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

Objective: To assess the knowledge and attitude of undergraduate nursing students towards the role of artificial intelligence (AI) in healthcare, aiming to understand their readiness and perception of integrating AI into clinical practice. Methods: A descriptive cross-sectional study was conducted to assess the knowledge and attitude of nursing students toward the role of Artificial Intelligence (AI) in healthcare. A total of 208 students were selected using non-probability convenience sampling technique. Informed consent was obtained from all the participants prior to the data collection. The study consisted of two parts: a 10-items knowledge questionnaire and a 10-items attitude questionnaire, designed to evaluate students' understanding of AI technologies and their perspectives on its integration into healthcare settings. The questionnaires were close-ended, focusing on basic knowledge about AI. Results: There was a significant difference in AI knowledge and attitudes between various groups. Male’s demonstrated significantly higher AI knowledge (82.1%) compared to females (69.8%) with a p-value of 0.003. Participants who attended formal AI training exhibited better knowledge, with 41.9% showing adequate knowledge, compared to 25.4% of non-attendees (p = 0.010). Prior exposure to AI workshops significantly influenced attitudes, with attendees showing a more positive attitude toward AI (67.4%) compared to non-attendees (35.8%), with a p-value of <0.001. Gender and formal AI training were found to significantly impact both knowledge and attitude towards AI in healthcare. Conclusion: The study highlights significant differences in AI knowledge and attitude among undergraduate nursing students, with males, participants with formal AI training, and those exposed to AI workshops demonstrating higher levels of knowledge and more positive attitude. These findings underscore the importance of incorporating AI education and training into nursing curricula to better prepare students for the integration of AI in clinical practice.

Artificial Intelligence (AI) has found a lot of scope in diversified applications including health care systems. Due to the rapid increase in digitization and change in the life style lot of people are facing health care issues like mental diseases. Now the days AI is use to help health care members with its analysis like tumor, cyst, cancer, dermatology issues etc. Looking towards the increasing cases there is a urgent demand of AI in medical specially in mental health care.Many electronic systems are used for the health data analysis so the combination of AI within system can help the patients. Due to the pandemic there is increase in health issues and it has pushed the limits for increase in need of mental health care system using AI. Since AI can provide services like personalize care, remote access, guiding patient, online doctor’s advice etc. AI can be used to identify the individual with high risk also it can provide intervention to treat and prevent mental illness. This work presents the comparison and role of different AI based mental healthcare analysis. As AI using electronic health record, brain imaging and other sensing system can predict the issues in individual and help to monitor patient’s progress and helps the doctor to alter treatment if needed and can help in decrease in suicidal issues. Apart from indentifying the particular issue in patient AI can help the patient to assign the right therapist as per his/her problem. Thus the patient is been given with right therapy at right time. It can also, guide the care taker to give medicine at given time. Natural language processing and Machine learning can be used to find the problem in individual along with its social media presence can be an effective tool to identify once mental health. This information can assist the healthcare practitioner to identify particular problem and guide for treatment. There is also a limitation for collecting data and training the AI based system which is discussed in this work. Along with that the technology limitation and challenges are well described.

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.

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.

Purpose: This study investigates the perceptions of medical students at Northern Border University (NBU) regarding the role of Artificial Intelligence (AI) in healthcare. Methods: A cross-sectional survey was conducted among NBU medical students between June 2024 and March 2025, using a structured questionnaire. A random sampling method was used to recruit 425 participants. A sample of 425 students was selected based on a calculated minimum of 386 participants, ensuring representation with a 95% confidence level and 5% margin of error. Data were analysed using STATA/SE 11.2 with descriptive and inferential statistics. Results: Most students acknowledged the beneficial role of AI in enhancing healthcare information access (80.71%), reducing clinical errors (59.06%), and improving decision-making (56.24%). The overall mean perception score was 5.78 (SD ±2.95) out of 10. However, concerns were noted regarding AI's impact on the patient-doctor relationship and confidentiality. No statistically significant differences in perception were found based on age (p = 0.28), gender (p = 0.07), or academic year (p = 0.06). Conclusion: NBU medical students largely recognize the potential of AI in healthcare but express reservations about its ethical and interpersonal implications. These insights can guide curriculum enhancements to better integrate AI in medical education

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.

