The Role of Artificial Intelligence and Advanced Informatics in the Evolution of Modern Genetics Education

Artificial intelligence (AI) is increasingly applied in clinical diagnostics, particularly in radiology, where it can assist with imaging triaging and anomaly detection. However, the integration of AI into medical education remains under researched. This study investigates the impact of an AI-focused panel discussion on medical students' perceptions, knowledge, attitudes and concerns about AI in radiology. A paired pre-post design questionnaire comprising of 13 five-point Likert scale questions was administered to 40 medical students to complete before and after an AI-focused educational panel session at the International Radiology Undergraduate Symposium in London, United Kingdom on 24th November 2024. The questionnaire assessed four domains: 'Understanding of AI,' 'Attitudes Toward AI in Radiology,' 'AI Education in Medical School,' and 'Concerns About AI in the Future.' The primary outcome was to assess the change in students' perceptions of AI's role in radiology. Differences between pre- and post-session responses were analysed using the Wilcoxon signed-rank test. The Hodges-Lehmann median difference, the effect size, r, and their corresponding 95% confidence intervals were calculated, and p-values were adjusted using the Holm-Bonferroni method. Of the 81 eligible attendees, 40 (49.4%) completed the questionnaire (39 pre-session, 40 post-session). Students demonstrated significant improvements in their understanding of AI's potential role in radiology (Z = 3.04, p = 0.002; Holm-Bonferroni = 0.029; median paired difference = 0.5, 95% CI 0.0-0.5; r = 0.49, 95% CI 0.25-0.68) and in their awareness of AI's broader clinical applications (Z = 3.65, p < 0.001; Holm-Bonferroni = 0.0035; median paired difference = 0.5, 95% CI 0.5-1.0; r = 0.60, 95% CI 0.38-0.75). Participants expressed a more positive view of AI in healthcare overall, although concerns about AI replacing radiologists and insufficient AI education persisted. Educational interventions have the potential to improve medical students' understanding and attitudes toward AI in radiology. Integrating structured AI education into undergraduate curricula may enhance AI literacy and better prepare future clinicians for an AI-enabled healthcare environment.

This research paper focuses on the role of Artificial Intelligence in UI/UX design. We know that one of the most important aspect in software development is the design of the user interface ( UI ), which refers to the look and feel of the product, and user experience ( UX ), which refers to the interaction by the user.The integration of Artificial Intelligence (AI) in User Experience (UX) and User Interface (UI) design has revolutionized digital interactions by enhancing personalization, automation, predictive analytics, and accessibility. AI-driven tools enable designers to create more intuitive, adaptive, and usercentric interfaces, improving user engagement and satisfaction. This research paper explores the various applications of AI in UX/UI, including AI-powered personalization, which tailors experiences based on user behavior, automation in design, which accelerates prototyping and layout generation, and predictive analytics, which enhances decision-making through data-driven insights. Additionally, the role of conversational AI, such as chatbots and virtual assistants, in improving user interactions is examined, along with AI's contribution to inclusive and accessible UX/UI design.Despite its advantages, the implementation of AI in UX/UI presents challenges such as data privacy concerns, ethical considerations, and potential over-reliance on automation. This paper discusses these challenges and proposes solutions to ensure that AI enhances UX/UI without compromising creativity, inclusivity, or ethical standards. The study concludes that while AI is transforming UX/UI design, a balanced approach combining AI-driven efficiency with human creativity is essential for building truly user-friendly and ethical digital experiences.

Artificial Intelligence (AI) has an important role to play in shaping the future of software development. AI responds to complex challenges in the information technology industry and expands the scope of future possibilities, which include increased automation, personalization, and security. The research aims to identify the role of AI in education and research from various aspects of software development, and evaluate the resulting implications for information technology as a whole. The research adopted the Systematic Literature Review Method following PRISMA guidelines. A total of 320 articles were collected from Scopus, Web of Science and Google Scholar and applying predefined criteria, 42 relevant articles were included for analysis. The research findings show that the role and integration of artificial intelligence (AI) has a significant impact in improving efficiency, bringing software innovation in education, learning and research in the future. AI has proven effective in personalizing learning, adapting teaching materials and improving student learning outcomes. AI accelerates the process of analyzing big data, identifying patterns and trends that conventional methods may miss. The implications of the findings suggest that the integration of AI in education and research not only improves the efficiency and effectiveness of the process, but opens up new opportunities for innovation and development of more adaptive and data-driven learning and research methods. The challenges of AI in education and research include data privacy, potential bias in algorithms, and the need for adequate technological infrastructure to support effective and secure implementation, avoid inequality of access, and ensure accurate results.

Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing industries, reshaping the way we interact with technology, and driving innovation across multiple disciplines. Advancements in Artificial Intelligence and Machine Learning is a comprehensive exploration of the latest developments, applications, and challenges in AI and ML, offering insights into cutting-edge research and real-world implementations. This book is a collection of twelve chapters, each exploring a distinct application of Artificial Intelligence (AI) and Machine Learning (ML). It begins with an overview of AI's transformative role in Next-Gen Mechatronics, followed by a comprehensive review of key advancements and trends in the field. The book then examines AI's impact across diverse sectors, including energy, digital communication, and security, with topics such as AI-based aging analysis of power transformer oil, AI in social media management, and AI-driven human detection systems. Further chapters address sentiment analysis, visual analysis for image processing, and the integration of AI in smart grid networks. The volume also covers AI applications in hardware security for wireless sensor networks, drone robotics, and crime prevention systems. The final set of chapters highlight AI's role in healthcare and automation, including an AI-assisted system for women's safety in India and the use of EfficientNet B0 CNN architecture for brain tumor detection and classification. Together, these chapters showcase the versatility and growing influence of AI and ML across critical modern industries. Key features A multidisciplinary approach covering AI applications in robotics, cybersecurity, healthcare, and digital transformation in 12 organized chapters. A focus on contemporary challenges and solutions in AI and ML across industries. Research-driven insights from experts and practitioners in the field. Practical discussions on AI-driven automation, security, and intelligent decision-making systems.

The integration of artificial intelligence (AI) into healthcare, particularly in the field of gastroenterology, marks a significant advancement in the diagnosis and treatment of pancreatic disorders. This narrative review explores the application of AI in enhancing Endoscopic Ultrasound (EUS) imaging techniques for pancreatic pathologies, focusing on developments over the past decade. Through a comprehensive literature search across several scientific databases, including PubMed, Google Scholar, and Web of Science, this paper selects and analyzes 50 studies that highlight the role, benefits, precision rates, and limitations of AI in EUS. The findings suggest that AI not only improves the quality of endoscopic procedures, as acknowledged by a majority of gastroenterologists in the UK and USA, but also offers a promising future for medical diagnostics and treatment, potentially addressing the shortage of specialists and reducing morbidity and mortality rates. Despite AI’s infancy in clinical applications and the ethical concerns regarding data privacy, its integration into EUS has enhanced diagnostic accuracy and provided minimally invasive therapeutic alternatives. This review underscores the necessity for further clinical data to evaluate the applicability and reliability of AI in healthcare, advocating for a collaborative approach between physicians and AI technologies to revolutionize the traditional clinical diagnosis and expand treatment possibilities in gastroenterology.

Artificial Intelligence (AI) is revolutionizing knowledge creation, dissemination, and application across disciplines. As AI continues to evolve, its potential to reshape industries and human cognition demands continuous scrutiny and adaptation. AI's growing impact on society demands collaboration among technologists, policymakers, and ethicists to ensure responsible and fair development. The paper argues that AI, while powerful, should complement rather than replace human expertise, fostering a balance betwee technological innovation and ethical responsibility. Future research must address the long-term implications of AI, including its effect on employment, privacy, and human creativity. Furthermore, the implications of AI-driven automation on workforce transformation and economic shifts require deeper examination. The ethical questions surrounding algorithmic accountability and decision-making authority in AI applications remain crucial areas for exploration. This research aims to provide a comprehensive understanding of AI’s role in shaping the future of human knowledge and society at large. Additionally, AI’s applications in enhancing scientific knowledge and improving automation efficiency demand continuous regulatory updates and technological refinements to ensure sustainable development. This paper explores AI's transformative role in science, healthcare, education, business, and the arts, demonstrating its interdisciplinary impact. It discusses AI-driven innovations, methodological advancements, and the ethical implications of AI's integration into human knowledge systems. By examining current research and case studies, the paper highlights both the opportunities and challenges presented by AI's rapid evolution.

