized patient care. However, challenges and limitations exist. Data quality and availability, interpretability of AI algorithms, data security, integration into surgical workflows, and regulatory considerations are important challenges. Addressing these challenges involves ensuring data privacy, developing transparent AI models, establishing robust infrastructure, engaging clinicians, and establishing regulatory frameworks. AI-powered surgical robots and systems can enhance surgical precision and automation. Improvements in interpretability and explainability foster trust and ethical considerations. Also, data sharing and collaboration advancements could refine AI algorithms' accuracy and generalizability. Personalized medicine and precision surgery are achieved through AI integration. Also, education and training could benefit from AI-powered decision support systems.

Smart digital agriculture results from the convergence of digital technologies and artificial intelligence (AI), which is bringing about a revolutionary change in the agricultural sector. This innovative strategy transforms conventional farming methods using AI algorithms, machine learning models, and cutting-edge sensor technology. Smart digital agriculture reduces environmental impact, increases agricultural yields, and optimizes resource use using data-driven insights and predictive analytics. Fundamental to it all is the capacity to examine enormous amounts of data from many sources, such as weather stations, aerial photography, soil sensors, and historical documents. Through AI-powered analytics, this data is transformed into actionable intelligence, enabling farmers to make informed decisions on precision farming techniques like site-specific nutrient management, targeted irrigation, and tailored pest control strategies. Moreover, AI-driven supply chain optimization, predictive market analytics, and climate change adaptation strategies contribute to a more resilient and sustainable food production system, empowering farmers to anticipate and mitigate potential risks. The integration of AI and digital technologies transcends individual farms, offering a holistic approach to agricultural management that fosters collaboration, knowledge-sharing, and continuous improvement. As the world grapples with the pressing need for sustainable food production and environmental stewardship, smart digital agriculture emerges as a game-changer, harnessing the boundless potential of AI to create a more efficient, productive, and eco-friendly agricultural ecosystem.

People in low- and middle-income countries face many obstacles when trying to access fertility treatments. These challenges include high costs, the need for specialized medical facilities, and cultural beliefs that may discourage seeking help. This paper explores how artificial intelligence (AI) and automation could help overcome some of these barriers and make fertility treatments more widely available. It examines how AI may improve the accuracy, efficiency, and consistency of different steps in fertility treatments, such as choosing the healthiest embryos, analyzing sperm, evaluating eggs, and planning treatment. The paper also discusses how automation could simplify laboratory procedures, from growing embryos and freezing them for future use to the possibility of fully automating the in vitro fertilization (IVF) process, which could help lower costs and make these treatments more accessible. Finally, this paper addresses the ethical and practical challenges associated with using these technologies, including potential biases in AI, equitable access, quality control, data privacy, job implications, and cultural sensitivities.Lay summaryThis paper explores how AI and automation could help make fertility treatments more accessible in low- and middle-income countries (LMICs). Many individuals and couples face difficulties conceiving, and assisted reproductive technology (ART) – which includes procedures such as intrauterine insemination (IUI) and IVF – offers them a chance to build a family. However, ART is often out of reach in LMICs due to high costs, the need for specialized medical facilities, and cultural barriers. AI and automation have the potential to improve accuracy, efficiency, and consistency in ART procedures, such as embryo selection, sperm and egg assessment, and treatment planning. Automation could also streamline laboratory processes, including embryo culture and freezing, which may eventually lead to more affordable and scalable fertility treatments. By reducing human error and dependence on highly trained specialists, AI-driven technologies could help lower costs and make ART available to more people. This paper also considers the ethical and practical challenges of using AI in reproductive medicine, including potential biases in AI algorithms, fairness in access to treatment, data privacy, workforce impact, and cultural sensitivities. Fertility treatments can be life-changing for those struggling with infertility due to medical conditions, age, or personal circumstances. They also provide opportunities for same-sex couples and individuals who want to start a family. However, in LMICs, infertility is often surrounded by social stigma, economic hardship, and limited medical resources, making access to ART even more difficult. By integrating AI and automation into reproductive medicine, it may be possible to break down these barriers, reduce costs, and create more inclusive and accessible fertility care. These advancements have the potential to bring hope to millions who dream of parenthood but currently lack the means to pursue it.

