Prospects Of Artificial Intelligence Research Articles

Application and Prospect of Artificial Intelligence in Personalized Learning

With the rapid development of "big data, intelligence, cloud, things and mobile" technology, artificial intelligence has been widely used in the field of education, providing technical support for personalized learning. The traditional education model cannot meet the personalized needs of students. Education reform calls for the introduction of artificial intelligence technology to achieve the optimal allocation of educational resources and improve teaching effectiveness. The knowledge economy era has put forward higher requirements for talents' innovation ability and lifelong learning ability. Personalized learning is an important way to cultivate these abilities. This paper first takes the main application of artificial intelligence in personalized learning as the starting point, and then analyzes the advantages and challenges of artificial intelligence in personalized learning. It is concluded that artificial intelligence plays a positive role in innovating teaching methods, achieving educational equality, and giving new impetus to learning, but at the same time it also faces challenges in various aspects.

Can Artificial Intelligence Replace Humans in Industry?

The article considers the possibility of replacing humans with artifi cial intelligence (AI) in industry. An overview of the current capabilities and achievements of AI in industry is presented. Examples of the successful use of AI and its advantages are given. The material describes positions that cannot be replaced by AI. It is emphasized that creative professions, managerial and strategic positions require creative thinking, emotional intelligence and the ability to make complex decisions, which is not yet available to AI. Although AI has signifi cant potential to improve production processes, a complete replacement of humans remains unlikely in the near future due to existing limitations and challenges. The article presents an analysis of the current capabilities and prospects of AI in industry, as well as recommendations for the state and business on the effective implementation of these technologies.

AI-Driven Drug Discovery: Accelerating the Development of Novel Therapeutics in Biopharmaceuticals

Artificial Intelligence (AI) has emerged as a transformative force in drug discovery, revolutionizing the biopharmaceutical industry's approach to developing novel therapeutics. This paper provides a comprehensive overview of AI-driven drug discovery, focusing on its applications in accelerating the development of innovative treatments. We examine the fundamental AI technologies employed in drug discovery, including machine learning algorithms, deep learning architectures, and natural language processing techniques. The paper analyzes the integration of AI across various stages of the drug discovery pipeline, from target identification to clinical trial design, highlighting significant improvements in efficiency and accuracy. We explore the impact of big data on AI-driven drug discovery, discussing the challenges and opportunities presented by multi-omics data integration, electronic health records mining, and the need for data standardization. The study also addresses ethical considerations and regulatory challenges associated with AI implementation in drug development. Finally, we present emerging trends and prospects for AI in biopharmaceuticals, emphasizing the importance of collaborative ecosystems and the potential for AI to revolutionize personalized medicine. This review synthesizes current research and industry practices, providing insights into the transformative potential of AI in drug discovery and the challenges that lie ahead in realizing its full potential.

Prospects of Artificial Intelligence in the Annihilation of Caste

The masquerading effect of caste has engulfed the social structure in such a way that the myth associated with it has perked up in the ideas of existence, making it a reality regulating day-to-day affairs of interaction. Most of the time, it is considered a functional prerequisite. The stratification associated with it has conformity emanating from the acceptance of the identity, which is not evading even after migrating to foreign countries to participate and be part of the capitalist mode of production. Artificial Intelligence (AI) is a creation of the human mind. Caste is also a creation of the human mind. Technology has always paved the way for the betterment of society if used properly. Caste practices have always divided society and contaminated the public sphere with divisive manifestations of their stronghold in influencing social processes and policies. Annihilation of caste was the published work of Dr B. R. Ambedkar in the year 1936, and still the content and the context are seen in society. In the backdrop of this, the article engages with the role (if any) of AI in mitigating the divisions in society. The readers are already aware of the repercussions of the caste system since time immemorial. The diffusion of technology has always been trying to be inclusive, depending on the socio-economic aspect of any society. Industrialization along with the Information and Communication Technology (ICT) revolution has rendered several avenues and opportunities for the so-called lower caste people to come forward and amalgamate themselves with the so-called upper caste people in the socio- economic process. The article discusses the prospect of the annihilation of caste by the influence of AI. It also engages in discussing caste as a main component of individual and community identity, which has become immune to any form of change in society. The article is based on available secondary sources.

