Use of Artificial Intelligence (AI) in Tertiary Institution Libraries in Jigawa State, Nigeria: Prospects, Requirements, and Challenges.

Purpose This study examines how secondary school administrators can lead ethical artificial intelligence (AI) integration within environments demanding technological innovation and educational value preservation. Design/methodology/approach The study conducted a scoping review of literature (2018–2025) to analyze administrative functions across four established leadership dimensions: instructional, managerial, strategic, and relational. Sources were obtained from academic databases and grey literature, with 21 sources selected based on relevance to secondary education and administrative practice. Analysis is grounded in foundational leadership scholarship while examining contemporary AI integration challenges. Findings The analysis reveals a misalignment between AI's most frequent use (relational leadership functions) and where it may be most appropriately suited (managerial and strategic functions). AI integration creates distinct opportunities and risks across each leadership dimension, with equity concerns emerging consistently. Communication represents the primary AI use, despite being the most fundamentally human aspect of educational leadership. Cognitive offloading risks emerge when administrators delegate critical thinking tasks to AI systems, potentially attenuating leadership capabilities essential for educational effectiveness. Research limitations/implications This study relies on secondary data collection and English-language sources, creating Western-centric bias and limiting generalizability beyond North American contexts. The corpus of 21 sources reflects the nascent research state in this emerging field. The rapid evolution of AI capabilities means current findings may prove transitional as technology advances. Future empirical research should examine long-term cognitive effects of AI reliance on administrators, stakeholder trust implications when AI-mediated communications are detected, differential equity impacts across diverse school communities, cross-cultural implementation patterns, and effectiveness of hybrid governance approaches for AI integration in educational leadership. Practical implications Findings support implementing hybrid governance models that combine regulatory oversight with participatory decision-making between administrators and stakeholders. Professional development programs must balance AI literacy training with preserving human capabilities essential for authentic educational leadership. Administrator preparation programs require redesign to address cognitive offloading risks while maintaining relationship-building and cultural competence development. Educational leaders should prioritize AI applications in managerial and strategic functions while preserving human judgment in relational leadership contexts. Policy frameworks must address equity concerns and provide guidance for schools serving vulnerable populations who currently receive less AI implementation support. Social implications AI implementation without critical examination risks amplifying existing educational inequities, particularly affecting Indigenous, newcomer, and racialized communities. Democratic participation in AI boundary-setting becomes essential for maintaining institutional trust and stakeholder engagement. The misalignment between AI deployment and appropriate applications threatens the relational foundations of effective educational leadership. Originality/value The study provides the first systematic examination of AI integration across established educational leadership dimensions in secondary school contexts, addressing a critical research gap given that nearly 60% of K-12 principals use AI tools while fewer than 10% of schools have established AI policies.

Global health and well-being largely depend on the pharmaceutical and medical device industries. Manufacturing and quality assurance (QA) processes are crucial to maintaining product efficacy, safety, and regulatory compliance in these sectors. Artificial intelligence (AI) integration presents ground-breaking opportunities to enhance these processes. This study aims to systematically assess the impact of AI on manufacturing and QA in these pharmaceutical and medical device industries. It examines the benefits, challenges, and ethical and legal implications of integrating AI. It offers a thorough understanding of how AI technology can and has been successfully integrated to enhance business operations. An extensive literature analysis was carried out to investigate AI's application, role, benefits, and challenges in manufacturing and quality assurance processes in both industries. Research was also conducted on emerging trends, future developments, and regulatory issues. Increased productivity, early detection of defects, safer and higher-quality goods, improved regulatory compliance, reduced costs, and more flexibility and scalability are some advantages of AI technologies. However, significant obstacles are also to overcome, such as high capital costs, data quality and availability issues, legacy system integration, ethical concerns about bias and data privacy, difficulties with regulatory compliance, and a lack of AI-skilled workers. Case studies show how AI has been utilized to guarantee regulatory compliance and optimize processes. AI integration has much to offer the pharmaceutical and medical device industries in terms of improved manufacturing and quality assurance procedures. By addressing restrictions and seizing novel opportunities, these industries can use AI's transformative potential to support innovation, enhance product quality and safety, ensure regulatory compliance, and improve global health outcomes.

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.

