Development, Application and Prospect of Artificial Intelligence in Biomedical Field

This review comprehensively explores the core application of artificial intelligence (AI) in the fields of genomics and bioinformatics, and deeply analyzes how it leads the innovative progress of science. In the cutting-edge fields of genomics and bioinformatics, the application of AI is propelling a deeper understanding of complex genetic mechanisms and the development of innovative therapeutic approaches. The precision of AI in genomic sequence analysis, coupled with breakthroughs in precise gene editing, such as AI-designed gene editors, significantly enhances our comprehension of gene functions and disease associations . Moreover, AI’s capabilities in disease prediction, assessing individual disease risks through genomic data analysis, provide robust support for personalized medicine. AI applications extend beyond gene identification, gene expression pattern prediction, and genomic structural variant analysis, encompassing key areas such as epigenetics, multi-omics data integration, genetic disease diagnosis, evolutionary genomics, and non-coding RNA function prediction. Despite challenges including data privacy, algorithm transparency, and bioethical issues, the future of AI is expected to continue revolutionizing genomics and bioinformatics, ushering in a new era of personalized medicine and precision treatments.

Introduction: In todays rapidly changing technology, artificial intelligence (AI) is changing the way we live, work and create art at an unprecedented speed. As an important part of human culture, dance art has also ushered in new development opportunities and challenges in this scientific and technological revolution. Objectives: This paper aims to explore the innovative application and influence of artificial intelligence (AI) in dance creation and performance. With the rapid development of AI technology, its application is increasingly wide in the field of art, and dance art is no exception Methods: This paper first outlines the application of AI technology in dance creation, including the generation of new dance movements through algorithms, auxiliary choreography, etc. These applications provide unprecedented creative sources and tools for dance creators. Then, this paper analyzes the role of AI in dance performance, such as the accurate movement performance of dance robot, the immersive experience of virtual reality technology in dance performance, etc. Results: These technologies not only enrich the form of dance performance, but also broaden the viewing experience of the audience. In addition, this paper also discusses the application of AI in dance teaching, such as intelligent guidance system, distance dance teaching, etc., which bring convenience and personalization to dance teaching. However, the application of AI in the field of dance also faces technical challenges and art ethical issues, which need further research and discussion. Conclusions: In conclusion, this paper believes that the application of AI technology in dance creation, performance and teaching has broad prospects, but it also needs to pay attention to its potential impact and challenges to promote the harmonious coexistence of dance art and AI technology. The research of this paper provides new perspectives and thoughts in the field of dance art, Provides a reference for future research and practice.

This article provides a brief overview of the application and development of artificial intelligence (AI) in the field of education under the background of New Liberal Arts. It also highlights the importance and current status of innovative approaches in college English teaching. Furthermore, it presents the research objectives and methods. The application of artificial intelligence in education has witnessed significant growth and advancement in recent years. AI technologies, such as machine learning and natural language processing, have been utilized to enhance various aspects of education, including personalized learning, intelligent tutoring systems, and automated grading. These applications have shown promising results in improving students' learning outcomes and engagement. In the context of college English teaching, innovation is crucial to meet the evolving needs and expectations of students. Traditional teaching methods often fail to cater to individual learning styles and preferences, leading to limited effectiveness and student disengagement. Therefore, there is a pressing need to explore innovative approaches that leverage AI technologies to create more interactive and personalized learning experiences. The research aims to investigate the potential of integrating AI into college English teaching and its impact on students' learning outcomes and engagement. The study will employ a mixed-methods approach, combining quantitative analysis of students' performance data with qualitative interviews and surveys to gather comprehensive insights. By examining the effectiveness and challenges of AI integration in English teaching, this research seeks to provide valuable recommendations for educators and policymakers to enhance the quality of college English education. Above all , this study aims to explore the application of artificial intelligence in college English teaching, considering its potential to improve learning outcomes and engagement. By adopting a mixed-methods research approach, this study intends to provide valuable insights and recommendations for educators and policymakers to promote innovative and effective English teaching practices.