The Latest Developments and Applications in Radiation Therapy Equipment and Technology

This paper reviews the latest technological developments in contemporary radiation therapy equipment and their significance in clinical applications. It begins with an introduction to the basic principles and historical evolution of radiation therapy, followed by a detailed analysis of common types of radiation therapy equipment, including linear accelerators, Gamma Knives, proton therapy equipment, and heavy ion therapy equipment. For each type, the paper conducts an in-depth comparative analysis of their technical characteristics, covering aspects such as performance, accuracy, therapeutic efficacy, safety, side effects, cost, and maintenance. The paper then explores the latest trends in radiation therapy technology development, such as image-guided radiation therapy (IGRT), intensity-modulated radiation therapy (IMRT), stereotactic radiosurgery (SRS), and adaptive radiation therapy (ART), highlighting their clinical applications and potential through case studies. Finally, the paper looks forward to future development directions in radiation therapy technology, including the research and development of new equipment and technologies, trends in personalized treatment, and the prospects for artificial intelligence in radiation therapy. The aim of this paper is to provide comprehensive references and guidance for research and clinical practice in radiation therapy equipment.

The evolution of artificial intelligence on nursing education in China

The incorporation of artificial intelligence (AI) in nursing education is a major innovation that has the capacity of changing the practices in the classroom as well as improving the learning outcomes. AI technologies such as machine learning, neural networks, natural language processing, and computer vision enable AI systems to perform intelligent functions like knowledge acquisition, problem-solving, and decision-making. These technologies are most useful in healthcare as they organize and provide insights from large clinical and patient records in decision making processes in order to improve health care. Nevertheless, the following issues are some of the challenges that need to be considered for proper implementation of AI in the Nursing Education especially in China. These are issues such as technological requirements, information security, and other petty issues including the issue of ethics as well as issues of the resistance that is likely to be encountered from the practitioners in the field, students included. However, the prospects of AI, XR, and VR technologies in improving the delivery of nursing education and equipping students with adequate preparation to work in today’s advanced health systems cannot be underestimated.

Enhancing liver surgery and transplantation: the role of 3D printing and virtual reality

This review explores the significant advancements in liver surgery and transplantation, particularly focusing on the integration of 3D printing and virtual reality (VR) technologies. The core objective is to enhance preoperative planning, simulation, and intraoperative navigation. The review discusses several studies that underscore the accuracy and utility of 3D printed models derived from medical imaging, which are instrumental in identifying small liver lesions, improving surgical education, and facilitating patient comprehension. Additionally, the role of VR in surgical simulation is examined, highlighting its superiority in tumor identification and its potential in training systems. While clinical outcomes data suggest a need for further randomized trials to establish the impact on surgical efficiency and recovery, the review also touches upon the promising future of augmented reality (AR) for intraoperative guidance and liver segment identification, with prospects of artificial intelligence (AI) integration. The conclusion underscores the importance of continued clinical evidence and technological advancements for wider adoption in liver surgery and transplantation.

Research and progress on the mechanism of lower urinary tract neuromodulation: a literature review.

The storage and periodic voiding of urine in the lower urinary tract are regulated by a complex neural control system that includes the brain, spinal cord, and peripheral autonomic ganglia. Investigating the neuromodulation mechanisms of the lower urinary tract helps to deepen our understanding of urine storage and voiding processes, reveal the mechanisms underlying lower urinary tract dysfunction, and provide new strategies and insights for the treatment and management of related diseases. However, the current understanding of the neuromodulation mechanisms of the lower urinary tract is still limited, and further research methods are needed to elucidate its mechanisms and potential pathological mechanisms. This article provides an overview of the research progress in the functional study of the lower urinary tract system, as well as the key neural regulatory mechanisms during the micturition process. In addition, the commonly used research methods for studying the regulatory mechanisms of the lower urinary tract and the methods for evaluating lower urinary tract function in rodents are discussed. Finally, the latest advances and prospects of artificial intelligence in the research of neuromodulation mechanisms of the lower urinary tract are discussed. This includes the potential roles of machine learning in the diagnosis of lower urinary tract diseases and intelligent-assisted surgical systems, as well as the application of data mining and pattern recognition techniques in advancing lower urinary tract research. Our aim is to provide researchers with novel strategies and insights for the treatment and management of lower urinary tract dysfunction by conducting in-depth research and gaining a comprehensive understanding of the latest advancements in the neural regulation mechanisms of the lower urinary tract.