Artificial intelligence (AI) is rapidly advancing and holds both opportunities and risks for the social work profession. This review article provides an analysis of the potential applications and implications of AI technologies for social work practice, education, research, and ethics. The article highlights promising uses of AI tools like predictive risk models, virtual simulations, chatbots, and data analytics to enhance social work services and training. However, it also critically examines the ethical considerations and social justice concerns related to the integration of AI in social work, including issues of privacy, transparency, accountability, consent, bias, displacement of roles, and threats to human dignity. To responsibly leverage AI's benefits while mitigating its risks, the article offers recommendations for social workers to collaborate on AI design, follow ethical protocols, pursue education on AI, and conduct further research. Key areas for future research identified include studying client perspectives on AI, testing the efficacy of AI systems for social work tasks, auditing algorithms for bias, and analyzing AI's impact on social work employment. In conclusion, while acknowledging AI's significant potential, the article emphasizes that purposeful oversight, participatory design, and adherence to social work values must remain priorities to ensure AI promotes dignity, autonomy, and justice. This review provides a balanced, evidence-based analysis to inform discussions on optimizing AI's integration in the social work profession.

In the realm of proactive medical care, digital twin technology is rising to the forefront as a paradigm shift due to the combination of customised healthcare, 6G connectivity, and artificial intelligence (AI). This study investigates AI-driven, 6G-powered healthcare digital twin technology, focusing on patient-centric monitoring and predictive analytics. It must clearly outline the research objectives, such as real-time health data integration, adaptive decision-making, and improved treatment personalization. The proposed methodology should describe the use of 6G-enabled ultra-reliable low-latency communication, AI models for digital twin construction, and secure data management frameworks. Improved diagnostic accuracy, fast response time to clinical emergencies, and improved resource allocation in healthcare systems were main outcomes that are presented in this abstract. The technical contributions of this work with the integration of AI, 6G, and digital twin technologies are highlighted in the end of this study, and this may support the next-generation intelligent healthcare solutions. For self-learning healthcare digital twin, a model that leverages 6G-enabled medical informatics, real-time data streams, and artificial intelligence (AI) analytics is presented in this study. This presented model may support in clinical diagnosis, and classifying damages earlier. A real-time updated digital copy of the patient was created by the suggested method by integrating multimodal data from various sources including imaging, vital signs, consumer electronic devices such as smartwatches, wearable sensor metrics, and electronic health records (EHRs). The patient physiological patterns and previous injury profiles are constantly learnt by the integration of digital twin and deep learning (DL) algorithms. This integration is effective in predicting the occurrence, kind, and severity of injuries and internal trauma, falls, cardiovascular events, and neurodegenerative insults. The digital twin and healthcare providers are connected seamlessly with 6G communication infrastructure, guaranteeing ultra-low latency and great dependability. This allows for pre-hospital and critical care settings to implement instantaneous feedback loops, as well as for remote diagnostics and adaptive clinical decision-making. From home health monitoring solutions to smartphones and wearables, consumer electronics are ubiquitous and serve as common data collectors, greatly increase access and enabling the instant collection of accurate data using a digital twin. To keep data private while training AI models on dispersed healthcare data, the architecture offers federated learning. Based on severity level, the injury types are classified by a multi-layered decision engine, and this may promote a customized, context-aware responses like alerting emergency teams, modifying treatment regimens, or demanding robotic surgical support. The AI-driven, 6G-powered healthcare digital twin revolutionizes the diagnosis of injury, and it establish a path for a precise, preventive, and predictive medicine. Furthermore, scaling efficiencies were reduced by 95.81%, while reaction times decreased to 96.88%, resulting in significant gains. This technology is particularly helpful for populations who are elderly, at risk, or located far away, as it decreases reaction time, reduces diagnostic error, and provides scalable, continuous care.