This study investigates the scientific development of Artificial Intelligence (AI) and Machine Learning (ML) applications in Human Resource Management (HRM) through bibliometric analysis. To this end, 522 academic publications indexed in the Scopus database between 2020 and 2024 were analyzed using the R-based bibliometrix package and VOSviewer software. Descriptive analysis, scientific productivity metrics, and content analysis techniques were employed. The findings revealed three main patterns. First, research on AI and ML applications in HRM has grown significantly—particularly between 2022 and 2024—driven by post-pandemic digital transformation. Second, India, China, and the USA lead in research output, while the UK and France demonstrate strong citation impact, indicating a globally expanding research ecosystem. Third, the thematic focus of research is shifting from technical infrastructure toward more human-centered and ethical dimensions. Additionally, keyword co-occurrence network analysis identified three major thematic clusters: HRM functions, AI applications, and machine learning analytics, highlighting the field’s interdisciplinary nature. Compared to the previous studies, this research provides a more comprehensive bibliometric analysis of AI and ML applications in HRM. It is the first extensive study to map the intellectual evolution of the field from a multidisciplinary perspective. Furthermore, it charts research trends and collaboration networks, revealing a shift from technical implementations to strategic integration. In conclusion, this analysis offers new insights to the literature by illustrating the technological evolution in HRM and highlighting the growing significance of cutting-edge approaches such as AI and ML, reaffirming the field as a timely and impactful area of research.

With the continuous development of science and technology, exploration in the field of life sciences has become increasingly profound, encompassing the understanding of human beings themselves and the exploration of disease etiology and treatment methods.Artificial intelligence is defined as the intelligence exhibited by artificially created systems. The application of artificial intelligence is quite extensive and can replace humans in various aspects of work, significantly enhancing efficiency in daily life and work. Artificial intelligence has a dual nature, as it could provide convenience to human society but could also bring about various adverse effects.This paper summarizes the current applications of artificial intelligence in the field of life sciences. By analyzing the applications of artificial intelligence in areas such as gene editing, drug development, and bioinformatics, it demonstrates the potential of artificial intelligence in the field of life sciences. At the same time, this paper also points out the ethical, technical, social, and various challenges that the application of artificial intelligence technology in the field of life sciences faces, with the aim of providing reference for research in related fields.

Chapter 15 - Application of artificial intelligence in pharmaceutical development

Technological progress, especially in the field of information, has led to reducing the gap between science, information and knowledge. This accelerating change has forced all educational institutions to comply with this new mission by using information technology in the teaching process, as artificial intelligence has become an integral part of the educational and learning process, and a means of Effective in the teaching process, in addition to being research tools in obtaining information and knowledge, the research problem focused on (what are the attitudes of the kindergarten teacher towards using artificial intelligence applications in the effective teaching process in the early education stage in the third Rusafa Education Directorate?), where the importance of The research is considered one of the researches that deals with an important and vital topic (artificial intelligence), as it gains its importance in that it represents the role that kindergarten teachers play in the early education stage in developing the skills and abilities and refining the information of the child through their use of artificial intelligence methods and applications, as well as The positive role of such research in stimulating kindergarten teachers’ motivation to benefit from artificial intelligence applications, which increases the efficiency and effectiveness of their teaching. The aim of the research is to reveal kindergarten teachers’ attitudes towards using artificial intelligence in the effective teaching process in the early education stage, as well as to reveal the most important The challenges facing kindergarten teachers using artificial intelligence in the effective teaching process, and are there any statistically significant differences between the average kindergarten teacher’s attitudes to using artificial intelligence in the effective teaching process in the early education stage according to the research variables? The research reached several conclusions, perhaps the most important of which is the existence of differences. There is statistical significance between the average kindergarten teacher’s attitudes to using artificial intelligence in the effective teaching process in the early education stage according to the research variables. The research recommends holding many workshops and training courses for kindergarten teachers on how to use artificial intelligence in effective teaching, and including programs and rehabilitation plans for kindergarten teachers. About the use of artificial intelligence according to the plans of the Ministry of Education, which are consistent with its vision, and encouraging teachers to proceed and benefit from applications and websites for safe and available artificial intelligence applications in the teaching process