Artificial intelligence-enabled antibiotic prescribing and clinical support in Nigerian health-care settings: Budgetary constraints, challenges, and prospect

Today, resistance developed by bacteria to common antibiotics that were otherwise regarded as effective is posing a serious challenge. It is believed that without any different efforts, this perennial problem will undermine all the ongoing efforts in antibiotic discovery and therapy development. In Nigeria, antibiotics are frequently prescribed in hospitals. However, issues like multidrug resistance (MDR) and inappropriate use and misuse of antibiotics, including incorrect dosages and use of broad-spectrum antibiotics for targeted infections, have precipitated the rise of MDR bacteria. Consequently, this leads to higher healthcare costs, mainly due to prolonged hospital stays and additional medications as well as increased patient mortality. The prospects of artificial intelligence (AI)-enabled antibiotic prescribing hold significant promise in transforming the current health-care practices. AI has the potential to enhance the precision and efficiency of antibiotic treatment through advanced algorithms and data analytics. This technology can contribute to improved diagnostic accuracy, providing real-time clinical support, optimizing dosage recommendations, personalized treatment plans, and streamlined antimicrobial stewardship, ultimately aiding the global fight against antibiotic resistance and optimizing patient outcomes. The integration of AI in antibiotic prescribing reflects a cutting-edge approach with the potential to revolutionize how antibiotics are prescribed to address challenges in antimicrobial stewardship, clinical decision-making, and combating antibiotic resistance. One of the key impediments to integrating AI into Nigeria’s health-care system is budgetary constraints. Addressing these constraints through strategic investments, improved budgetary allocation to research and development, and leveraging the opportunities presented by AI technologies can significantly enhance antibiotic prescribing and health-care practices, leading to improved public health outcomes.

Artificial Intelligence for Pediatric Emergency Medicine

Pediatric Emergency Medicine (PEM) addresses the unique needs of children in emergencies. This subspecialty faces significant challenges, including the need for specialized training, patient crowding, and the demand for timely and accurate management. Artificial Intelligence (AI) presents promising solutions by enhancing diagnostic precision and operational efficiency. This review examines current trends and prospects of AI in PEM, focusing on its applications, benefits, challenges, and transformative potential. The review highlights AI’s role in overcoming PEM challenges and its future opportunities. Key AI applications in PEM include early sepsis detection, improving triage accuracy, predicting injuries, and supporting diagnostics. AI models show significant potential in forecasting clinical outcomes, optimizing resource management, and improving patient care. Despite these benefits, challenges remain, including the need for specialized training for physicians and the integration of AI systems into clinical practice. Yet, AI holds considerable promise for advancing PEM through enhanced diagnostic tools, more efficient patient management, and improved clinical decision support. Continued advancements and collaborations between AI researchers and pediatric emergency practitioners are essential to fully realize AI’s potential in this field.

Harnessing AI for Sustainable Shipping and Green Ports: Challenges and Opportunities

The maritime industry, responsible for moving approximately 90% of the world’s goods, significantly contributes to environmental pollution, accounting for around 2.5% of global greenhouse gas emissions. This review explores the integration of artificial intelligence (AI) in promoting sustainability within the maritime sector, focusing on shipping and port operations. By addressing emissions, optimizing energy use, and enhancing operational efficiency, AI offers transformative potential for reducing the industry’s environmental impact. This review highlights the application of AI in fuel optimization, predictive maintenance, route planning, and smart energy management, alongside its role in autonomous shipping and logistics management. Case studies from Maersk Line and the Port of Rotterdam illustrate successful AI implementations, demonstrating significant improvements in fuel efficiency, emission reduction, and environmental monitoring. Despite challenges such as high implementation costs, data privacy concerns, and regulatory complexities, the prospects for AI in the maritime industry are promising. Continued advancements in AI technologies, supported by collaborative efforts and public–private partnerships, can drive substantial progress towards a more sustainable and efficient maritime industry.