This study addresses a critical gap in understanding Artificial Intelligence (AI)'s role in education by empirically investigating and comparing the distinct perceptions of teachers and students regarding AI's role in a comprehensive range of social development aspects in both online and physical classroom settings. In particular, we evaluated how teachers utilize AI in their teaching methods, namely, Communicative Language Teaching (CLT), the Direct Method (DL), Task-Based Language Teaching (TBLT), Content and Language Integrated Learning (CLIL), and Community Language Learning (CLL), and students in their learning methods, namely, Communicative Learning (CL), Immersive Learning (IL), Task-Based Collaborative Learning (TBCL), Content Integrated Learning (CIL), and Community-Based Reflective Learning (CBRL), to configure their social development. We interviewed 20 teachers (10 from online and 10 from physical classes) and 40 students (20 from online and 20 from physical classes) and evaluated their perceptions regarding AI usage in teaching and learning methods towards social development. The results of our study are convincing enough to suggest that both teachers and students perceive AI usage helpful in teaching models; however, variation in their perception is observed. Notably, the divergence in the perception of teachers and students with regard to AI's role is a key observation of this study. For instance, the teachers perceived AI as a highly effective tool in fostering community building during online sessions; in contrast, the students viewed its role as being moderately effective. Likewise, the teachers perceived AI's role as a critical tool in traditional classrooms rather than in virtual ones, whereas the students associated AI with online learning-in terms of digital tools, learning opportunities, and critical discussion-by rating its impact on social confidence and verbal-nonverbal communications significantly more strongly in physical settings. On the contrary, the teachers emphasized AI's relevance to their self-confidence, emotional intelligence, and community engagement in online teaching platforms; yet, the ratings dropped to moderate in physical contexts. The students' perceptions in this regard matched those of the teachers, as they also emphasized the importance of social confidence and overall well-being in physical classrooms, where the teachers' assessment was comparatively low. These patterns provide analytical insights that are decisively valuable for designing AI-integrated pedagogical models that support social development within the educational environments.

This paper explores the relationship between Artificial Intelligence (AI) integration in the workplace, cultural orientation, and its impact on job autonomy and creative self-efficacy. Our study employs a mixed-method experimental design across 480 individuals from different cultural backgrounds, specifically individualistic (United Kingdom) and collectivistic (Mexico) cultures. We evaluate how they perceive AI's role in their professional lives. We focus on two key aspects: job autonomy, the level of control and discretion employees have over their tasks, and creative self-efficacy, the confidence in one's ability to generate innovative ideas. Our findings revealed a significant increase in job autonomy following AI integration across all participants. Interestingly, this increase was more pronounced in the individualistic participants. Regarding creative self-efficacy, we found gender-specific impacts, with male participants experiencing a decrease, contrary to our expectations. Finally, our results supported the hypothesis that cultural orientation influences perceptions of AI, with collectivistic participants being more receptive to AI integration. These findings have significant implications for organizations integrating AI in multicultural environments. They highlight the importance of considering cultural differences in AI deployment strategies and suggest a need for culturally sensitive AI systems. The study also opens avenues for future research, particularly in exploring the role of other cultural dimensions, conducting longitudinal studies, and investigating ethical and bias-related aspects of AI in the workplace.

The role of Artificial Intelligence (AI) and Generative Artificial Intelligence (Gen AI) in digital healthcare

PurposeThis study aims to investigate the significance of an emerging concept – green talent management (TM) and its influence on employees’ innovative work behavior, together with the moderating roles of transformational leadership and artificial intelligence within the context of higher educational institutions.Design/methodology/approachTwo hundred and thirty-five structured questionnaires were administered to the academic staff in five universities located in Northern Cyprus, and the data was analyzed using partial least square structural equation modeling with the aid of WarpPLS (7.0).FindingsThis study provides evidences that green hard and soft TM exerts significant influence on employees’ innovative work behavior. Similarly, transformational leadership and artificial intelligence were confirmed to have a significant impact on employees’ innovative work behavior. Moreover, the study found transformational leadership and artificial intelligence to significantly moderate the relationship between green hard TM and employees’ innovative work behavior.Research limitations/implicationsThe study provides theoretical and managerial implications of findings that will assist the leaders in higher educational institutions in harnessing the potential of green TM in driving their employees’ innovative work behavior toward the achievement of sustainable competitive advantage in the market where they operate.Originality/valueThe attention of researchers in the recent time has been on the way to address the challenge facing organizational leaders on how to develop and retain employee that will contribute to the sustainability of their organization toward the achievement of sustainable competitive advantage in the market they operate. Meanwhile, the studies exploring these concerns are limited. In view of this, this study investigates the significance of an emerging concept – green talent management and its influence on employees’ innovative work behavior, together with the moderating roles of transformational leadership and artificial intelligence within the context of higher educational institutions.