The article "Embracing Real AI: A Call to Action for Medical Physicists in Healthcare" urges medical physicists to prepare for the integration of artificial intelligence (AI) into healthcare practices, emphasizing their pivotal role in adapting to technological advancements. The authors advocate for embracing AI through advocacy, broadening perspectives, and enhancing coordination and communication. They propose an ABC strategy focusing on increasing educational initiatives, fostering interdisciplinary collaboration, and creating team collaboration to facilitate AI integration. The commentary highlights AI's potential in enhancing diagnostics, personalizing medicine, and automating routine tasks while addressing challenges such as data sharing and the role of federated learning. The article calls for medical physicists to lead in embracing AI, emphasizing continuous learning and collaboration to leverage its potential for improving healthcare and patient care. Medical physicists have consistently demonstrated strong interest in developing proficiency in the adoption of new technological advancements. The roots of the profession come from the radiation sciences, including radiation protection, radiation therapy, diagnostic imaging, and nuclear medicine.1 As science and technology continued to evolve, medical physicists' roles have extended into other non-radiation domains, such as non-ionizing-radiation-based imaging (ultrasound and magnetic resonance), molecular imaging, computer aided diagnosis (CAD), information technologies, and data science.2 In addition, medical physicists gradually have adopted increasingly more active roles in ensuring the professional education of other radiology/radiation oncology team members, maintaining high quality standards via quality assurance (QA) methods. They also play a major role in advising the hospital management on medical devices and software acquisition. The continuing expansion of these roles and responsibilities has put medical physicists on the forefront of embracing emerging technologies, making the profession one of the most technical and versatile in healthcare settings. Currently, as our field grows in importance, we medical physicists seek to continue to engage in significant ways to for increased contributions and roles in human health. This commentary/opinion urges medical physicists to prepare for their expanding roles in the field of AI and its implementation and oversight in clinical practice. Medical physicists must embrace "Real AI" to help integrate AI into healthcare practices. Conceptually we advocate for a strategy that involves Real AI through advocacy, broadening, and enhancing coordination/communication (an ABC strategy). In our current and future work medical physicists will use AI to automate routine tasks, allowing medical physicists to focus on more complex tasks. Furthermore, Medical Physics will use AI to enhance efficiency, safety, diagnostic and therapeutic applications, and for personalized medicine. However, as we have done in the past with other complex concepts (such as radiation), medical physicists need to be prepared for the potential risks and ethical dilemmas associated with AI, such as bias and lack of transparency. It will be important that Medical Physicists prepare for the rapidly changing AI landscape, and continue learning, gain hands-on experience, and collaborate with other AI experts in the healthcare environment. This paper aligns with the already approved guidance document developed by the AAPM in conjunction with International Atomic Energy Agency (IAEA)3 that discusses how medical physicists can ensure the effective implementation and management of AI systems. It is crucial for the Clinical Quality Management Program (CQMP) personnel to receive regular training and updates on relevant guidelines and legislation. Clear communication channels should be established with IT experts, vendors, and other stakeholders for smooth coordination.4 Comprehensive documentation should be developed to ensure compliance with contractual obligations and guidelines. The clinical team should be involved in acceptance testing and discussions, depending on the clinical purpose of the AI system.4 Protocols for data collection and curation should be established, along with the development of standardized validation datasets for performance evaluation.4 A system for monitoring updates to AI systems and models should be implemented, with the CQMP leading new acceptance/commissioning rounds for any updates. Lastly, mechanisms for continuous evaluation and improvement of the CQMP processes should be established, which could involve regular audits, feedback mechanisms from end-users, and incorporating lessons learned from previous rounds.4 Nowadays, major healthcare systems in the US consider their data as immensely valuable assets that require rigorous protection to ensure Health Insurance Portability and Accountability Act (HIPAA) compliance, as well as intellectual property considerations. It can be very difficult for researchers to share clinical data with vendors for development purposes without a significant return being specified to the institution, such as joint intellectual property or substantial grant funding. Instead, these healthcare systems encourage their researchers to commercialize their findings independently, allowing the institution to retain full rights to intellectual property. That said, the realization of federated learning would be a significant advancement. To achieve this, a powerful pre-trained model that would be adaptable to operation on different scales and in various clinical scenarios is necessary. It is plausible that local adaptation may not require substantial computing power or AI expertise. This concept is particularly intriguing and could be beneficial to smaller centers and clinics in underserved areas. However, the primary challenge is the cost. As we become more reliant on AI systems like OpenAI's ChatGPT or Google Gemini, we often overlook the fact that these conveniences come with a hefty price tag, costing billions of dollars to develop and maintain.5 As medical physicists we and other healthcare professionals can anticipate that AI will significantly transform healthcare, improving efficiency, accuracy, and the level of detail that can be extracted from imaging, and methods of therapy. These technological advancements are expected to bring immense value to the field, offering a new horizon in diagnostic and therapeutic capabilities. Yet, we also must recognize that it also introduces potential significant risks and ethical dilemmas. One of the primary concerns is the possibility of bias in AI, which can stem from the training data, the algorithms, or their application, leading to potentially detrimental effects on patient care. As medical physicists, we should acknowledge that the complexity and lack of transparency in AI decision-making processes present obstacles in terms of accountability and rectifying errors and requires greater oversight and responsibility. The integration of AI also has great capacity in redefining the role of medical physicists, impacting education and employment within the field. Addressing these issues necessitates the creation of ethical standards for AI in healthcare, emphasizing transparency, responsibility, and equity, with contributions from diverse stakeholders, including patients, medical professionals, and ethicists.6 Such measures are crucial to ensure the responsible utilization of AI in healthcare, and ultimately serve the best interests of patients and society. We anticipate that continued guidance from our professional societies will be helpful as our collective communities develop methods and approaches that help us learn, adopt, and employ AI responsibly. Advocacy: increase educational initiative, public awareness, and recommending processes at all levels of the clinical workforce, as well as patient engagement. Broadening Perspectives: encourage Interdisciplinary Collaborations that allow medical physicists to work with professionals from other disciplines such as computer science, data science, and biomedical engineering, to gain insights into different perspectives on AI applications in healthcare. This enables medical