A study on the knowledge and perception of artificial intelligence

Background: Artificial intelligence (AI) has become an essential tool in all fields, including dentistry. Different levels of familiarity with the knowledge regarding AI and perception among dental professionals exist globally, with many advocating for its integration into the curriculum. However, highlighting ethical concerns like data privacy, biases, and job displacement must be addressed to ensure its responsible implementation in dentistry.Objective: This study aimed to assess the knowledge and perception of AI in dentistry among dentists working in teaching dental hospitals of Peshawar.Methods: This analytical cross-sectional study was conducted from January 2024 to June 2024 at Sardar Begum Dental College and Hospital (SBDC). The population included 475 total dentists, including house officers, postgraduate trainees, and faculty members, selected through a census sampling technique. Participants were required to have a valid Pakistan Medical & Dental Council (PMDC) registration and willingness to participate, while those unwilling or unavailable were excluded.Results: Among the 475 participants, 49.3% had low knowledge, 48% moderate, and 2.7% high. Faculty members demonstrated greater knowledge compared to house officers and trainees. Most participants agreed that AI enhances efficiency and quality in dentistry and should be incorporated into the curriculum. Ethical and data privacy concerns were more pronounced among younger participants. Interest in learning more about AI was high, particularly among house officers. Perceptions varied significantly by designation and age, but not by gender.Conclusion: Although knowledge of AI in dentistry remains limited, attitudes toward its integration are largely positive. Bridging knowledge gaps and addressing ethical challenges are essential for the effective and responsible incorporation of AI into dental education and clinical practice.

Today, when digital transformation affects the field of social work, the integration of artificial intelligence technologies into professional practices gains importance. This study examines social work students' views on artificial intelligence with a phenomenological approach. The aim of the study is to determine students' perceptions of artificial intelligence, its potential role in professional practices and their concerns about ethics. The study included 14 third and fourth year social work students living in Bandırma. A semi-structured interview form was used as a data collection tool, and in-depth interviews lasted between 30-45 minutes. The analyses were carried out by thematic analysis method using MAXQDA software. In the findings, 8 sub-themes and 27 codes were identified under 3 main themes: 'Perceptions of Artificial Intelligence', 'Intersection of Social Work and Artificial Intelligence' and 'Artificial Intelligence Education and Access'. While students emphasise the benefits of artificial intelligence such as making life easier (f=10) and fast access (f=8), they have concerns about lack of empathy (f=8), privacy and data security (f=8). In addition, the integration of artificial intelligence into the curriculum (f=9) emerged as an important need. The results show that artificial intelligence in social work education should be positioned in line with professional values.

The integrations of Artificial Intelligence (AI) have generated tools that enhance the efficacy of various healthcare processes. AI-driven technology enables an accurate and early detection of diseases, personalizes treatment plans according to individual patient data, and streamlines administrative tasks. However, the study on the knowledge and perceptions among healthcare professionals in Malaysia was limited. Thus, this study was conducted to assess the knowledge and perceptions of AI in healthcare among healthcare professionals in a private setting in Malaysia. A cross-sectional study was conducted on 198 healthcare professionals in a private healthcare setting in Negeri Sembilan, from September 2023 until December 2023. A validated questionnaire comprised of demographic data and knowledge of AI was used. Descriptive analysis and Chi-square were used to analyze the data. Overall, most of the respondents (69.7%) had poor knowledge of AI with only 49.0% of the respondents defining AI as the use of computer algorithms to perform tasks that require human intelligence. Besides, the majority of the respondents (61.6%) are aware of the integrations of AI in the healthcare system. The majority of respondents (79.8%) have not yet utilized AI and lack experience in integrating AI into their daily work routines, however, the majority expressed a readiness to learn and incorporate AI into their professional practice (79.8%). Overall, the perceptions of AI among the respondents were moderate (70.2%). Hence, it is imperative to implement focused educational and training initiatives to tackle the diverse perspectives on Artificial Intelligence (AI) among healthcare professionals. Furthermore, it is crucial to thoroughly review and enhance the guidelines and policies regarding AI on the ethical framework in the hospital to ensure that AI is used ethically among healthcare professionals.