The integration of artificial intelligence (AI) into policing has advanced significantly since the 1950s, with applications ranging from crime prediction to facial recognition. AI's role in enhancing public safety, resource allocation, and monitoring police behavior is increasingly recognized. However, the Philippine National Police (PNP) faces both opportunities and challenges in adopting AI, necessitating thorough exploration. This study examines PNP personnel's awareness of AI, the extent of their formal AI training, perceived benefits and barriers to AI adoption, and recommendations for improving AI integration. Survey results show that while most PNP personnel are aware of AI's role in policing, there are significant gaps in understanding specific applications like data analysis and crime prediction. Formal AI training within the PNP is notably lacking and often perceived as inadequate. Despite these deficiencies, respondents acknowledge AI's potential administrative and operational benefits. However, barriers such as insufficient training, communication gaps, heavy workloads, and skepticism about AI effectiveness hinder broader adoption. Respondents strongly agree on the need for more formal training, improved internal communication, leadership support, and regular awareness campaigns to facilitate AI integration. Addressing these gaps through structured training programs, better communication strategies, and supportive leadership can enhance AI's effective use in police work. Keywords: Artificial Intelligence, policing, public safety, Angeles City, Philippines, facial recognition, predictive analytics, ethical considerations, bias mitigation, privacy concerns.

Artificial intelligence (AI) is increasingly integrated into nursing education and healthcare, emphasizing its significance, applications, benefits, and challenges. AI in nursing curricula focuses on equipping students with essential skills to deliver safe and effective patient care in a rapidly evolving healthcare environment. Applications include AI-driven clinical decision support systems, simulations, virtual patients, and exploration of AI ethics. These tools enhance critical thinking, decision-making, and data analysis in healthcare contexts.This review summarizes AI's role in clinical practice, covering disease diagnosis, treatment planning, patient engagement, and ethical considerations while highlighting the need for human expertise in AI adoption.: 1. To enhances understanding of AI’s significance in Nursing Education; 2. To explore the impact of applying AI in nursing Education. 3. To promote AI usage in Nursing Institutions.This review analyzed AI's integration into healthcare and nursing education using indexed literature from PubMed, Scopus, and EMBASE. Key issues include data privacy, algorithm transparency, and biases, requiring responsible AI implementation. Effective strategies include curriculum design, faculty training, hands-on practice, industry collaboration, and continuous learning. Research highlights AI's role in improving diagnosis, treatment planning, personalized medicine, mental health support, and patient education while enhancing accuracy, reducing costs, and minimizing errors. Scholars have explored virtual simulations, faculty and student perspectives, AI competencies, and ethical concerns. Academic journals, conferences, and credible online sources provide valuable insights into AI's impact on nursing education and student outcomes.: In conclusion, integrating AI into nursing education is a developing field with great potential to enhance learning and prepare nurses for AI-driven healthcare. Research covers topics like virtual simulations, AI competencies, ethical concerns, and stakeholder perspectives. Key resources include academic journals, conferences, and online databases.AI supports disease diagnosis, personalized treatment, and clinical decision-making, aiming to improve patient care rather than just automating tasks. However, challenges like data privacy, bias, and the need for human expertise must be addressed.By tackling these challenges and promoting responsible AI use, nurse educators can equip future nurses with the skills needed for the evolving healthcare landscape.