Abstract: This paper examines the potential role of artificial intelligence in addressing the critical issue of case backlogs plaguing the Indian judiciary. The integration of artificial intelligence within the Indian judiciary presents a transformative potential for enhancing efficiency, accessibility, and consistency in legal processes, yet it also raises complex ethical, technical, and institutional challenges. This paper systematically analyses the prospective applications of artificial intelligence in the judicial system, including case management, legal research, predictive analytics, automated drafting of judgments, and alternative dispute resolution mechanisms. Artificial intelligence-powered tools can significantly reduce the backlog of cases, which currently plagues the Indian courts, by streamlining procedural workflows, assisting judges in identifying precedents, and providing data-driven insights for case outcomes, thereby facilitating informed and timely decision-making. Moreover, artificial intelligence can democratize access to legal information for the public and lower-income litigants, offering real-time guidance on legal procedures and potential remedies, which aligns with the constitutional mandate of equal justice. However, the deployment of Artificial intelligence in judicial processes faces multifaceted challenges. Key concerns include algorithmic bias, transparency, accountability, and the interpretative nature of law, which often requires nuanced human judgment that Artificial intelligence may not fully replicate. Data privacy and security issues are particularly critical in the Indian context, where sensitive personal and institutional information must be safeguarded against misuse. Institutional resistance, inadequate digital infrastructure, and the lack of specialized training for judicial officers and legal professionals further complicate Artificial intelligence adoption. Additionally, the regulatory and ethical frameworks governing Artificial intelligence in legal contexts remain nascent, necessitating the development of robust guidelines to ensure that Artificial intelligence complements rather than compromises judicial independence and the rule of law. This study also explores international experiences with Artificial intelligence -assisted judicial processes, drawing lessons from jurisdictions such as Singapore, the United States, and the European Union, to assess the feasibility, risks, and best practices for India. By adopting a structured methodology that combines doctrinal legal analysis, technological evaluation, and empirical assessment, the paper identifies strategic pathways for integrating Artificial intelligence in the judiciary while mitigating associated risks. The findings underscore that while Artificial intelligence has the potential to revolutionize judicial efficiency, it cannot substitute the discretionary, interpretative, and ethical responsibilities inherent in judicial decision-making. Therefore, a phased, carefully regulated, and human-centric approach is recommended, wherein Artificial intelligence functions as an augmentative tool to assist judges, lawyers, and administrative staff without undermining procedural fairness or legal accountability. Overall, this study contributes to the scholarly discourse on technology-enabled justice in India, highlighting the dual imperative of leveraging Artificial intelligence for efficiency gains while safeguarding the foundational principles of transparency, equity, and the rule of law, and offers policy recommendations, implementation strategies, and frameworks for continuous monitoring and evaluation of AI interventions in the Indian judicial system.

The integration of Artificial Intelligence (AI) in healthcare is revolutionizing diagnostics, offering important opportunities while emerging challenges. This essay explores the potential and obstacles of AI-based diagnostics. Key problems include technological and ethical issues such as data privacy, compatibility of devices , and regulatory issues. Ethical considerations also arise around patient privacy, accountability for AI errors, legal frameworks, and algorithm transparency. Despite these challenges, AI showcases substantial opportunities for the improvement of diagnostics and treatment techniques. Its capabilities include earlier disease detection, personalized treatment pathways, and healthcare professional support, particularly in radiology, pathology, and genomics. Additionally, AI can help and reduce the cost of healthcare access by streamlining resources, optimizing staff deployment, and facilitating remote monitoring and telemedicine, especially for developing countries. With the analysis of data from wearable devices and enabling remote consultations, AI helps us in the proactive management of chronic conditions. AI-based diagnostics can help overcome long term healthcare challenges, enhancing precision and treatment efficiency. A balanced approach is essential, addressing ethical, privacy, and legal considerations while emphasizing AI’s role as a support for healthcare professionals. Future research should focus on AI algorithm transparency, ethics, and the expansion of telemedicine, especially in underserved areas.

This study explores the transformative potential of Artificial Intelligence (AI) in education by analyzing its impact on student learning outcomes. Through a comprehensive literature review, the research synthesizes current findings on the integration of AI in educational settings, examining both the benefits and challenges it presents. The study explores into AI's role in personalizing learning experiences, enhancing student engagement, and improving academic performance. Ethical considerations such as data privacy and algorithmic bias are also assessed. This research also identifies existing gaps in the literature and suggests avenues for future inquiry, contributing to a deeper understanding of how AI can be effectively and responsibly integrated into education to optimize student success.