Artificial Intelligence Knowledge Research Articles

Exploring clinical specialists’ perspectives on the future role of AI: evaluating replacement perceptions, benefits, and drawbacks

Background of studyOver the past few decades, the utilization of Artificial Intelligence (AI) has surged in popularity, and its application in the medical field is witnessing a global increase. Nevertheless, the implementation of AI-based healthcare solutions has been slow in developing nations like Pakistan. This unique study aims to assess the opinion of clinical specialists on the future replacement of AI, its associated benefits, and its drawbacks in form southern region of Pakistan.Material and methodsA cross-sectional selective study was conducted from 140 clinical specialists (Surgery = 24, Pathology = 31, Radiology = 35, Gynecology = 35, Pediatric = 17) from the neglected southern Punjab region of Pakistan. The study was analyzed using χ2 - the test of association and the nexus between different factors was examined by multinomial logistic regression.ResultsOut of 140 respondents, 34 (24.3%) believed hospitals were ready for AI, while 81 (57.9%) disagreed. Additionally, 42(30.0%) were concerned about privacy violations, and 70(50%) feared AI could lead to unemployment. Specialists with less than 6 years of experience are more likely to embrace AI (p = 0.0327, OR = 3.184, 95% C.I; 0.262, 3.556) and those who firmly believe that AI knowledge will not replace their future tasks exhibit a lower likelihood of accepting AI (p = 0.015, OR = 0.235, 95% C.I: (0.073, 0.758). Clinical specialists who perceive AI as a technology that encompasses both drawbacks and benefits demonstrated a higher likelihood of accepting its adoption (p = 0.084, OR = 2.969, 95% C.I; 0.865, 5.187).ConclusionClinical specialists have embraced AI as the future of the medical field while acknowledging concerns about privacy and unemployment.

Digital proficiency: assessing knowledge, attitudes, and skills in digital transformation, health literacy, and artificial intelligence among university nursing students

BackgroundImplementing digital transformation and artificial intelligence (AI) in education and practice necessitates understanding nursing students’ attitudes and behaviors as end-users toward current and future digital and AI applications.PurposeThis study aimed to assess the perceived knowledge, attitudes, and skills of nursing students regarding digital transformation, as well as their digital health literacy (DHL) and attitudes toward AI. Furthermore, we investigated the potential correlations among these variables.MethodsA descriptive correlational design was employed in a Saudi nursing college utilizing a convenience sample of 266 nursing students. A structured questionnaire consisting of six sections was used, covering personal information, knowledge, skills and attitudes toward digital transformation, digital skills, DHL, and attitudes toward AI. Descriptive statistics and Pearson correlation were employed for data analysis.ResultsNursing students exhibited good knowledge of and positive attitudes toward digital transformation services. They possessed strong digital skills, and their DHL and positive attitude toward AI were commendable. Overall, the findings indicated significant positive correlations between knowledge of digital transformation services and all the digital variables measured (p = < 0.05). Senior students reported greater digital knowledge and a positive attitude toward AI.ConclusionThe study recommends an innovative undergraduate curriculum that integrates opportunities for hands-on experience with digital healthcare technologies to enhance their digital literacy and skills.

Assessing the Knowledge and Perception of Artificial Intelligence for Teaching and Research among Lecturers in the Faculties of Arts in Nigeria

This study assesses the knowledge of artificial intelligence (AI) for teaching and research among lecturers in the faculties of arts in Nigeria. Despite the growing recognition of AI's potential to enhance educational practices, there are significant gaps in educators' AI literacy. The research adopts a quantitative approach, surveying lecturers across Nigerian universities to gauge their awareness, engagement, and perceptions of AI integration. Results reveal a moderate level of AI awareness among respondents, with a notable interest in further training tailored to arts disciplines. Challenges such as technical barriers and limited resources hinder seamless AI integration, highlighting the need for targeted interventions and support mechanisms. Recommendations include enhanced training programs, infrastructure improvement, and ethical guidelines to facilitate responsible AI utilization in arts education. Addressing these challenges and fostering AI literacy among educators can create an enabling environment for leveraging AI to enhance teaching and research outcomes in Nigerian arts faculties.