Objective: This article undertakes a comprehensive exploration of the constructivist paradigm in artificial intelligence (AI) development, aiming to uncover how constructivist perspectives shape our understanding of AI. It delves into the evolution of AI thought, emphasizing the significance of constructivist epistemology in comprehending AI's philosophical and cognitive dimensions. Method: The study employs a variety of philosophical methodologies, including historical-philosophical analysis, comparative analysis of philosophical teachings, and a system-structural dialectical approach. These methods facilitate an in-depth examination of AI's conceptual intricacies within a constructivist framework, focusing on the relationship between artificial and natural intelligence and the epistemological implications of AI. Results: The investigation reveals that the main challenge in AI research is the absence of clear problem-solving rules, highlighting the current limitations of human self-knowledge in logical and emotional intelligence. It showcases AI's vast capabilities, from extensive knowledge bases to real-time processing, and emphasizes AI's role in enhancing human cognitive processes. Conclusions: Artificial intelligence, as a construct of human intellect, mirrors the capacity for design and creativity inherent in human thought. The study underscores AI's foundational role in the epistemology of science and technology, advocating for a holistic understanding of the human brain as a dynamic system to further our grasp of AI and its cognitive potential.

This review aims to delve into the role of artificial intelligence in medicine. Ulcerative colitis (UC) is a chronic, inflammatory bowel disease (IBD) characterized by superficial mucosal inflammation, rectal bleeding, diarrhoea and abdominal pain. By identifying the challenges inherent in UC diagnosis, we seek to highlight the potential impact of artificial intelligence on enhancing both diagnosis and treatment methodologies for this condition. A targeted, non-systematic review of literature relating to ulcerative colitis was undertaken. The PubMed and Scopus databases were searched to categorize a well-rounded understanding of the field of artificial intelligence and its developing role in the diagnosis and treatment of ulcerative colitis. Articles that were thought to be relevant were included. This paper only included articles published in English. Artificial intelligence (AI) refers to computer algorithms capable of learning, problem solving and decision-making. Throughout our review, we highlighted the role and importance of artificial intelligence in modern medicine, emphasizing its role in diagnosis through AI-assisted endoscopies and histology analysis and its enhancements in the treatment of ulcerative colitis. Despite these advances, AI is still hindered due to its current lack of adaptability to real-world scenarios and its difficulty in widespread data availability, which hinders the growth of AI-led data analysis. When considering the potential of artificial intelligence, its ability to enhance patient care from a diagnostic and therapeutic perspective shows signs of promise. For the true utilization of artificial intelligence, some roadblocks must be addressed. The datasets available to AI may not truly reflect the real-world, which would prevent its impact in all clinical scenarios when dealing with a spectrum of patients with different backgrounds and presenting factors. Considering this, the shift in medical diagnostics and therapeutics is coinciding with evolving technology. With a continuous advancement in artificial intelligence programming and a perpetual surge in patient datasets, these networks can be further enhanced and supplemented with a greater cohort, enabling better outcomes and prediction models for the future of modern medicine.

Artificial intelligence is essential for improving corporate operations and productivity optimization. Due to the Fourth Industrial Revolution's effects on digital competitiveness, businesses are becoming more interested in upgrading to a higher technical order. This is closely related to artificial intelligence-based “smart” automation, which removes human labor from various corporate processes. (Akter et al., 2020). This study aims to look into the role and advantages of artificial intelligence (AI) tools in small and medium-sized businesses, as well as how company management will change in the future as a result of their ability to gain a competitive edge via the use or advancement of technical features. This paradigm provides resources as standard utilities that users may rent and release over the Internet as needed. (Avram, 2014). To get a comprehensive grasp of the difficulties, advantages, and moral implications of AI-driven corporate initiatives, a qualitative methodology comprising focus groups and interviews will be employed.