This study evaluates the impact of Artificial Intelligence (AI) applications in power systems on energy security and to determine relevant policy implications. We use a mixed methods approach to analyze the benefits and risks associated with AI implementation on the European power grid, focusing on four key dimensions of energy security: availability, affordability, accessibility, and acceptability. We investigated the benefits of AI using PyPSA, a Python-based model of the European electricity system. Three AI applications were parametrized: load reduction, load shifting, and wind wake steering. We compared scenarios in which these AI applications are widely deployed against a baseline scenario without these applications to determine if AI improves energy security. The study also analyzes risks associated with AI deployment in the power grid. We developed a risk taxonomy centered around six high-level categories: cybersecurity, jurisdictional or sovereignty issues, unexplained or unexpected actions by the model, unethical or illegal decision-making, reliance and trust in decision-making, and supplier dependency and vendor lock-in. Additionally, we conducted a back-casting exercise with subject-matter experts to determine positive and negative future outcomes of AI deployment and identify actions to create positive outcomes and avoid negative ones. The paper presents a set of policy implications for AI on the European synchronous grid. We find that AI applications can improve energy security in power systems. In the scenarios we tested, behind-the-meter applications have a greater impact on energy security than front-of-meter applications. The results suggest that AI applications targeting energy consumption may significantly improve energy security metrics. Keywords: Energy Security, AI, Power Grid, European Electricity System

Artificial intelligence (AI) applications pave the way for innovations in the healthcare (HC) industry. However, their adoption in HC organizations is still nascent as organizations often face a fragmented and incomplete picture of how they can capture the value of AI applications on a managerial level. To overcome adoption hurdles, HC organizations would benefit from understanding how they can capture AI applications’ potential.We conduct a comprehensive systematic literature review and 11 semi-structured expert interviews to identify, systematize, and describe 15 business objectives that translate into six value propositions of AI applications in HC.Our results demonstrate that AI applications can have several business objectives converging into risk-reduced patient care, advanced patient care, self-management, process acceleration, resource optimization, and knowledge discovery.We contribute to the literature by extending research on value creation mechanisms of AI to the HC context and guiding HC organizations in evaluating their AI applications or those of the competition on a managerial level, to assess AI investment decisions, and to align their AI application portfolio towards an overarching strategy.

With the rising demand for personalized learning and increased digital natives, the traditional education model is under pressure to reform. the rapid development of artificial intelligence (AI) provides new education solutions, and the application of AI is becoming widespread, from intelligent tutoring to automated grading to learning analytics. However, the application of AI in education is not without challenges. This literature review aims to explore the impact of AI on education, analyze its potential advantages, and propose strategies to address the challenges and provide guidance for the future development of education. Understanding the impact of AI on education is instructive for the future direction of AI in education and reveals the importance of AI for education to the extent that it is important for formulating effective educational policies and cultivating future talents in AI technology. The final results of the study show that although AI in the education industry there are various difficulties and limitations, but AI offers great potential for smarter and more efficient education levels and education systems. In today's era, computer programming education is emerging as a pivotal force in shaping the future, as it not only hones students' logical thinking and innovative capabilities but also lays a solid foundation for their career paths in the digital world. By integrating knowledge across various disciplines, programming education stimulates students' enthusiasm for applying their learning to real-world problems and enhances their sense of social engagement, enabling them to tackle challenges through technological means.

BackgroundDespite the extensive hopes and expectations for value creation resulting from the implementation of artificial intelligence (AI) applications in healthcare, research has predominantly been technology-centric rather than focused on the many changes that are required in clinical practice for the technology to be successfully implemented. The importance of leaders in the successful implementation of innovations in healthcare is well recognised, yet their perspectives on the specific innovation characteristics of AI are still unknown. The aim of this study was therefore to explore the perceptions of leaders in healthcare concerning the innovation characteristics of AI intended to be implemented into their organisation.MethodsThe study had a deductive qualitative design, using constructs from the innovation domain in the Consolidated Framework for Implementation Research (CFIR). Interviews were conducted with 26 leaders in healthcare.ResultsParticipants perceived that AI could provide relative advantages when it came to care management, supporting clinical decisions, and the early detection of disease and risk of disease. The development of AI in the organisation itself was perceived as the main current innovation source. The evidence base behind AI technology was questioned, in relation to its transparency, potential quality improvement, and safety risks. Although the participants acknowledged AI to be superior to human action in terms of effectiveness and precision in some situations, they also expressed uncertainty about the adaptability and trialability of AI. Complexities such as the characteristics of the technology, the lack of conceptual consensus about AI, and the need for a variety of implementation strategies to accomplish transformative change in practice were identified, as were uncertainties about the costs involved in AI implementation.ConclusionHealthcare leaders not only saw potential in the technology and its use in practice, but also felt that AI’s opacity limits its evidence strength and that complexities in relation to AI itself and its implementation influence its current use in healthcare practice. More research is needed based on actual experiences using AI applications in real-world situations and their impact on clinical practice. New theories, models, and frameworks may need to be developed to meet challenges related to the implementation of AI in healthcare.