Knowledge, attitude, and practice of artificial intelligence among medical students in Sudan: A cross-sectional study

Introduction: In this cross-sectional study, we explored the knowledge, attitudes, and practices related to artificial intelligence (AI) among medical students in Sudan. With AI increasingly impacting healthcare, understanding its integration into medical education is crucial. This study aimed to assess the current state of AI awareness, perceptions, and practical experiences among medical students in Sudan. We aimed to evaluate the extent of AI familiarity among Sudanese medical students by examining their attitudes toward its application in medicine. Additionally, this study seeks to identify the factors influencing knowledge levels and explore the practical implementation of AI in the medical field. Method: A web-based survey was distributed to medical students in Sudan via social media platforms and email during October 2023. The survey included questions on demographic information, knowledge of AI, attitudes toward its applications, and practical experiences. The descriptive statistics, chi-square tests, logistic regression, and correlations were analysed using SPSS version 26.0. Results: Out of the 762 participants, the majority exhibited a basic understanding of AI, but detailed knowledge of its applications was limited. Positive attitudes toward the importance of AI in diagnosis, radiology, and pathology were prevalent. However, practical application of these methods was infrequent, with only a minority of the participants having hands-on experience. Factors influencing knowledge included the lack of a formal curriculum and gender disparities. Conclusion: This study highlights the need for comprehensive AI education in medical training programs in Sudan. While participants displayed positive attitudes, there was a notable gap in practical experience. Addressing these gaps through targeted educational interventions is crucial for preparing future healthcare professionals to navigate the evolving landscape of AI in medicine. Recommendations: Policy efforts should focus on integrating AI education into the medical curriculum to ensure readiness for the technological advancements shaping the future of healthcare.

Perceptions and attitudes of dental students and dentists in South Korea toward artificial intelligence: a subgroup analysis based on professional seniority

BackgroundThis study explored dental students’ and dentists’ perceptions and attitudes toward artificial intelligence (AI) and analyzed differences according to professional seniority.MethodsIn September to November 2022, online surveys using Google Forms were conducted at 2 dental colleges and on 2 dental websites. The questionnaire consisted of general information (8 or 10 items) and participants’ perceptions, confidence, predictions, and perceived future prospects regarding AI (17 items). A multivariate logistic regression analysis was performed on 4 questions representing perceptions and attitudes toward AI to identify highly influential factors according to position, age, sex, residence, and self-reported knowledge level about AI of respondents. Participants were reclassified into 2 subgroups based on students’ years in school and 4 subgroups based on dentists’ years of experience. The chi-square test or Fisher’s exact test was used to determine differences between dental students and dentists and between subgroups for all 17 questions.ResultsThe study included 120 dental students and 96 dentists. Participants with high level of AI knowledge were more likely to be interested in AI compared to those with moderate or low level (adjusted OR 24.345, p < 0.001). Most dental students (60.8%) and dentists (67.7%) predicted that dental AI would complement human limitations. Dental students responded that they would actively use AI in almost all cases (40.8%), while dentists responded that they would use AI only when necessary (44.8%). Dentists with 11–20 years of experience were the most likely to disagree that AI could outperform skilled dentists (50.0%), and respondents with longer careers had higher response rates regarding the need for AI education in schools.ConclusionsKnowledge level about AI emerged as the factor influencing perceptions and attitudes toward AI, with both dental students and dentists showing similar views on recognizing the potential of AI as an auxiliary tool. However, students’ and dentists’ willingness to use AI differed. Although dentists differed in their confidence in the abilities of AI, all dentists recognized the need for education on AI. AI adoption is becoming a reality in dentistry, which requires proper awareness, proper use, and comprehensive AI education.

Experts’ perception of artificial intelligence knowledge in Egyptian newsrooms

Experts’ perception of artificial intelligence knowledge in Egyptian newsrooms

Study of artificial intelligence and its awareness in dental mainly aesthetic dentistry and prosthodontics

: Each day there is a new research. Similarly in dental profession with recent advances, artificial intelligence is a new trend. It's for better accurate superior health care. This makes dental diagnosis treatment plan better and successful. : This study is to assess the awareness and knowledge of artificial intelligence in dental profession. : this cross sectional study was done by questionnaire in 100 dental professionals. This was to know their knowledge and awareness in artificial intelligence. : very few dental professionals know of artificially intelligence. Those who know actually know v little. : Attempt should be made to know more about artificial intelligence. Its new application and more uses of artificial intelligence can be done in dental in future.