physicists to provide continuing education and connect the community with research opportunities. Improving Coordination and Communication through creating team collaboration: enhance communication with healthcare professionals, administrators, and patients by clearly defining and articulating the role of medical physicists in AI applications. Promote the sharing of knowledge, as exemplified by creating data repositories through contributions, to further creating the foundation of our understanding and application of AI in the field. We consider the concept of Real AI in our context to be aimed at providing and/or qualifying a ready AI product that has undergone a rigorous QA process, that is free of false additives and biases, with data carefully curated to represent the demographics and be attuned to the needs of the clinic, sourced with proper ingredients, and abiding by laws and regulations that can ensure the product serves the common health needs of patients and benefits the public's interest. What AI 'is' and what it 'is not' is a complex topic that warrants further exploration and understanding, but one vital for comprehension of what utility AI can fulfill in the clinical process, what its advantages and limitations are, and how it can be curated to perform in the clinical scenarios relevant to a particular radiology/radiation oncology practice. Multiple data-analysis algorithms have been created over the course of years, and not all of them qualify as AI.7 What distinction(s) lie in what constitutes AI? One possible interpretation is that AI is a system that can adapt to new data, or a system that generates insights driven by data. AI systems are designed to "learn" and adapt to new data and be stable over the course of introducing data perturbations or employ model adaptation mechanisms. AI systems can adjust the underlying data-processing mechanisms based on the input they receive, which allows them to improve their performance and make more accurate predictions or decisions over time. This is often achieved through techniques such as machine learning, where algorithms are trained on a dataset and then used to make predictions or decisions without being explicitly programed to perform the task.8 Understanding how such datasets are selected, what data needs to be fed into AI model to achieve desired results, and how to prevent common pitfalls and ethical conundrums associated with the use of AI models requires additional training that might yet be lacking in the traditional training of the radiology/radiation oncology adjacent specialists. The scope of involvement of each member of the team when it comes to AI integration into the clinic continues to be determined as the field rapidly evolves. When it comes to the role of medical physicists in conjunction with AI, an open discussion of the exact responsibilities is still ongoing, and feedback is encouraged from all the members of the community. So, what can medical physicists do? They can use AI to enhance quality improvement and safety by analyzing medical data to identify trends, patterns, and outliers.9 This can lead to the identification of areas for improvement or potential safety hazards and help them enter the realm of Responsible AI. AI can also improve diagnostic and therapeutic techniques by enhancing the quality of medical imaging and automating image interpretation.10 Furthermore, AI can help in integrating diagnostics, personalized medicine, and theragnostics by analyzing large datasets to tailor treatment plans to individual patients.11 This can lead to more effective and personalized care. AI can also automate routine tasks in medical physics, such as treatment planning and QA processes, leading to increased efficiency.12 Lastly, AI techniques like machine learning and deep learning can be leveraged for research and development to analyze complex datasets, discover patterns, and develop innovative techniques for disease detection, treatment, and monitoring.13 Whether it involves developing AI-driven solutions like automated segmentation, dose calculations, addressing intricate problems in the clinic, or potentially even contributing to open-source AI initiatives, such activities will empower medical physicists to enhance their skills and make tangible contributions to the advancement of healthcare. Embracing AI not only fosters a sense of accomplishment but also opens doors to the world of `automation' and scaling that will pervade all technologies of the future. The AHAIBC committee is at the center of bringing the medical physicist forward by developing curriculum concepts, bootcamps, and engendering engagement for our society. Integration of AI into the realm of medical physics education is critical, especially considering the potential significance of incorrect AI usage or misapplication. The physicist is responsible for installing and commissioning the AI software, ensuring the modeling is not biased, performing continuing QA on the hospital data and processes, and establishing efficient resource management. Embracing education in AI offers new benefits for medical physicists as it is already revolutionizing various industries and professional practices and we need to be equally prepared. One way to engage and prepare healthcare professionals for the upcoming AI wave is to start with the roots of quality safety and assurance. To do this, we should enable a comprehensive QA program that encompasses all clinical operations related to medical fields including radiology, nuclear medicine, and radiation oncology. Ensuring the safe operation of hardware, software, clinical operation processes and machinery is of utmost importance and one of the most crucial responsibilities of a medical physicist. A Real AI approach can be highly beneficial in achieving the goal of safe clinical implementation. Understanding the potential and limitations of AI serves as a cornerstone for fostering engagement not only within our profession but with other healthcare providers. Continuous learning and participation in hands-on experience are essential components for navigating the complexities of AI applications within healthcare. Collaboration, networking, and exploring AI's purpose and impact are equally vital in this journey. Additionally, some physicists may choose personal projects, embracing challenges in small groups, and actively contributing to AI-focused teams to amplify the motivation and expertise of our field. Insights through personal and collaborative opportunities ultimately provide for and encourage professional growth and innovation within our medical physics field. Some medical physicists may be able to attend specialty meetings and conferences dedicated to AI which further enriches their knowledge base and provides them avenues for fruitful collaboration. There are successful educational programs such as the Radiological Society of North America Artificial Intelligence (RSNA AI)-certificate program.14 Interdisciplinary cooperation and inter-institutional collaboration for AI experts is of paramount importance for integrating AI into medical physicists' practice on a larger scale, and mechanisms enabling this collaboration should be provided to the community. In summary, the authors believe that being prepared for and embracing the changes that AI is already bringing at the current time will benefit our community, healthcare, patient care, and society at large immediately and for the future. We are at a critical juncture, which can be considered a fourth industrial revolution, where AI and automation are applied more broadly. Medical physicists have a pivotal role to play in this revolution. We need to position ourselves at the forefront of 'Real AI' and lead the charge in this exciting new era. It is time for action, and we can take the first steps with potentially just a few ABCs. All authors contributed their efforts in writing and editing this call for action. ChatGPT search engine has been utilized to provide additional background to the subject of matter for illustrative purposes. The authors appreciate members of the Ad. The authors declare no conflicts of interest. The content for this call for action has been edited with the help of large language models ChatGPT and Google NotebookLM.

