MEDICAL AND PHARMACEUTICAL RESEARCH USING ARTIFICIAL INTELLIGENCE

Artificial intelligence (AI) is a branch of computer science that allows machines to work efficiently, can analyze complex data. The research focused on AI has increased tremendously, and its role in healthcare service and research is emerging at a greater pace. This review elaborates on the opportunities and challenges of AI in healthcare and pharmaceutical research. The literature was collected from domains such as PubMed, Science Direct and Google scholar using specific keywords and phrases such as ‘Artificial intelligence’, ‘Pharmaceutical research’, ‘drug discovery’, ‘clinical trial’, ‘disease diagnosis’, etc. to select the research and review articles published within the last five years. The application of AI in disease diagnosis, digital therapy, personalized treatment, drug discovery and forecasting epidemics or pandemics was extensively reviewed in this article. Deep learning and neural networks are the most used AI technologies; Bayesian nonparametric models are the potential technologies for clinical trial design; natural language processing and wearable devices are used in patient identification and clinical trial monitoring. Deep learning and neural networks were applied in predicting the outbreak of seasonal influenza, Zika, Ebola, Tuberculosis and COVID-19. With the advancement of AI technologies, the scientific community may witness rapid and cost-effective healthcare and pharmaceutical research as well as provide improved service to the general public.

With the rapid development of artificial intelligence (AI) technology, the field of education has ushered in new opportunities and challenges. Especially in college English teaching, the application of AI provides new possibilities for improving the teaching effect. This paper aims to explore how to effectively improve the quality of college English teaching in the context of AI. By analyzing the current situation and problems in college English teaching and combining them with the development of AI technology, this paper puts forward corresponding strategies and schemes, aiming to provide reference for college English teaching.

Artificial intelligence (AI) has been applied in many fields, including science, technology, and medicine. However, vascular surgeons face many obstacles and limitations in using and applying AI because of their limited understanding of computer science, programming languages, and complex AI technologies, such as machine learning, deep learning, and artificial neural networks. This article describes the basic knowledge of AI, applications of AI technologies in vascular surgery, and the use of smart wearable devices. Finally, the challenges and limitations of AI are discussed as essential issues hindering its widespread application in vascular surgery.

With the gradual maturity of artificial intelligence (AI) technology, it is urgent to change and innovate the traditional media communication paradigm. Current research and application areas include language processing, image recognition, AI systems, intelligent video technology, and text recognition. With the rapid development of artificial intelligence technology, it is used in all walks of life in society. All sectors of society have also set off the exploration and application of artificial intelligence. In the field of news communication, artificial intelligence technology brings opportunities and challenges to the news industry in publishing news content forms and organizations. Exploring the new development trends and countermeasures of the news industry in the era of artificial intelligence has become a key topic for the news communication industry and academia. With the support of the huge communication network, the information transmission of short video news is more abundant. While people obtain information, it can also enhance the interactivity of the news. In a fast-paced society, short video news is undoubtedly closer to life. However, due to some reasons, human factors lead to incomplete access to information. In this paper, an intelligent neural network is proposed to be applied to an intelligent robot or intelligent system so that it can better obtain information to edit short video news and disseminate it, and compare it with the existing such machines or systems on the market today. This paper analyzes the development of short video news dissemination based on artificial intelligence and proposes improvement strategies based on specific examples.

Abstract. Problem. The rapid advancement of artificial intelligence (AI) has significantly impacted various domains, particularly education. AI-driven tools are increasingly being integrated into teaching methodologies, assessment frameworks, and curriculum design. However, despite the growing research interest in Artificial Intelligence in Education (AIED), the field remains fragmented, lacking a comprehensive analysis of dominant research themes and emerging trends. Understanding these trends is crucial for optimizing AI applications in education and shaping future developments in the field. Goal. The primary goal of this study is to analyze the current landscape of AIED research by identifying key thematic clusters, evaluating dominant research directions, and predicting future trajectories in AI-driven education. Methodology. This study employs network analysis, topic modeling, and global research trend evaluation to analyze scientific publications in AIED. The Latent Dirichlet Allocation (LDA) algorithm is applied to classify research articles into thematic categories, enabling the identification of primary topics shaping the field. Data is sourced from the Web of Science database, and hierarchical clustering techniques are used to determine semantic similarities between thematic models. Results. The analysis reveals five major research clusters in AIED, including AI applications in medical education, machine learning in education, adaptive learning technologies, large language models in learning, and student-centered AI-driven educational strategies. The study identifies increasing trends in the adoption of generative AI and chatbot-based learning support, while traditional AI-driven assessment methodologies show moderate growth. The results also highlight strong interconnections between AI-driven personalization and intelligent tutoring systems, emphasizing the shift toward adaptive and student-centric learning environments. Originality. This research provides a structured and systematic analysis of AIED trends, leveraging advanced topic modeling techniques. Unlike previous studies focusing on isolated AI applications in education, this study presents a holistic view of thematic structures in AIED research, offering a quantitative and data-driven approach to understanding the evolution of the field. Practical Value. The findings can serve as a strategic reference for educators, policymakers, and AI developers in shaping future educational technologies. By identifying key thematic directions, this research supports evidence-based decision-making for integrating AI into educational systems. The results can also guide further research in AIED, helping scholars explore emerging AI-driven teaching methodologies, adaptive learning models, and ethical considerations in AI-powered education.