Efficiency, accuracy, and health professional's perspectives regarding artificial intelligence in radiology practice: A scoping review

AbstractIn this scoping review, we evaluated the performance of artificial intelligence (AI) in clinical radiology practice and examined health professionals' perspectives regarding AI use in radiology. This review followed the Joanna Briggs Institute (JBI) methodological guidelines. We searched multiple databases and the gray literature from March 15, 2016 to December 31, 2023. Of 49 articles reviewed, 13 assessed the performance of AI in radiology clinical practice, and 36 examined the attitudes of health professionals toward the use of AI in radiology. In four separate studies, AI significantly improved the diagnostic sensitivity or detection rate. Furthermore, six articles emphasized a significant reduction in case reading times with AI use. Although three studies suggested an increase in specificity with the assistance of AI, these findings did not reach statistical significance. Health professionals expressed the belief that AI would have a significant impact on radiology but would not replace radiologists in the near future. Limited knowledge of AI was observed among health professionals, who supported increased education and explicit regulations and guidelines related to AI. Overall, AI can enhance diagnostic efficiency and accuracy in clinical radiology practice. However, knowledge gaps and the concerns of health professionals should be addressed by prioritizing education and reinforcing ethical and legal regulations to facilitate the advancement of AI use in radiology. This scoping review provides evidence toward a comprehensive understanding of AI's potential in clinical radiology practice, promoting its use and stimulating further discussion on related challenges and implications.

Global AI governance: barriers and pathways forward

Abstract Recent advances in artificial intelligence (AI), combined with a proliferation in use, have led to a newfound emphasis on strengthening the global governance of AI. In this article, we assess the prospects for stronger global AI governance and consider potential pathways forward. We map the nascent landscape of international regimes focused on AI governance and conclude that a governance deficit remains due to the inadequacy of existing initiatives, gaps in the landscape and difficulties reaching agreement over more appropriate mechanisms. First-order cooperation problems stemming from interstate competition and second-order cooperation problems from dysfunctional international institutions problematize overcoming current deficiencies in global AI governance. In light of these cooperation problems, we evaluate two pathways for strengthening global AI governance: (a) developing new centralized international AI institution(s); and (b) strengthening coordination between, and capacities of, existing institutions. We argue that strengthening the existing weak ‘regime complex’ of international institutions is the more politically legitimate and viable path forward. Improving coordination between, and capacities of, existing international institutions governing AI would support inclusive and mutually reinforcing policy change that can mitigate a range of risks associated with these technologies. As first steps for strengthening the weak AI regime complex, we recommend that the OECD is foregrounded as a centre of expert AI knowledge so that it can facilitate peer pressure among states and harmonization of policies. We also encourage scrutiny of different nodes in the regime complex to ensure that they are fulfilling appropriate functions based on their democratic mandates.

AI technologies affording the orchestration of ecosystem-based business models: the moderating role of AI knowledge spillover

Due to the extraordinary capacity of artificial intelligence (AI) to process rich information from various sources, an increasing number of enterprises are using AI for the development of ecosystem-based business models (EBMs) that require better orchestration of multiple stakeholders for a dynamic, sustainable balance among people, plant, and profit. However, given the nascency of relevant issues, there exists scarce empirical evidence. To fill this gap, this research follows the affordance perspective, considering AI technology as an object and the EBM as a use context, thereby exploring how and whether AI technologies afford the orchestration of EBMs. Based on data from Chinese A-share listed companies between the period from 2014 to 2021, our findings show an inverted U-shape quadratic relationship between AI and EBM, moderated by knowledge spillover. Our results enhance the understanding of the role of AI in configuring EBMs, thus providing novel insights into the mechanisms between AI and a specific business practice with societal concerns (i.e., EBM).

