Role of Artificial Intelligence in Mental 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.

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

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.

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

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.

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.

Supply chain management (SCM) using artificial intelligence (AI) transforms business practices by encouraging sustainability. Gaining insight into AI's role in improving supply chain effectiveness and lowering environmental impact is essential as demand for sustainable practices rises. This study aims to investigate how AI contributes to sustainability in SCM and determine the primary challenges and opportunities associated with implementing AI. The study aims to provide an extensive review of AI's potential to assist sustainable and green supply chain practices. This standard and strategic literature review was conducted employing the Scopus database. The five-stage methodology was adopted in the review process, which includes pilot search, locating studies, study selection, synthesis analysis, and reporting. The choice of 82 relevant studies on AI and sustainable SCM was made during the review after the exclusion of irrelevant articles. The review emphasises AI's significant role in enhancing sustainability in SCM by reducing environmental impact, improving resource efficiency, and promoting green practices. However, the study also highlights the identification of challenges such as integration complexity, implementation cost, and technological limitations and future agenda.

This chapter provides a comprehensive exploration of the integration of artificial intelligence (AI) in data analysis and business intelligence. It begins by elucidating fundamental AI techniques such as machine learning, natural language processing, and deep learning, showcasing their applicability in deciphering complex datasets. Real-world case studies spanning various industries underscore AI's capacity to unveil patterns, predict trends, and optimize operations. The chapter further delves into AI's role in enhancing business intelligence platforms, facilitating informed decision-making through timely and accurate insights. Ethical considerations and challenges inherent in AI-driven analysis are addressed, emphasizing responsible implementation. Ultimately, the chapter offers a glimpse into future advancements while highlighting AI's pivotal role in reshaping data-driven decision-making processes and fostering innovation in the business realm.

This paper reviews the diverse applications of Artificial Intelligence (AI) in agricultural land planning, highlighting how AI enhances agricultural production efficiency, optimizes resource allocation, and strengthens decision-making quality to promote sustainable agriculture. The article discusses AI's role in land suitability analysis, precision agriculture, integration into decision support systems, and the challenges and limitations of technology, emphasizing AI's significant role in advancing sustainable agricultural development and future research directions. Despite challenges such as data quality, model transparency, and ethical issues, AI's application prospects remain broad, potentially becoming a significant driving force in agricultural land planning and global food security.

Background: Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, especially by computers. In the recent past, the utility of AI has been tested and proven in many fields like health care, education, finance, defense, etc. AI has received so much attention these days and is described as the new electricity and the fourth industrial revolution. It is anticipated that AI’s role will be crucial and imminent soon in different fields, including the health sector. The medical graduates need to update their knowledge and keep pace with the new developments in technology. Given this, our study was designed to evaluate the awareness and perception of medical students regarding the basic concepts of AI and its implications in health care. Aim and Objective: The aim and objective of the study are to assess the knowledge and perceptions of medical students about AI at a tertiary care teaching hospital. Materials and Methods: The study was a questionnaire-based, cross-sectional study. A prevalidated, self-made questionnaire was distributed to 264 medical students of all professional years through Google Forms. The student’s knowledge, opinions, and perceptions of AI were evaluated. The analysis of the collected data was done with an Excel sheet and using descriptive statistical measures such as mean and percentages. Results: Our study revealed that the majority of the students (79.16%) were under the impression that AI decreases errors in clinical practice. 86% of participants agreed that AI aids doctors in making accurate decisions in the diagnosis and treatment of different conditions. Around 72% of the students said that AI guides the patient to choose the right medical care. Onethird of the participants thought that AI hampers the relationship between patient and doctor by breaking trust between them. A few subsets of participants expressed concerns over ethical issues, confidentiality, empathy, and sympathy, which are considered to be the pillars of medical practice. Conclusions: Our study concluded that medical students at Mamatha Academy of Medical Sciences, Hyderabad, have positive perceptions and good awareness about AI and its use in health-care practice, but they still need a better understanding of AI.

Background: The integration of artificial intelligence (AI) in healthcare has significantly transformed clinical practices, offering substantial improvements in diagnosis, treatment planning, and patient outcome predictions. AI technologies, including artificial neural networks, fuzzy expert systems, and hybrid intelligent systems, are advancing the field of augmented medicine by combining AI with traditional healthcare practices. Methods: This study reviews the diverse applications of AI in healthcare, focusing on its impact on clinical procedures, disease detection, and healthcare management. The analysis covers the use of AI-driven tools such as surgical navigation systems, augmented reality for pain management, and machine learning algorithms for early disease detection and clinical documentation. Results: AI technologies like AccuVein and augmented reality headsets have enhanced clinical procedures such as intravenous placements and surgical interventions. Advances in machine learning, particularly neural networks and deep learning, have improved the detection of complex patterns in imaging data, facilitating early diagnosis of diseases like cancer and pneumonia. Natural language processing (NLP) has enhanced the analysis and classification of clinical documentation, while robotic process automation (RPA) has optimized administrative tasks. AI's role in managing infectious diseases, particularly during the COVID-19 pandemic, has been critical, demonstrating its potential in screening, diagnosis, and treatment surveillance. AI applications in oncology and laboratory medicine have also shown increased accuracy and efficiency in disease diagnosis and patient care. Conclusion: AI is revolutionizing healthcare by enhancing diagnostic accuracy, treatment efficacy, and patient care quality. Despite its transformative potential, challenges such as legal accountability and data bias must be addressed for successful integration into healthcare systems. Continued research and innovation in AI applications are essential to maximizing its benefits while minimizing associated risks.

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.

Purpose: The aim of the study was to examine the Role of Artificial Intelligence in Marketing Automation in China Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The study found that the role of artificial intelligence (AI) in marketing automation in China has revolutionized the marketing landscape, offering unprecedented opportunities for efficiency, personalization, and data-driven decision-making. AI technologies, such as machine learning, natural language processing, and predictive analytics, have enabled Chinese businesses to automate and optimize various marketing processes, leading to enhanced customer engagement and improved ROI. AI-powered marketing automation tools have facilitated precise targeting and segmentation, allowing brands to deliver highly personalized content and offers to individual consumers at the right time through the right channels. This level of personalization has significantly improved customer experiences, fostering stronger brand loyalty and higher conversion rates. Furthermore, AI has enhanced the ability of marketers in China to analyze vast amounts of data quickly and accurately, providing deep insights into consumer behavior, preferences, and trends. This has enabled more informed strategic decisions, agile responses to market changes, and proactive identification of new opportunities. Unique Contribution to Theory, Practice and Policy: Technology Acceptance Model, Social Cognitive Theory &amp; Resource-Based View (RBV) may be used to anchor future studies on Role of Artificial Intelligence in Marketing Automation in China. Organizations should prioritize hiring skilled AI professionals and investing in robust AI infrastructure to effectively leverage AI technologies for marketing automation. This includes building in-house capabilities or partnering with external vendors to develop and implement AI-powered marketing solutions tailored to specific business needs. Encourage marketing teams to experiment with AI-driven tools and technologies in their campaigns and initiatives. Create a supportive environment that rewards innovation and learning from failures, enabling organizations to iterate and improve AI-driven marketing strategies over time. Policymakers should collaborate with industry stakeholders to develop regulatory frameworks that govern the ethical use of AI in marketing automation. This includes establishing guidelines for data privacy, algorithmic transparency, and consumer protection to ensure that AI technologies are deployed responsibly and ethically.