The integration of artificial intelligence (AI) in pathogenic microbiology has accelerated research and innovation. This study aims to explore the evolution and trends of AI applications in this domain, providing insights into how AI is transforming research and practice in pathogenic microbiology. We employed bibliometric analysis and topic modeling to examine 27,420 publications from the Web of Science Core Collection, covering the period from 2010 to 2024. These methods enabled us to identify key trends, research areas, and the geographical distribution of research efforts. Since 2016, there has been an exponential increase in AI-related publications, with significant contributions from China and the USA. Our analysis identified eight major AI application areas: pathogen detection, antibiotic resistance prediction, transmission modeling, genomic analysis, therapeutic optimization, ecological profiling, vaccine development, and data management systems. Notably, we found significant lexical overlaps between these areas, especially between drug resistance and vaccine development, suggesting an interconnected research landscape. AI is increasingly moving from laboratory research to clinical applications, enhancing hospital operations and public health strategies. It plays a vital role in optimizing pathogen detection, improving diagnostic speed, treatment efficacy, and disease control, particularly through advancements in rapid antibiotic susceptibility testing and COVID-19 vaccine development. This study highlights the current status, progress, and challenges of AI in pathogenic microbiology, guiding future research directions, resource allocation, and policy-making.

The integration of Artificial Intelligence (AI) into metallurgy is transforming metal production and processing, optimizing efficiency, quality, and sustainability. AI-driven technologies, including machine learning and big data analytics, enhance traditional metallurgical practices by optimizing production processes, predicting material properties, and automating decision-making. AI applications in metallurgy include real-time process control, predictive maintenance, and smart manufacturing, leading to improved operational efficiency and reduced environmental impact. Machine learning algorithms analyze vast datasets to forecast outcomes in casting, smelting, and alloy development, minimizing defects and maximizing resource utilization. Predictive maintenance systems further enhance efficiency by reducing unplanned downtime and extending equipment lifespan. AI also accelerates materials discovery by simulating and predicting alloy behaviors, fostering innovation in high-performance materials for industries such as aerospace and automotive. Despite its transformative potential, AI implementation in metallurgy faces challenges such as data quality, infrastructure adaptation, and workforce training. However, as advancements continue, AI is poised to revolutionize metallurgy by fostering more efficient, sustainable, and precise metal production processes. This paper explores the current applications, benefits, and challenges of AI in metallurgy, highlighting its role in shaping the industry’s future