Purpose This study examines the extent to which artificial intelligence (AI) applications predict psychological flourishing and sustainable development among Fayoum University students. It further explores gender differences in AI usage, flourishing levels, and sustainability orientations, as well as the interrelationships among the three variables. Methods A quantitative correlational design was employed using validated scales measuring AI application use, psychological flourishing, and sustainable development. The sample consisted of 190 university students from Fayoum University (Egypt) there ages were between 20 and 25 years. The mean age was 22.54 years, with a standard deviation of 3.22 years. Data were analyzed using t-tests, Pearson’s correlation coefficients, and simple linear regression to test the study hypotheses. Results Findings revealed no significant gender differences in AI application use, while significant differences were found in psychological flourishing and sustainable development in favor of male students. Significant positive correlations emerged among AI use, flourishing, and sustainability. Regression analyses showed that AI application use moderately predicted psychological flourishing (R 2 = 0.098) but strongly predicted sustainable development (R 2 = 0.604), indicating that AI plays a substantial role in shaping students’ sustainability-related behaviors. Conclusions The study demonstrates that AI applications contribute meaningfully to enhancing students’ well-being and sustainability practices, with a stronger influence on sustainability outcomes. These findings underscore the growing relevance of AI-driven learning environments in promoting sustainable education and student development. Implications The results highlight the need for integrating AI-based tools into higher education policies, fostering responsible AI use, and developing institutional initiatives that enhance students’ well-being and sustainability competencies.

A 3D indicator for guiding AI applications in the energy sector

This chapter examines the advances and perspectives of the applications of artificial intelligence (AI) in the classification of magnetic resonance (MR) images. It focuses on the development of AI-based automatic classification models that have achieved competitive results compared to the state-of-the-art. Accurate and efficient classification of MR images is essential for medical diagnosis but can be challenging due to the complexity and variability of the data. AI offers tools and techniques that can effectively address these challenges. The chapter first addresses the fundamentals of artificial intelligence applied to the classification of medical images, including machine learning techniques and convolutional neural networks. Here, recent advances in the use of AI to classify MRI images in various clinical applications, such as brain tumor detection, are explored. Additionally, advantages and challenges associated with implementing AI models in clinical settings are discussed, such as the interpretability of results and integration with existing radiology systems. Prospects for AI in MR image classification are also highlighted, including the combination of multiple imaging modalities and the use of more advanced AI approaches such as reinforcement learning and model generation.

This research examines what impact the Big Data Processing Framework (BDPF) has on Artificial Intelligence (AI) applications within Corporate Marketing Communication (CMC), and thereby the research question stated is: What is the potential impact of the BDPF on AI applications within the CMC tactical and managerial functions? To fulfill the purpose of this research, a qualitative research strategy was applied, including semi-structured interviews with experts within the different fields of examination: management, AI technology and CMC. The findings were analyzed through performing a thematic analysis, where coding was conducted in two steps. AI has many useful applications within CMC, which currently mainly are of the basic form of AI, so-called rule-based systems. However, the more complicated communication systems are used in some areas. Based on these findings, the impact of the BDPF on AI applications is assessed by examining different characteristics of the processing frameworks. The BDPF initially imposes both an administrative and compliance burden on organizations within this industry, and is particularly severe when machine learning is used. These burdens foremost stem from the general restriction of processing personal data and the data erasure requirement. However, in the long term, these burdens instead contribute to a positive impact on machine learning. The timeframe until enforcement contributes to a somewhat negative impact in the short term, which is also true for the uncertainty around interpretations of the BDPF requirements. Yet, the BDPF provides flexibility in how to become compliant, which is favorable for AI applications. Finally, BDPF compliance can increase company value, and thereby incentivize investments into AI models of higher transparency. The impact of the BDPF is quite insignificant for the basic forms of AI applications, which are currently most common within CMC. However, for the more complicated applications that are used, the BDPF is found to have a more severe negative impact in the short term, while it instead has a positive impact in the long term.