ВЕЛИКІ МОВНІ МОДЕЛІ ШТУЧНОГО ІНТЕЛЕКТУ В МЕДИЦИНІ

This study conducts a thorough analysis of the current state and prospects of artificial intelligence (AI) technologies, particularly focusing on large language models (LLMs) and their implementation in healthcare. In an age of rapid digital technology advancement, this research is crucial for understanding the potential influences of AI on medical practices and research. A diverse range of methods, including analysis and synthesis, comparison, generalization, induction and deduction, systematization, as well as the phenomenological method, were employed for a comprehensive analysis. These techniques enabled not only an in-depth examination of the technical aspects of AI application but also facilitated an evaluation of its prospective impact on the field of medicine. The paper highlights the essential role of integrating large language models into the medical field. These models are not only capable of substantially enhancing the efficiency of processing vast medical data but also play a fundamental role in refining diagnostic procedures and reforming clinical decision-making processes. Furthermore, the article examines potential challenges associated with the use of AI in healthcare, particularly focusing on concerns regarding transparency, privacy, bias, and accountability These issues demand meticulous attention and effective solutions to ensure the successful integration of AI into medical practices. The research includes a complex, interdisciplinary approach surrounding the field of medicine, informatics, ethics, and law, underscoring the synergy among these diverse knowledge domains for the effective understanding and utilization of AI in healthcare. Moreover, the article underscores the present status and the prospective evolution of large language models within the medical sphere, emphasizing their significance and the imperative for ongoing research in this area. In summary, the authors support a holistic strategy for integrating AI into the medical sector. This strategy involves crafting models that prioritize personal data protection, generating high-quality and representative datasets for training, establishing ethical guidelines, and formulating relevant standards and legal frameworks. Additionally, the paper stresses the necessity of addressing technical challenges and innovating new methodologies for assessing AI's efficacy. The significance of this research is underscored by its potential benefits and hurdles associated with AI's incorporation into healthcare, highlighting the critical need for the medical community to be prepared for these evolving dynamics.

MODERN STATE OF APPLICATION OF AI TECHNOLOGIES IN CHEMICAL EDUCATION: PROBLEMS AND APPROACHES

The integration of artificial intelligence (AI) technologies into education systems has been a transformative force across multiple disciplines, with chemistry education proving particularly fertile ground for innovation. As we consider the prospects of AI in chemistry education, it is important to recognize the unique opportunities and challenges that this integration brings. AI technologies, ranging from machine learning algorithms to advanced simulation tools, offer unprecedented opportunities to improve the way chemistry is taught and learned. By introducing interactive content, personalized learning experiences, and improved visualization techniques, these technologies not only enable deeper understanding of complex chemical concepts but also transform traditional teaching methods. This article examines the current state of AI applications in chemistry education and highlights the significant benefits and potential future developments that could further transform this field. As AI continues to advance, its role in educational contexts, particularly in chemistry, promises to be both transformative and far-reaching, paving the way for more effective educational technologies. Keywords: chemistry, artificial intelligence, chemistry education, modern education trends, methods of teaching chemistry.

Possibilities and prospects of artificial intelligence in the treatment of colorectal cancer (review)

AIM: to study modern approaches to the application of machine learning and deep learning technologies for the management of patients with colorectal cancer.MATERIALS AND METHODS: after screening 398 publications, 112 articles were selected and the full text of the works was studied. After studying the full texts of the articles, the works were selected, machine learning models in which showed an accuracy of more than 80%. The results of 41 original publications were used to write this review.RESULTS: several areas have been identified that are the most promising for the use of artificial intelligence technologies in the management of patients with colorectal cancer. They are predicting the response to neoadjuvant treatment, predicting the risks of metastasis and recurrence of the disease, predicting the toxicity of chemotherapy, assessing the risks of leakage of colorectal anastomoses. As the most promising factors that can be used to train models, researchers consider clinical parameters, the immune environment of the tumor, tumor RNA signatures, as well as visual pathomorphological characteristics. The models for predicting the risk of liver metastases in patients with stage T1 (AUC = 0.9631), as well as models aimed at assessing the risk of 30-day mortality during chemotherapy (AUC = 0.924), were characterized with the greatest accuracy. Most of the technologies discussed in this paper are software products trained on data sets of different quality and quantity, which are able to suggest a treatment scenario based on predictive models, and, in fact, can be used as a doctor’s assistant with very limited functionality.CONCLUSION: the current level of digital technologies in oncology and in the treatment of colorectal cancer does not allow us to talk about a strong AI capable of making decisions about the treatment of patients without medical supervision. Personalized treatment based on the microbiotic and mutation spectrum and, for example, personal pharmacokinetics, so far look fantastic, but certainly promising for future developments.