Development of a transdisciplinary education concept to prepare textile technology students for dealing with AI

A basic understanding of the mechanisms and implications of the use of Artificial intelligence (AI) is crucial to effectively implement AI in business and society. To achieve this goal, the research project described here aims to provide students from different disciplines with practical AI knowledge. Instead of focusing on traditional teaching approaches, students work together on transdisciplinary and interdisciplinary projects. The basis here is provided by department-specific application scenarios. Supported by learning nuggets, expert lectures and an accessible software infrastructure, students thus gain easy access to AI topics.The goal in the Faculty of Textile and Clothing Technology is to train specialists in the use of AI in the textile process chain. Students will learn how to classify image data of fibers based on a practical example. Among other things, they will generate a data set for a neural network and work closely with computer science students to implement it. The focus is on enabling students to apply AI independently and thus create the basis for new innovative ideas in industry

Knowledge, Awareness and Practice of Artificial Intelligence and Types of Realities Among Healthcare Professionals: A Nationwide Survey From Pakistan.

Background Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, enabling them to perform tasks. The advancements in AI have also improved virtual reality (VR), augmented reality (AR) and mixed reality (MR) experience allowing a greater opportunity for use in the field of medicine. Objective To evaluate the knowledge, attitude and practice of AI and types of realities among Pakistani healthcare professionals (HCPs). Materials and methods This was a prospective, nationwide study designed at the Department of Neurosurgery at Punjab Institute of Neurosciences (PINS), Lahore, was conducted between January 2024 to February 2024. More than 500 HCPswere approached, out of which 176 participated in this survey consensually. A pre-formed general questionnaire based on knowledge, attitude and practices of AI and types of realities was modified according to local conditions. Google Forms (Google Inc., USA) was used to conduct the one-time sign up response. Statistical Package for Social Sciences (IBM SPSS Statistics for Windows, Version 24, USA) was used to analyze submitted responses. Results About 69.9% respondents were male HCPs. Most of the respondents were from the fields of neurosurgery, medicine and general surgery, i.e., 10.80%, 10.20% and 4%, respectively. More than 90% HCPs used Internet and electronic devices daily. A majority of 62.50% respondents agreed that AI brings benefits for the patients, while at the same time, 45.50% agreed that they would not trust the assessment of AI more than that of HCPs. 61% HCPs feared that AI-based systems could be manipulated from the outside sources, like terrorists and hackers. Although 90% respondents knew the definition of AR and VR,a strikingly low 40% respondents could only identify the practical applications of these realities when asked in a mini-quiz. About 61.40% HCPs never used any AI-based application throughout their clinical practice, but Google Health was used by 29.50% respondents, followedby Remote Patient Monitoring AI application used by 3.4% individuals. Conclusion There is an evident under-utilization of AI and types of realities in clinical practice in Pakistan. Lack of awareness, paucity of resources and conventional clinical practices are the key reasons identified. Pakistan is on the path towards the point where the developed world is currently. There is a potential to move past the initial stages of AI implementation and into more advanced modes of adopting AI and types of realities.

AI technology specialization and national competitiveness.

This study investigates the factors influencing specialization in artificial intelligence (AI) technology, a critical element of national competitiveness. We utilized a revealed comparative advantage matrix to evaluate technological specialization across countries and employed a three-way fixed-effect panel logit model to examine the relationship between AI specialization and its determinants. The results indicate that the development of AI technology is strongly contingent on a nation's pre-existing technological capabilities, which significantly affect AI specialization in emerging domains. Additionally, this study reveals that scientific knowledge has a positive impact on technological specialization, highlighting the necessity of integrating scientific advancements with technological sectors. Although complex technologies positively influence AI specialization, their effect is less pronounced than that of scientific knowledge. This suggests that in rapidly advancing fields, such as AI, incorporating new scientific knowledge into related industries may be more advantageous than simply advancing existing technologies to outpace competitors. This insight points nations toward enhancing AI competitiveness in new areas, emphasizing the vital importance of both scientific and technological capabilities, and the integration of novel AI knowledge with established sectors. This research offers critical guidance for policymakers in less technologically and economically developed countries, as these nations may not have the technological infrastructure required to foster AI specialization through increased technical complexity.