Revolutionizing agriculture: A comprehensive review on artificial intelligence applications in enhancing properties of agricultural produce.

Artificial Intelligence in the American Healthcare Industry: Looking Forward to 2030

This introductory article opens the “Applications of AI in the Semiconductor Industry” section by giving a holistic overview of the development of artificial intelligence (AI) technologies applied to the industry. Historically, the semiconductor industry has utilised complex automation for many tasks and areas, especially in repetitive work and uniform processes. The high need for flexibility in manufacturing, increased diversification of products, complexity, and demand for more autonomous operations, including human-machine interaction, have led to a strong push towards using AI technologies in semiconductor manufacturing. AI technologies are applied in semiconductor product development, digitised product definition (DPD), knowledge management system for risk assessment and root cause analysis, image recognition for inspection and defect classification in front end (FE) and back end (BE) applications for anomaly detection in process chains. Deep learning (DL) and Machine Learning (ML) techniques have given a new stimulus to semiconductor industry research to address the unique challenges for semiconductor manufacturing as the technologies nods are evolving and the number of process parameters to be controlled is increasing. In the end, 106the article introduces the four contributions to this section, highlighting the use of AI, computer vision, neural networks (NNs) in various use cases in semiconductor manufacturing processes.

Artificial Intelligence (AI) emerges as a critical technological intervention that promises to revolutionize educational approaches, pedagogical methodologies, and learning experiences. Traditional educational models are increasingly becoming inadequate in addressing the complex, dynamic skills requirements of a rapidly evolving global knowledge economy. The integration of AI technologies represents a pivotal mechanism for reimagining educational delivery, personalization, and strategic innovation. This systematic review examines the conceptual structure of artificial intelligence in education (AIED) research, focusing on AI applications, research topics, and research design elements. Following PRISMA guidelines, the study analyzed peer-reviewed articles published between 2020 and 2024 from major academic databases. The PRISMA methodology consists of a 27-item checklist and guidelines to enhance the quality of reporting as well as the credibility of systematic reviews. The findings reveal that AI applications in education primarily cluster into four categories: Emerging technologies, intelligent evaluation and management, personalized tutoring and adaptive learning, and profiling and prediction. The investigation of research topics indicates a strong emphasis on system and application design, followed by studies on the adoption and acceptance, impacts, and challenges of AIED. Analysis of research designs shows that while descriptive and survey methods dominate the field, there is limited use of experimental and mixed methods approaches. The theoretical foundations of AIED research demonstrate its multidisciplinary nature, drawing from fields such as education, psychology, mathematics, and sociology, though many studies lack robust theoretical grounding. Research contexts predominantly focus on higher education and K12 settings, with minimal attention to preschool education. This review contributes to the understanding of AIED's current landscape and suggests future research directions, including the need to incorporate emerging AI technologies, strengthen research in underrepresented educational contexts, enhance methodological rigour, deepen theoretical contributions, and foster interdisciplinary collaboration. These insights are particularly valuable for educational stakeholders navigating the transformation toward Education 4.0 and beyond.