Application and prospect of artificial intelligence in the precision diagnosis and treatment of chronic rhinosinusitis

Application and prospect of artificial intelligence in the precision diagnosis and treatment of chronic rhinosinusitis

Determinants of Chromatin Organization in Aging and Cancer-Emerging Opportunities for Epigenetic Therapies and AI Technology.

This review article critically examines the pivotal role of chromatin organization in gene regulation, cellular differentiation, disease progression and aging. It explores the dynamic between the euchromatin and heterochromatin, coded by a complex array of histone modifications that orchestrate essential cellular processes. We discuss the pathological impacts of chromatin state misregulation, particularly in cancer and accelerated aging conditions such as progeroid syndromes, and highlight the innovative role of epigenetic therapies and artificial intelligence (AI) in comprehending and harnessing the histone code toward personalized medicine. In the context of aging, this review explores the use of AI and advanced machine learning (ML) algorithms to parse vast biological datasets, leading to the development of predictive models for epigenetic modifications and providing a framework for understanding complex regulatory mechanisms, such as those governing cell identity genes. It supports innovative platforms like CEFCIG for high-accuracy predictions and tools like GridGO for tailored ChIP-Seq analysis, which are vital for deciphering the epigenetic landscape. The review also casts a vision on the prospects of AI and ML in oncology, particularly in the personalization of cancer therapy, including early diagnostics and treatment optimization for diseases like head and neck and colorectal cancers by harnessing computational methods, AI advancements and integrated clinical data for a transformative impact on healthcare outcomes.

“What Scares Me Is the Speed at Which Artificial Intelligence Is Developing”: Students' Perceptions of Artificial Intelligence in Foreign Language Teaching

As artificial intelligence (AI) becomes an integral part of our daily lives, the concern of the teaching community about the illegal use of these technologies in the educational process is increasing. In order to adapt the education system and teaching practices to new technological challenges, it is necessary to analyze the opinions of all the parties concerned. The purpose of this study is to identify the attitude of students of Kazan Federal University to the use of artificial intelligence technologies in the educational process and the practice of their application in foreign languages learning process. To achieve this goal, an online survey of students of Kazan Federal University was conducted. The survey touched upon the practical aspects of the use of artificial intelligence in language teaching, the advantages and disadvantages of AI tools from the students’ point of view, as well as their opinion regarding the prospects of AI in education. As a result of the study, we came to the conclusion that at the moment AI tools are not widespread enough in teaching foreign languages. Only one-fifth of the respondents use these tools, but the respondents’ comments suggest that the number of users will grow. The attitude of students towards the use of AI is ambiguous, with responses ranging from enthusiastic to skeptical. Students’ positive impressions are mainly related to saving time and effort, as well as to the ability of AI to present complex materials with understandable language. Among the main disadvantages, the respondents noted unreliability of data and fake content. Despite the fact that students are generally positive about the use of AI, a significant part of respondents do not trust software products such as ChatGPT, since, in their opinion, it provides answers of average quality that need to be corrected. Based on the data obtained, the authors attempted to formulate recommendations on improving the methods of teaching and control in the process of teaching foreign languages at universities.

A Review of Artificial Intelligence in Breast Imaging.