The integration of Artificial Intelligence (AI) in project management has emerged as a transformative approach, revolutionizing traditional practices by enhancing efficiency, decision-making, and risk management. Despite its potential, organizations face significant challenges, including high implementation costs, concerns over data privacy, and resistance to change, which hinder effective adoption. The purpose of this study is to explore emerging trends, key applications, and challenges of AI in project management, while also evaluating its impact on improving risk management, resource allocation, and decision-making in complex projects. The study employs a systematic literature review (SLR) methodology, adhering to the PRISMA protocol, to analyze peer-reviewed articles from MDPI, IEEE, Science Direct, and Emerald databases, published between 2018 and 2024. Keywords combined with Boolean operators were used to filter relevant studies, ensuring a balanced and focused selection of high-quality publications. The results reveal AI's capacity to proactively identify risks, adapt to dynamic project environments, and optimize resource allocation, ultimately enhancing decision-making efficiency and project outcomes. However, challenges such as implementation costs and resistance to organizational change remain critical barriers. The implications suggest that while AI significantly enhances project management, addressing these challenges is essential for broader adoption and scalability. This research concludes that AI is a game-changer in project management, offering insights into emerging trends and critical challenges. Future research should focus on developing scalable, cost-effective AI solutions to overcome adoption barriers, thereby extending the benefits of AI integration across diverse industries.

This study explores the transformative integration of Artificial Intelligence (AI) into sustainable finance, highlighting its potential to redefine financial practices in alignment with Environmental, Social, and Governance (ESG) criteria. Through a systematic review of current practices and an analysis of AI's applications, challenges, and strategic frameworks, the research elucidates AI's role in enhancing financial operations' efficiency, accuracy, and sustainability. Findings indicate that AI technologies, such as the Financial Maximally Filtered Graph (FMFG) algorithm, significantly improve the processing and analysis of vast datasets, facilitating sustainable investment decisions. However, the integration of AI into sustainable finance is accompanied by ethical, regulatory, and technological challenges. The study proposes strategic recommendations for overcoming these barriers, emphasizing the development of robust policy frameworks, industry best practices, and a balanced approach to AI integration. The conclusion underscores the promise of AI in advancing sustainable finance, offering insights for stakeholders on navigating the complexities of this integration to achieve a more sustainable and resilient financial system.

Purpose This paper aims to explore the paradigm shift in leadership and strategic management driven by the integration of responsible artificial intelligence (AI) in healthcare. It explores the evolving role of leadership in adapting to AI technologies while ensuring ethical governance, transparency and accountability in healthcare decision-making. Design/methodology/approach This study conducts a comprehensive review of current literature, case studies and industry reports to evaluate the implications of responsible AI adoption in healthcare leadership. It focuses on key areas such as AI-driven decision-making, resource optimisation, crisis management and patient care, while also addressing challenges in integrating AI technologies effectively. Findings The integration of AI in healthcare is transforming leadership from traditional, experience-based decision-making to data-driven, AI-enhanced strategies. Responsible leadership emphasises addressing ethical concerns such as bias, transparency and accountability. AI technologies improve resource allocation, crisis management and patient care, but challenges such as workforce resistance and the need for upskilling healthcare professionals remain. Practical implications Healthcare leaders must adopt a responsible leadership framework that balances AI’s potential with ethical and human-centred care principles. Recommendations include developing AI literacy programmes for healthcare professionals, ensuring inclusivity in AI algorithms and establishing governance policies that promote transparency and accountability in AI applications. Originality/value This paper provides a critical, forward-looking perspective on how responsible AI can drive a paradigm shift in healthcare leadership. It offers novel insights into the integration of AI within healthcare organisations, emphasising the need for leadership that prioritises ethical AI usage and promotes patient well-being in a rapidly evolving digital landscape.