Background: Artificial Intelligence (AI), recognized as the fourth industrial revolution, is revolutionizing medical practice through enhanced diagnostics, treatment planning, and healthcare delivery. Its expanded role post-COVID-19 underscores the urgency of AI literacy among future physicians. This study aimed to explore medical students’ perceptions of AI in medical education. Method: A cross-sectional study was conducted from June to July 2024 at CMH and AIMC medical colleges in Pakistan. Using a non-probability convenience sampling technique, an online questionnaire was distributed among MBBS students from both public and private institutions. A minimum of 10% of students from each academic year were invited, yielding a final sample of 139 respondents (response rate: 92%). Associations between categorical variables were analyzed using the chi-square test. Results: Among participants, 57.2% were female and 42.8% male. Most students (96.4%) agreed that computers aid learning, and 93.5% believed AI will be essential in future medical practice. Additionally, 77% felt AI would improve medical education, while 87.7% expressed willingness to receive AI-based instruction. A majority (89.1%) supported the inclusion of AI training in the curriculum, and 83.5% believed AI knowledge would benefit their careers. Female students were significantly more likely to support curriculum integration (p = 0.03) and AI instruction (p = 0.04), though no significant gender differences were observed in perceptions of AI’s future role (p = 0.12). Conclusion: Medical students exhibited highly positive attitudes toward AI. While they do not view AI as replacing physicians, they recognize its transformative role. Integrating AI education into MBBS curricula is therefore essential.

The study aimed to assess teachers' perceptions of artificial intelligence (AI), their understanding of its potential advantages and disadvantages, and the specific training needs necessary for successful AI integration. The study aimed to assess teachers' perceptions of artificial intelligence (AI), their understanding of its potential advantages and disadvantages, and the specific training needs necessary for successful AI integration. A mixed-method research design was used in collecting and analyzing quantitative and qualitative data. Descriptive statistics was utilized to delineate teachers' perspectives on AI in education. Open-ended questions were employed to gather insights into the challenges teachers encounter when using AI in the classroom. The findings revealed that teachers' experiences with AI present opportunities and challenges in integrating technology into the classroom. While teachers expressed optimism about AI's potential to enhance education, citing benefits such as personalized learning, educator support, and administrative efficiency, they also voiced concerns about increased workload, excessive student reliance on technology, algorithmic bias within AI systems, and data privacy. A balanced approach to AI integration is recommended not only that complements traditional teaching methods and addresses privacy and algorithmic bias. Schools are encouraged to offer comprehensive professional development programs for teachers to introduce AI technologies, address the ethical and practical challenges associated with their use in education, ensure equitable access to AI tools, and improve technological infrastructure. Keywords: Artificial Intelligence, Capacity building ,Educators, Education, private schools,

Artificial intelligence (AI) has led to the development of various opportunities during the COVID-19 pandemic. An abundant number of applications have surfaced responding to the pandemic, while some other applications were futile. The present study aimed to assess the perception and opportunities of AI used during the COVID-19 pandemic and to explore the perception of medical data analysts about the inclusion of AI in medical education. This study adopted a mixed-method research design conducted among medical doctors for the quantitative part while including medical data analysts for the qualitative interview. The study reveals that nearly 64.8% of professionals were working in high COVID-19 patient-load settings and had significantly more acceptance of AI tools compared to others (P < 0.05). The learning barrier like engaging in new skills and working under a non-medical hierarchy led to dissatisfaction among medical data analysts. There was widespread recognition of their work after the COVID-19 pandemic. Notwithstanding that the majority of professionals are aware that public health emergency creates a significant strain on doctors, the majority still have to work in extremely high case load setting to demand solutions. AI applications are still not being integrated into medicine as fast as technology has been advancing. Sensitization workshops can be conducted among specialists to develop interest which will encourage them to identify problem statements in their fields, and along with AI experts, they can create AI-enabled algorithms to address the problems. A lack of educational opportunities about AI in formal medical curriculum was identified.

Artificial intelligence (AI) is gaining recognition for its ability to enhance patient outcomes in healthcare. Therefore, integrating AI into the undergraduate curriculum is essential to equip students with foundational knowledge before graduation. This study investigated the knowledge and perceptions of AI education among undergraduate healthcare students. This cross-sectional study targeted students from various health colleges in Saudi Arabia and was conducted from January 1, 2024, to April 30, 2024. Data were collected using a validated questionnaire comprising three sections: demographics, knowledge, and perceptions of AI. We utilized a convenience sampling technique to distribute the survey. Descriptive analysis was performed to assess the baseline characteristics, and a multivariable logistic regression model was used to identify the covariates that significantly influence knowledge scores. A total of 303 students participated in our study, including 142 pharmacy and 161 non-pharmacy students. Non-pharmacy students demonstrated significantly greater knowledge of AI than pharmacy students (p = 0.0084). Positive perceptions of AI were more prevalent than negative perceptions among all participants (p = 0.0062). Pharmacy and non-pharmacy students held significantly different knowledge of advanced AI skills (p = 0.0019) and the importance of AI applications (p = 0.0259). Additionally, there were significant differences in perceptions regarding the belief that AI-powered medical devices would help healthcare providers reduce medical errors (p = 0.0279). Healthcare students recognize the benefit of AI in enhancing patient outcomes but lack practical knowledge of its medical applications. They believe integrating AI education into their curriculum is essential for acquiring skills before graduation. Therefore, enhancing AI training may foster innovation, enhance patient outcomes, and align with the evolving demands of the healthcare sector.