With the increasing dominance of artificial intelligence (AI) techniques, the important prospects for their application have extended to various medical fields, including domains such as in vitro diagnosis, intelligent rehabilitation, medical imaging, and prognosis. Breast cancer is a common malignancy that critically affects women's physical and mental health. Early breast cancer screening-through mammography, ultrasound, or magnetic resonance imaging (MRI)-can substantially improve the prognosis for breast cancer patients. AI applications have shown excellent performance in various image recognition tasks, and their use in breast cancer screening has been explored in numerous studies. This paper introduces relevant AI techniques and their applications in the field of medical imaging of the breast (mammography and ultrasound), specifically in terms of identifying, segmenting, and classifying lesions; assessing breast cancer risk; and improving image quality. Focusing on medical imaging for breast cancer, this paper also reviews related challenges and prospects for AI.

Challenges and Prospects of Artificial Intelligence in Nigerian Journalism Practice: A Narrative Review

The integration of artificial intelligence (AI) into Nigerian journalism practices presents both challenges and prospects for the industry. This paper explores the dynamic interplay between AI technologies and journalism in Nigeria, addressing key conceptual clarifications, theoretical frameworks, challenges, and prospects associated with AI utilization in the field. Drawing upon existing literature, the study examines the multifaceted nature of AI, ranging from its conceptual underpinnings to its practical applications in journalism. It utilizes the Technology-Organization-Environment (TOE) framework to analyze the factors influencing AI adoption within Nigerian newsrooms, considering technological, organizational, and environmental contexts. Identifying challenges such as infrastructural limitations, financial constraints, cultural resistance, and ethical considerations, the paper elucidates the complexities inherent in AI integration in Nigerian journalism. These challenges encompass issues of data quality, transparency, bias, and accountability, underscoring the need for responsible AI implementation and robust ethical frameworks. Additionally, the paper discusses the prospects offered by AI in enhancing journalistic efficiency, facilitating data-driven reporting, personalizing news delivery, and combating misinformation. By leveraging AI for fact-checking, multilingual reporting, predictive analytics, and automated content generation, Nigerian journalists can innovate and adapt to the evolving media landscape. Moreover, AI presents opportunities for improving audience engagement, storytelling, collaboration, and addressing bias and ethics concerns. Embracing AI in journalism also creates educational pathways for aspiring journalists, fostering skill development and innovation in the industry.

Exploring the Role of Artificial Intelligence in Mental Healthcare: Current Trends and Future Directions - A Narrative Review for a Comprehensive Insight.

Mental health is an essential component of the health and well-being of a person and community, and it is critical for the individual, society, and socio-economic development of any country. Mental healthcare is currently in the health sector transformation era, with emerging technologies such as artificial intelligence (AI) reshaping the screening, diagnosis, and treatment modalities of psychiatric illnesses. The present narrative review is aimed at discussing the current landscape and the role of AI in mental healthcare, including screening, diagnosis, and treatment. Furthermore, this review attempted to highlight the key challenges, limitations, and prospects of AI in providing mental healthcare based on existing works of literature. The literature search for this narrative review was obtained from PubMed, Saudi Digital Library (SDL), Google Scholar, Web of Science, and IEEE Xplore, and we included only English-language articles published in the last five years. Keywords used in combination with Boolean operators ("AND" and "OR") were the following: "Artificial intelligence", "Machine learning", Deep learning", "Early diagnosis", "Treatment", "interventions", "ethical consideration", and "mental Healthcare". Our literature review revealed that, equipped with predictive analytics capabilities, AI can improve treatment planning by predicting an individual's response to various interventions. Predictive analytics, which uses historical data to formulate preventative interventions, aligns with the move toward individualized and preventive mental healthcare. In the screening and diagnostic domains, a subset of AI, such as machine learning and deep learning, has been proven to analyze various mental health data sets and predict the patterns associated with various mental health problems. However, limited studies have evaluated the collaboration between healthcare professionals and AI in delivering mental healthcare, as these sensitive problems require empathy, human connections, and holistic, personalized, and multidisciplinary approaches. Ethical issues, cybersecurity, a lack of data analytics diversity, cultural sensitivity, and language barriers remain concerns for implementing this futuristic approach in mental healthcare. Considering these sensitive problems require empathy, human connections, and holistic, personalized, and multidisciplinary approaches, it is imperative to explore these aspects. Therefore, future comparative trials with larger sample sizes and data sets are warranted to evaluate different AI models used in mental healthcare across regions to fill the existing knowledge gaps.