The integration of Artificial Intelligence (AI) into contemporary art curation is revolutionizing how exhibitions are designed, artworks are selected, and audiences interact with art. Through technologies such as machine learning, natural language processing (NLP), and computer vision, AI enables curators to analyze vast collections, predict visitor preferences, and construct dynamic exhibition experiences. This paper examines the dual nature of AI in art curation—its potential to enhance creativity and efficiency, and the ethical, technical, and philosophical challenges it introduces. Drawing upon case studies from global museums and digital art platforms, the research identifies key application domains, evaluates curatorial outcomes, and analyzes stakeholders’ perceptions of AI-assisted practices. Quantitative data from 30 institutions are supplemented by qualitative analysis to explore how AI reshapes curatorial authority, audience engagement, and the meaning-making process. Findings reveal that while AI fosters innovation and accessibility, it also risks algorithmic bias, cultural homogenization, and diminished human interpretive agency. The paper concludes that sustainable AI curation requires a human–machine collaborative framework emphasizing transparency, inclusivity, and cultural sensitivity.

Introduction: The integration of artificial intelligence (AI) into higher education is swiftly revolutionizing pedagogical methodologies, in conjunction with learning processes and research paradigms. The interdisciplinary potential of AI within academic settings was examined in this study, employing a case study conducted at the University of Tirana. Through the utilization of bibliometric analysis and survey-based research, this study comprehensively investigates the swiftly emerging trends in AI applications, students' significant familiarity with AI technologies, as well as the substantial challenges impeding broader adoption. The bibliometric analysis highlights significant exponential growth in AI research, particularly within pivotal domains such as finance and accounting, thereby emphasizing the swiftly increasing relevance of blockchain and automation. The survey indicates a robust enthusiasm among students for AI in educational settings. More than 90 percent of students actively incorporate AI tools in their project work. Nonetheless, resource limitations and ethical considerations, including privacy, data security, and algorithmic bias, pose considerable challenges to the widespread adoption of AI, despite the prevailing enthusiasm. Objective: The aim of this research is to examine the incorporation of Artificial Intelligence (AI) within the realm of higher education. This analysis concentrates on the applications, advantages, and challenges associated with AI, with the intent of advancing interdisciplinary research and educational methodologies at the University of Tirana. Theoretical Framework: This research expands upon the principles of AI adoption within the educational sector, alongside an examination of ethical considerations and multidisciplinary collaboration. Theories pertaining to technological integration and adaptive learning systems serve as the foundational framework for comprehending the implications of AI in the realm of education. Method: The methodology employs bibliometric analysis to examine AI-related research trends utilizing data from SCOPUS and conducts a survey to assess students' familiarity with and perceptions of AI. Data collection was facilitated through bibliometric instruments and an online survey incorporating Likert-scale and open-ended questions. Results and Discussion: The results underscore an increased focus on artificial intelligence (AI) and blockchain within scholarly research, wherein students exhibit considerable engagement and interest in AI applications. Nevertheless, limitations in resources and ethical issues, including privacy and bias, persist as primary challenges. The discourse underscores the imperative for investment in infrastructure and the incorporation of ethical education. Research Implications: This research highlights the imperative for higher education institutions to integrate artificial intelligence tools, cultivate adaptive curricula, and address ethical considerations in order to adequately prepare students for a future shaped by AI. The implications of these findings also pertain to educational policy and the formulation of interdisciplinary research strategies. Originality/Value: The study contributes by delivering a comprehensive bibliometric analysis and gives insights into student engagement with artificial intelligence. Its significance resides in presenting actionable recommendations to enhance the integration of artificial intelligence in higher education.