Artificial intelligence (AI) is transforming oncology and surgery by improving diagnostics, personalizing treatments, and enhancing surgical precision. Patients appreciate AI for its potential to provide accurate prognoses and tailored therapies. However, AI's implementation raises ethical concerns, data privacy issues, and the need for transparent communication between patients and health care providers. This study aims to understand patients' perspectives on AI integration in oncology and surgery to foster a balanced and patient-centered approach. The study utilized a comprehensive literature review and analysis of existing research on AI applications in oncology and surgery. The focus was on examining patient perceptions, ethical considerations, and the potential benefits and risks associated with AI integration. Data was collected from peer-reviewed journals, conference proceedings, and expert opinions to provide a broad understanding of the topic. The perspectives of patients was also emphasized to highlight the nuances of their acceptance and concerns regarding AI in their health care. Patients generally perceive AI in oncology and surgery as beneficial, appreciating its potential for more accurate diagnoses, personalized treatment plans, and improved surgical outcomes. They particularly value AI's role in providing timely and precise diagnostics, which can lead to better prognoses and reduced anxiety. However, concerns about data privacy, ethical implications, and the reliability of AI systems were prevalent. Consequently, trust in AI and health care providers was deemed as a crucial factor for patient acceptance. Additionally, the need for transparent communication and ethical safeguards was also highlighted to address these concerns effectively. The integration of AI in oncology and surgeryholds significant promise for enhancing patient care and outcomes. Patients view AI as a valuable tool that can provide accurate prognoses and personalized treatments. However, addressing ethical concerns, ensuring data privacy, and building trust through transparent communication are essential for successful AI integration. Future initiatives should focus on refining AI algorithms, establishing robust ethical guidelines, and enhancing patient education to harmonize technological advancements with patient-centered care principles.

This study examines AI's role in enhancing guest satisfaction and efficiency in the hotel industry. Employing a mixedmethods approach, it analyzes guest feedback and interviews staff at AI-integrated hotels. The findings aim to identify key AI applications that boost satisfaction and efficiency, and outline challenges and best practices for AI implementation. This research offers a holistic view of AI's influence on hospitality, enriching understanding and guiding industry practices. As the hotel industry continues to evolve, the integration of artificial intelligence (AI) technologies has become increasingly prevalent, aiming to enhance guest experience. This research investigates the impact of AI integration on guest experience enhancement within the hotel industry. The purpose of this study is to comprehensively explore how AI technologies influence various aspects of guest satisfaction in hotels. A mixed-methods approach is employed, combining quantitative analysis of guest feedback data with qualitative methods by interviewing the guests staying in the hotel. Data is collected from a diverse range of hotels that have implemented AI technologies, allowing for a nuanced understanding of the impacts across their establishments. This research is expected to provide valuable insights into the multifaceted effects of AI integration in the hotel industry. Specifically, it aims to identify the specific AI applications that most significantly contribute to guest satisfaction levels. Additionally, the study seeks to uncover potential challenges and limitations associated with AI implementation, as well as best practices for successful integration. This topic lies in its comprehensive examination of AI's impact on both guest experience within the hotel industry. While previous research has explored AI's role in hospitality, few studies have undertaken such a holistic analysis, considering its implications for guests. By addressing this gap, this research contributes to a deeper understanding of the transformative effects of AI in the hotel sector, providing practical insights for industry practitioners and stakeholders.

The integration of artificial intelligence (AI) in nonoperating room anesthesia (NORA) represents a timely and significant advancement. As the demand for NORA services expands, the application of AI is poised to improve patient selection, perioperative care, and anesthesia delivery. This review examines AI's growing impact on NORA and how it can optimize our clinical practice in the near future. AI has already improved various aspects of anesthesia, including preoperative assessment, intraoperative management, and postoperative care. Studies highlight AI's role in patient risk stratification, real-time decision support, and predictive modeling for patient outcomes. Notably, AI applications can be used to target patients at risk of complications, alert clinicians to the upcoming occurrence of an intraoperative adverse event such as hypotension or hypoxemia, or predict their tolerance of anesthesia after the procedure. Despite these advances, challenges persist, including ethical considerations, algorithmic bias, data security, and the need for transparent decision-making processes within AI systems. The findings underscore the substantial benefits of AI in NORA, which include improved safety, efficiency, and personalized care. AI's predictive capabilities in assessing hypoxemia risk and other perioperative events, have demonstrated potential to exceed human prognostic accuracy. The implications of these findings advocate for a careful yet progressive adoption of AI in clinical practice, encouraging the development of robust ethical guidelines, continual professional training, and comprehensive data management strategies. Furthermore, AI's role in anesthesia underscores the need for multidisciplinary research to address the limitations and fully leverage AI's capabilities for patient-centered anesthesia care.