As artificial intelligence (AI) technologies rapidly evolve, psychiatric nurses need to be prepared for using and developing this technology in care delivery. The purpose of this research is to explore psychiatric nurses' perspectives regarding AI applications in supporting both direct and indirect care processes. A descriptive qualitative method was used in this study. The study sample consisted of 28 psychiatric nurses who agreed to participate in the research between April and October 2024, who had completed or were currently pursuing their master's or doctoral education. Data were collected through individual face-to-face/online meetings (via Google Meets application) with psychiatric nurses. Audio recordings were taken during the interviews. The audio recordings were transcribed verbatim and content analysis was performed. In this study, psychiatric nurses' perspectives regarding AI applications in care support were categorised under five themes: (1) Limitations of artificial intelligence in meeting patient needs, (2) Enhancing psychiatric nursing care support, (3) Future Perspectives on enhancing psychosocial care, (4) The challenging path of integration into education and care, (5) Uncertainty, unknowns, risks and concerns. This study reveals psychiatric nurses' insights regarding the potential of AI, particularly in terms of developing psychosocial care, and their predictions about challenges that may be encountered in this process. Taking a pioneering role in integrating AI into care processes, psychiatric nurses may open the doors to a new era while contributing to the development of the profession.

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.

The COVID-19 pandemic caused by the SARS-CoV-2 virus has spread rapidly worldwide, leading to a global outbreak. Most governments, enterprises, and scientific research institutions are participating in the COVID-19 struggle to curb the spread of the pandemic. As a powerful tool against COVID-19, artificial intelligence (AI) technologies are widely used in combating this pandemic. In this survey, we investigate the main scope and contributions of AI in combating COVID-19 from the aspects of disease detection and diagnosis, virology and pathogenesis, drug and vaccine development, and epidemic and transmission prediction. In addition, we summarize the available data and resources that can be used for AI-based COVID-19 research. Finally, the main challenges and potential directions of AI in fighting against COVID-19 are discussed. Currently, AI mainly focuses on medical image inspection, genomics, drug development, and transmission prediction, and thus AI still has great potential in this field. This survey presents medical and AI researchers with a comprehensive view of the existing and potential applications of AI technology in combating COVID-19 with the goal of inspiring researchers to continue to maximize the advantages of AI and big data to fight COVID-19.

As a disruptive technology, artificial intelligence has affected global economic development on multiple levels. With the widespread application of artificial intelligence technology in various industries, the market demand for artificial intelligence technology and applications continues to grow, providing college students with opportunities to participate in emerging industries. College students can create new products and services and achieve technological innovation with the continuous development of artificial intelligence technology. This paper adopts the method of literature review, and combines the existing artificial intelligence background to explore the possibility of using artificial intelligence as an auxiliary tool for cultivating college students' innovation and entrepreneurship ability. Combining cutting-edge theories with a variety of practical explorations, this paper finally forms a set of methodologies for computer and electronic engineering college students to complete learning and entrepreneurial practice through artificial intelligence in the stage of university education.

With the emergence of GPT developed by the OPENAI team, artificial intelligence is in a thriving stage of development. This article aims to design and implement an intelligent data analysis platform based on artificial intelligence technology, expanding people's application of artificial intelligence through the integration of AI operation and data analysis. This platform has achieved cost reduction and efficiency improvement in data analysis. By importing the original dataset and inputting analysis requirements, AI can be called to generate visual charts and analysis conclusions. This research is expected to promote the development of artificial intelligence technology in the business field and provide new ideas and methods for software design.

Artificial intelligence (AI) is rapidly altering the field of hematology, providing novel approaches to diagnosis, prognosis, and management of hematological illnesses. AI technologies, including machine learning (ML) and deep learning (DL), allow for the analysis of massive volumes of clinical, genetic, and imaging data, resulting in more accurate, rapid, and individualized care. In diagnostic hematology, AI is transforming blood smear analysis, bone marrow aspirations, and genomic profiling by automating cell classification, detecting anomalies, and discovering critical genetic changes associated with blood illnesses. AI-powered models are also improving prognostic skills by predicting disease progression, treatment response, and risk of relapse in illnesses such as leukemia, lymphoma, anemia, and myeloproliferative disorders. Furthermore, AI applications in precision medicine enable clinicians to adapt medicines based on individual genetic profiles, thereby increasing therapeutic success and reducing unwanted effects. The combination of AI and modern technology such as wearable health monitors and real-time diagnostic tools promises to improve patient management by providing proactive care via continuous monitoring and adaptive treatment options. As AI develops, it has enormous potential in hematology, enabling early identification, optimizing treatment regimens, and ultimately improving patient survival and quality of life. This study investigates the future implications of AI applications in hematology, emphasizing their revolutionary impact on diagnosis, prognosis, and individualized treatment techniques.