Pharmaceutical regulatory processes are undergoing a significant transformation, largely driven by the integration of Artificial Intelligence (AI). What was once a slow, paper-intensive system is now evolving into a smarter, faster, and more predictive framework. This journal explores the shift from traditional methods to AI-powered tools that enhance various aspects of regulatory affairs, including regulatory submissions, safety monitoring, labeling, and compliance. While AI offers clear advantages—such as improved speed, accuracy, and efficiency—it also raises critical concerns regarding data privacy, fairness, and regulatory approval. These issues must be carefully considered to ensure that AI applications in regulatory processes remain transparent, unbiased, and compliant with established standards. This journal provides real-world insights and practical analyses, highlighting both the opportunities and challenges of AI in regulatory affairs. It emphasizes the need for responsible innovation that prioritizes patient safety and adheres to ethical practices. By exploring the potential of AI while acknowledging its limitations, this journal aims to guide the pharmaceutical industry toward an AI-enhanced regulatory future that balances technological progress with the highest standards of care.

This article examines the impact of artificial intelligence on the formation of future doctors while studying biophysics at a medical university. The role of medical professionals is changing as this article examines the multidimensional impact of AI on clinical practice and medical education. The integration of artificial intelligence into medical training enhances learning effectiveness through the use of personalized educational tools such as adaptive learning platforms and virtual patient simulators that enable medical students to practice diagnostic and therapeutic skills in a safe environment. The research focuses on the need to rethink medical curricula to include artificial intelligence literacy and ethics issues so that future doctors can effectively collaborate with artificial intelligence systems while paying special attention to patient-centered care. This study shows that artificial intelligence will undoubtedly empower future doctors and will not be able to replace the human factor in healthcare. It is seen as a transformative tool, and careful integration into medical practice is required. This article provides recommendations for policy makers, medical professionals, and healthcare organizations, and suggests using artificial intelligence to create a more efficient, ethical, and empathetic generation of doctors. This study explores the possibilities and challenges of artificial intelligence in medicine and contributes to the ongoing debate about the future of healthcare, while also highlighting the need for a balanced approach to technology adoption. He advocates an active and ethical position that ensures that AI complements the art and science of medicine, and aims to protect these fields from any potential compromises caused by technology.

Underground coal gasification(UCG) is a technology that converts solid coal into gaseous fuel, playing a crucial role in achieving carbon emission reduction and promoting green environmental protection. To advance the intelligent extraction of underground coal gasification, this article elaborates on the key influencing indicators of the technology. It reviews the progress in the application of artificial intelligence(AI) in underground coal gasification, comparing the suitability of various algorithms for the process. By integrating case studies, the article identifies existing problems in the application of artificial intelligence in gasification and suggests future development directions. Research indicates that the integration of artificial intelligence and underground coal gasification has been applied in site selection, state prediction, intelligent process control, environmental protection, and risk assessment, providing new analytical approaches for the complex underground coal gasification process. However, the process is intricate, with most algorithms relying on localized data and lacking generalization and transferability. There is also a lack of adaptive control systems capable of responding to dynamic changes in underground conditions. Future efforts in underground coal gasification should focus on developing adaptive intelligent control systems to enhance process stability and safety, strengthening dual-driven modeling that combines mechanisms and data for accurate dynamic predictions, and deepening interdisciplinary integration of geology, engineering, and artificial intelligence to establish an integrated intelligent prediction model.

This paper reviews the latest advances in the application of Artificial Intelligence (AI) technologies in the domain of surgical oncology, and the challenges they face. The application of AI in surgical oncology encompasses a wide range of medical image analysis, preoperative planning and simulation, surgical navigation and robotic-assisted surgery, as well as postoperative monitoring and follow-up. The utilisation of computer-aided diagnosis (CAD) systems, deep learning models (e.g., U-Net), three-dimensional reconstruction techniques, and virtual reality (VR) has led to a substantial enhancement in the accuracy of tumour detection, the precision of surgical planning, and the safety of surgical operations. Concurrently, the integration of AI with postoperative data analysis and prediction models furnishes patients with personalised recovery plans and an enhanced prognosis. However, the application of AI in surgical oncology still faces challenges such as data privacy and security, algorithm performance validation, and multidisciplinary cooperation. In the future, with the continuous optimisation of the technology and the integration of multimodal data, AI is expected to drive oncology treatment in the direction of greater precision and personalisation, bringing about a revolutionary change in oncology surgery.