BackgroundThe growing use of artificial intelligence (AI) in healthcare necessitates understanding the perspectives of future practitioners. This study investigated the perceptions of German-speaking medical and dental students regarding the role of artificial intelligence (AI) in their future practices.MethodsA 28-item survey adapted from the AI in Healthcare Education Questionnaire (AIHEQ) and the Medical Student’s Attitude Toward AI in Medicine (MSATAIM) scale was administered to students in Austria, Germany, and Switzerland from April to July 2023. Participants were recruited through targeted advertisements on Facebook and Instagram and were required to be proficient in German and enrolled in medical or dental programs. The data analysis included descriptive statistics, correlations, t tests, and thematic analysis of the open-ended responses.ResultsOf the 409 valid responses (mean age = 23.13 years), only 18.2% of the participants reported receiving formal training in AI. Significant positive correlations were found between self-reported tech-savviness and AI familiarity (r = 0.67) and between confidence in finding reliable AI information and positive attitudes toward AI (r = 0.72). While no significant difference in AI familiarity was found between medical and dental students, dental students exhibited slightly more positive attitudes toward the integration of AI into their future practices.ConclusionThis study underscores the need for comprehensive AI education in medical and dental curricula to address knowledge gaps and prepare future healthcare professionals for the ethical and effective integration of AI in practice.

Human resources (HR) experts can now utilize calculations and AI to smooth out their work processes, diminish their predispositions, and work on their examination and dynamic on account of advancements in artificial intelligence (computer based intelligence) innovation. Nonetheless, a few organizations are reluctant to embrace computer based intelligence for extra use cases because of the ongoing downsides and weaknesses. In human resource management, artificial intelligence (computer based intelligence) can be extremely helpful on the grounds that it can robotize drawn-out assignments and diminish predisposition in the determination cycle. Man-made intelligence can be applied to progress authoritative procedures, improve employee engagement, and support vocation development. Employee engagement can be expanded and vocation useful learning experiences can be more individualized and custom-made with the guide of computer based intelligence innovation. The monetary expenses of setting up and maintaining artificial intelligence in HR, as well as the risk of missteps and unintentional predispositions, ought to be considered by associations. Future advancements in artificial intelligence will expand personalization, mechanization, and data based decisions in human resource management. Before very long, as artificial intelligence keeps on changing the scene of human resource management, HR managers should likewise know about the challenges they might experience. Worries about simulated intelligence making work more secure and more open are normal among HR chiefs. The recruiting and preparing of new employees is a dreary undertaking for the department of human resources. Artificial intelligence has many applications that can assist with peopling who work in human resources with their manual work. Hence, the present study has been focused to give a theoretical outline on the role of Artificial Intelligence (AI) in Human Resource Management (HRM) in recent era.

Introduction: With the expansion of next -generation sequencing (NGS) technologies and omics data analysis, genetics education has entered a new phase characterized by large volumes of complex data. In this context, traditional teaching methods have become less effective. Utilizing artificial intelligence (AI) and bioinformatics offers an innovative approach to elevate genetics education to an interactive, data -driven, and analysis -focused level. This study responds to the growing demand for data -driven and analytical training in genetics. Given the vast amount of genomic data and the complexity of the required analyses, employing AI and bioinformatics tools can significantly enhance the quality of education and research in this field. The aim of this study is to investigate the impactful role of advanced AI and bioinformatics in improving modern genetics education . Method: This study was conducted as a narrative review. Scientific sources published in PubMed, Scopus, Web of Science, and Google Scholar between 2005 and 2025 were reviewed. Articles related to the use of AI and informatics in genetics education were selected and analyzed using content analysis . Results: The review results indicated that AI -based tools, including machine learning algorithms, genomic language models, and adaptive training systems, significantly contribute to personalizing education, simulating biological processes, and analyzing genetic variants. Furthermore, practical training in bioinformatics skills —such as working with genetic databases, analytical software, biological programming, and applied biostatistics —empowers students to analyze complex genomic data. However, the lack of digital educational resources and specialized instructors continues to pose a major challenge in data -driven education . Conclusion: The integration of AI and bioinformatics into genetics education offers an innovative approach to training specialists in modern genetics. Developing localized content, virtual training courses, and policies that align the education system with technological advancements are effective strategies for enhancing the quality of genetics education in Iran and similar countries.