Knowledge and Perception of Artificial Intelligence among Faculty Members and Students at Batterjee Medical College

ABSTRACT Background: Mounting research suggests that artificial intelligence (AI) is one of the innovations that aid in the patient’s diagnosis and treatment, but unfortunately limited research has been conducted in this regard in the Kingdom of Saudi Arabia (KSA). Hence, this study aimed to assess the level of knowledge and awareness of AI among faculty members and medicine students in one of the premier medical colleges in KSA. Methods: A cross-sectional descriptive study was conducted at Batterjee Medical College (BMC), Jeddah (KSA), from November 2022 to April 2023. Result: A total of 131 participants contributed to our study, of which three were excluded due to incomplete responses, thereby giving a response rate of 98%. Conclusion: 85.4% of the respondents believe that AI has a positive impact on the healthcare system and physicians in general. Hence, there should be a mandatory course in medical schools that can prepare future doctors to diagnose patients more accurately, make predictions about patients’ future health, and recommend better treatments.

IJCM_269A: Knowledge, attitude, and practice of artificial intelligence in medical field among Undergraduate and Postgraduate medical students in Tamil Nadu: A cross-sectional online survey.

Background: Artificial Intelligence (AI) in the health care has gained attention worldwide due to its potential to revolutionize health care delivery, improve diagnostics, and enhance patient outcomes. AI has the potential to address challenges in Indian health care sector like shortage of workforce by providing decision support, automating repetitive tasks, and improving diagnostic accuracy. Objective: To assess the Knowledge, Attitude and Practice (KAP) of Artificial Intelligence in health care among Undergraduate and Postgraduate medical students in Tamil Nadu. Methodology: A cross-sectional online survey was conducted among 200 undergraduate and postgraduate medical students over a period of 2 months (Sept to Oct 2023). Data collection was done using Google form which comprises of a pretested, semi-structured questionnaire and statistical analysis was done using IBM SPSS version 21. Results: Out of 200 participants, 94 (47%) were Undergraduates and 106 (53%) were Postgraduates. In total 123 (61.5%) had good knowledge, 190 (95%) had positive attitude and 61 (30.5%) had good practice towards Artificial Intelligence in healthcare. Postgraduates had better knowledge and practice than undergraduates Conclusion: A majority of the participants displayed good knowledge and positive attitude towards Artificial Intelligence in healthcare. Notably, postgraduates outperformed undergraduates, suggesting the need for tailored educational interventions. While AI training in the curriculum remains limited, the high willingness to engage with AI in the future underscores its potential for transformative impact in healthcare.

Radiologists and trainees’ perspectives on artificial intelligence

Background and objectivesThe purpose of this study was to investigate perspectives held by radiologists on the use of artificial intelligence (AI) in their day-to-day work and to identify factors limiting its routine implementation. Materials and methodsSpanish board-certified radiologists and trainees completed an online survey of 21 questions on general information and communications technology (ICT) and AI in radiology. Analysis was carried out for the subgroups of gender, age, and professional experience. Associations with a p-value <0.05 were considered statistically significant. ResultsA total of 102 radiologists and trainees completed the questionnaire. No se observaron diferencias estadísticas significativas entre los grupos de sexo. A significant difference was detected in ICT and AI knowledge between age groups, with participants under 40 and those between 40 and 55 years old demonstrating better ICT knowledge (p < 0.01). The survey results revealed that 77.4% of participants believed that AI represents an opportunity for the radiology profession in the future, while 9.8% believed it would have no impact. Three main practical application areas for AI in radiology were proposed: in screening (23.36%), in image interpretation and reporting (21.17%), and in the requesting of imaging and patient scheduling (14.6%). The biggest concern among the surveyed population was the potential increase in workload. ConclusionsA positive attitude toward AI was observed among Spanish radiologists, with the majority believing that AI could offer opportunities for the radiology profession in the near future. AI training programmes may further improve its acceptance among professionals.