The rapid integration of artificial intelligence (AI) technologies into academic and professional practice has profoundly shaped the communication and public relations domain. However, there is limited empirical understanding of how individuals actually perceive and use these new technologies. Therefore, this article investigates Communication and Public Relations (PR) students’ perceptions of artificial intelligence (AI), specifically aiming to understand how literacy, psychological factors, and trust influence their AI word-of-mouth (WOM). We proposed a theoretical model, which we tested using a quantitative approach with SmartPLS analysis on data gathered from 402 online questionnaires administered to students across three major Romanian universities. The key findings indicate that Internet use fosters AI literacy, which subsequently enhances both AI self-efficacy and the perceived ease of AI use. Crucially, higher AI literacy leads to greater trust, promoting informed choices. While AI self-efficacy encourages appropriate reliance on the technology and positively impacts behavioral intention toward AI, higher trust surprisingly leads to lower privacy concerns. Conversely, heightened privacy concerns increase algorithm aversion, which in turn negatively impacts both behavioral intention and WOM communication about AI. We also confirm that a positive behavioral intention is a strong predictor of increased WOM communication. These findings have significant implications for academia, policymakers, and PR practitioners by highlighting the necessity of boosting AI literacy to mitigate aversion and foster responsible AI adoption among future communication professionals.

ObjectiveThis survey aims to understand frontline healthcare professionals’ perceptions of artificial intelligence (AI) in healthcare and assess how AI familiarity influences these perceptions.Materials and MethodsWe conducted a survey from February to March 2023 of physicians and physician assistants registered with the Kansas State Board of Healing Arts. Participants rated their perceptions toward AI-related domains and constructs on a 5-point Likert scale, with higher scores indicating stronger agreement. Two sub-groups were created for analysis to assess the impact of participants’ familiarity and experience with AI on the survey results.ResultsFrom 532 respondents, key concerns were Perceived Communication Barriers (median = 4.0, IQR = 2.8-4.8), Unregulated Standards (median = 4.0, IQR = 3.6-4.8), and Liability Issues (median = 4.0, IQR = 3.5-4.8). Lower levels of agreement were noted for Trust in AI Mechanisms (median = 3.0, IQR = 2.2-3.4), Perceived Risks of AI (median = 3.2, IQR = 2.6-4.0), and Privacy Concerns (median = 3.3, IQR = 2.3-4.0). Positive correlations existed between Intention to use AI and Perceived Benefits (r = 0.825) and Trust in AI Mechanisms (r = 0.777). Perceived risk negatively correlated with Intention to Use AI (r = −0.718). There was no difference in perceptions between AI experienced and AI naïve subgroups.DiscussionThe findings suggest that perceptions of benefits, trust, risks, communication barriers, regulation, and liability issues influence healthcare professionals’ intention to use AI, regardless of their AI familiarity.ConclusionThe study highlights key factors affecting AI adoption in healthcare from the frontline healthcare professionals’ perspective. These insights can guide strategies for successful AI implementation in healthcare.

Educators have called for training in artificial intelligence (AI) in medical education given its certain impact on the future of healthcare. However, there is no consensus regarding how to introduce AI into medical education and little is known about how AI is viewed among medical trainees. In an effort to inform the development of medical education curricula on AI, this study explores perceptions of resident physicians regarding AI in healthcare and its possible impact on their future practice. The authors conducted focus groups with resident physicians across multiple specialties in 2018-2019. Residents were invited to voluntarily participate during pre-existing conference times. Interview transcripts were coded iteratively, and coded data was clustered into categories and themes to capture resident perceptions on AI. Fifty-six residents from emergency medicine, internal medicine, pathology, pediatrics, and radiology participated in six separate focus groups. Conversations generated the following five overarching themes: healthcare is transforming, AI has a role at the clinical and systems level, concern for lack of agency in the development and implementation of AI, AI presents potential harms and uncertainties, and enduring roles of the physician: humanism, judgment, and responsibility. Residents described humanistic roles that should not be replaced by technology and voiced concerns that physicians lack agency to influence how AI will be used in healthcare. Medical education should explore humanistic and ethical challenges related to AI, provide a foundational understanding of AI technology, and offer opportunities for participation in the development of AI technology when possible.