Purpose: The growing sophistication of financial fraud in the banking sector has necessitated the adoption of advanced technical solutions such as artificial intelligence (AI) and robotic process automation (RPA) to enhance fraud detection and prevention. This study examines the role, effectiveness, and challenges of AI and RPA in combating financial fraud, addressing gaps left by traditional rule-based systems. Methodology: This study employs a literature review methodology, synthesizing existing research, case studies, and industry reports to evaluate the impact of AI and RPA on fraud detection. Key themes analyzed include real-time analytics, anomaly detection, predictive modeling, operational efficiency, and implementation challenges. Findings: The findings reveal that AI significantly improves fraud detection accuracy, reduces false positives, and adapts to emerging threats, while RPA enhances compliance and operational efficiency by automating repetitive tasks. However, challenges such as algorithmic bias, adversarial AI attacks, data privacy concerns, high implementation costs, and ethical dilemmas around transparency and accountability hinder widespread adoption. Despite these obstacles, financial institutions report substantial reductions in fraud-related losses after integrating AI and RPA. Unique contribution to theory, practice and policy (recommendations): This study contributes to theory by consolidating insights on AI and RPA’s transformative potential in fraud detection. For practice, it recommends investing in explainable AI, robust adversarial defense mechanisms, and cost-effective RPA integration. Policymakers should establish ethical AI governance frameworks, promote regulatory alignment, and incentivize innovation to ensure financial security and transparency. The study underscores that maximizing the benefits of AI and RPA requires continuous technological advancement, ethical oversight, and collaborative regulatory efforts.

This study investigates the relationship between technology quality and consumer behavior in the field of virtual reality tourism. An online survey was conducted with 403 customers in Vietnam, spanning various age groups, who have a passion for travel and have used virtual reality (VR) and augmented reality (AR) technologies in tourism. The research findings indicate that technology quality, including information quality, system quality, and usefulness, positively influences users' perceived value of VR/AR quality. This perceived VR/AR value, in turn, has a positive impact on travel intention. However, privacy concerns were found to negatively affect users' perceived value of VR/AR. The study also examines the role of Explainable Artificial Intelligence (XAI) in positively moderating the relationship between privacy concerns and perceived VR/AR value. An interesting discovery is that XAI helps alleviate users' privacy concerns when experiencing virtual tourism technologies, addressing a limitation that conventional AI has not yet resolved. Several data analysis and validation methods were applied, including reliability testing of the measurement scale (Cronbach's alpha), Exploratory Factor Analysis (EFA), and Confirmatory Factor Analysis (CFA). Hypothesis testing and model evaluation were conducted using Structural Equation Modeling (SEM). Additionally, data analysis was supported by SPSS 26 and AMOS 28 software. The results of the study provide critical insights for technology manufacturers to enhance product quality, enabling users to optimize their virtual tourism experiences. Furthermore, the findings serve as a foundation for businesses leveraging smart tourism technologies to design and implement more effective VR/AR-based tourism products. By doing so, they can attract more tourists to real-world destinations, strengthen their competitive advantage, and promote the sustainable development of the tourism industry. This study highlights the importance of integrating advanced technologies like XAI to address user concerns and improve the adoption and satisfaction of virtual tourism solutions, ultimately contributing to the growth and sustainability of the tourism sector.