Examining artificial intelligence literacy among pre-service teachers for future classrooms

In the context of global integration and increasing reliance on Artificial Intelligence (AI) in education, evaluating the AI literacy of pre-service teachers is crucial. As future architects of educational systems, pre-service teachers must not only possess pedagogical expertise but also a strong foundation in AI literacy. This quantitative study examines AI literacy among 529 pre-service teachers in a Nigerian university, utilizing structural equation modeling (SEM) for comprehensive analysis. The research explores various dimensions of AI literacy, revealing that a profound understanding of AI significantly predicts positive outcomes in AI use, detection, ethics, creation, and problem-solving. However, no correlation exists between AI knowledge and emotion regulation or the assumption that active AI use enhances AI detection capabilities. The study identifies a trade-off between AI application and creation, emphasizing the ethical considerations intertwined with emotional and persuasive facets of AI use. It also supports the link between AI creation and problem-solving, emphasizing the foundational role of AI knowledge in shaping diverse aspects of AI literacy among pre-service teachers. The findings offer valuable insights for educators, administrators, policymakers, and researchers aiming to enhance AI literacy in pre-service teacher education programs.

A cross-sectional study of knowledge, perception and acceptance of artificial intelligence in healthcare among medical graduates in Bengaluru

A cross-sectional study of knowledge, perception and acceptance of artificial intelligence in healthcare among medical graduates in Bengaluru

Enhancing teacher AI literacy and integration through different types of cases in teacher professional development

Integrating artificial intelligence (AI) into teaching practices is increasingly vital for preparing students for a technology-centric future. This study examined the influence of a case-based AI professional development (PD) program on AI integration strategies and AI literacy among seven middle school science teachers. Employing three distinct case problems, from well-structured to ill-structured, the AI PD program aimed to stimulate teachers’ reflection on AI literacy development and encourage the construction of problem-solving and AI integration strategies within various pedagogical contexts. Analysis of video-recorded case discussions revealed that teachers primarily drew on personal experiences for collaborative problem-solving across the three cases. However, the complexity of the case problems influenced their approach to knowledge co-construction, and dealing with ill-structured problems promoted the application of new knowledge. Through analyzing the survey data, we found a marked increase in teachers’ AI literacy, particularly in the domain of knowing and understanding AI, suggesting a pivotal role for direct instruction that supports AI literacy growth. However, their application of this AI knowledge was limited during the case discussions, while other domains of teacher AI literacy were more frequently employed. The findings highlight the importance of combining direct instruction with case-based discussions in AI-related PD programs to bolster teacher AI literacy effectively. The research has implications for using a case-based learning approach during short-term PD initiatives and advocates the ongoing need for comprehensive AI literacy development to facilitate teachers’ AI integration in subject-specific teaching.

예비유아교사가 지각하는 유아과학교육 필요성과 인공지능 지식, 과학교수태도 및 과학교수효능감 간의 구조적 관계

The purpose of this study was to investigate the structural relationship between pre-service early childhood teachers' perceived need for early childhood science education and their knowledge of artificial intelligence, attitudes toward science instruction, and self-efficacy in science teaching. The subjects of this study were 224 pre-service early childhood teachers in Seoul, Gyeonggi, Chungcheong, and Gyeongsang provinces, and their data were analyzed for the research. The relationships among research variables were analyzed using Pearson's correlation analysis, and the structural relationships were explored through structural equation modeling (SEM) using SPSS 25.0 and AMOS 23.0. These results were as followed. First, when examining the tendencies of research variables, all scored above 3.0 points, indicating a high level, and normality was considered satisfactory in the analysis. Second, statistically significant positive correlations were observed among all variables, with a particular focus on endogenous variables such as science teaching self-efficacy and its sub-factors, as well as attitudes toward science instruction and its sub-factors. Third, the measurement model was found to be appropriate. Examining the path coefficients of the structural model revealed that the perceived need for early childhood science education had statistically significant effects on artificial intelligence knowledge, attitudes toward science instruction, and science teaching self-efficacy. Statistically significant effects of artificial intelligence knowledge were found on attitudes toward science instruction and science teaching self-efficacy. Mediation analysis revealed that knowledge of artificial intelligence partially mediated the influence of perceived need for early childhood science education on attitudes toward science instruction and science teaching self-efficacy. These research findings suggest the need to consider incorporating educational approaches related to artificial intelligence to enhance the foundation of early childhood science education.