The rise of artificial intelligence (AI), particularly ChatGPT, has transformed educational landscapes globally. Moreover, the Beijing Consensus on Artificial Intelligence and Education and the ‘Pact for the Future’ propose that AI can support UNESCO in achieving development goals, especially focusing on SDG 4, which emphasizes quality education. Thus, this study investigates undergraduate students’ familiarity with and attitudes toward AI tools, as well as their perceived risks and benefits of using AI tools at a private university in China. An explanatory sequential mixed-method design was employed with an online survey of 167 students, followed by a qualitative analysis of open-ended responses. Data were analyzed using the one-sample Wilcoxon signed-rank test and thematic analysis, supported by SPSS and ATLAS.ti 25. The findings revealed that students demonstrated moderate familiarity with AI tools, particularly ChatGPT and willingness to use them in coursework. Positive attitudes toward AI’s value in education were evident, although concerns such as dependence and reduced independent thinking, algorithmic bias and ethical concerns, accuracy and information quality, data security, and privacy concerns were observed among students. Moreover, students generally viewed AI positively and perceived AI integration as inevitable and becoming common in academic settings. Students were concerned that the misuse of AI by their teachers was minimal and trusted their teachers to use AI effectively in teaching. Students also perceived AI’s benefits, such as personalized learning, efficiency and convenience, career and skill development, and support for independent learning. This study contributes to the discourse on AI integration in higher education by highlighting students’ nuanced perceptions and balancing their benefits with potential risks. The findings of this study were limited by the small sample size and institution. Future research should explore diverse contexts to develop comprehensive AI implementation frameworks for higher education.

Background: The rapid advancement of artificial intelligence (AI) in various sectors has revolutionized problem-solving approaches, particularly in healthcare. Developed countries have invested significantly in AI research and applications in healthcare, while low-income countries such as Somalia lag due to various challenges. This study aimed to assess the knowledge, attitudes, and practices (KAP) of AI among healthcare professionals in Somalia and explore their familiarity with AI technologies and practices. Methods: A cross-sectional study was conducted from January 1, 2024, to March 15, 2024, among 441 healthcare professionals in Somalia, using an online questionnaire. The questionnaire assessed the participants' sociodemographic information, knowledge of AI applications in healthcare, attitudes towards AI capabilities, and practical experience with AI in healthcare. Results: Most participants demonstrated good knowledge of AI (67.6%) and a positive attitude towards its potential in healthcare (80.5%). However, a significant gap was observed in the practical application of AI, with 79.1% of the respondents reporting poor practice. The study also found that sociodemographic factors such as age, gender, and income level did not significantly influence knowledge or attitudes towards AI but did affect its practical use. Professionals in certain fields such as midwifery and public health are more likely to use AI in their work. Knowledge and attitude scores were also significant predictors of practice scores. Conclusion: Healthcare professionals in Somalia demonstrate a good understanding and positive attitudes towards AI but encounter challenges in its practical application. This study emphasizes the necessity of an enhanced infrastructure, technical expertise, and data access to fully utilize AI's potential in healthcare. It also highlights the significance of addressing ethical considerations and implementing regulations to ensure responsible use of AI in healthcare. Efforts are needed to translate awareness and receptiveness into effective practice, which could result in a better healthcare system.

This paper investigated GP perception of artificial intelligence in relation to symptom checking, and specifically whether GPs view artificial intelligence as an opportunity or a threat. The authors advocate for a sustained collaboration between GPs and artificial intelligence as the way forward for patient and societal benefit. A collaborative approach requires broad-level adoption of artificial intelligence-enabled applications to complement rather than replace a GP's own expertise. Drawing on extant literature, this study investigated how measures of self-efficacy, being an individual's ability to believe in their own ability to organise and implement courses of action, influence the perception of artificial intelligence as an opportunity rather than as a threat. Prior work suggested that higher measures of self-efficacy would correlate with the view that artificial intelligence is an opportunity. In this work, 110 GPs from the UK were invited to be surveyed via a structured questionnaire about perceived self-efficacy and view of artificial intelligence. Of these, 26 GPs agreed to participate, giving a response rate of 24%. The results gave preliminary evidence that higher levels of perceived self-efficacy were associated with greater perceptions of artificial intelligence either as an opportunity or as a threat. This finding offers a new perspective for policy makers, leaders and academics, who are looking for predictors of artificial intelligence engagement. This work may form the basis for further research on the potential causal relationship between self-efficacy and AI adoption, which could ultimately help facilitate artificial intelligence adoption.

This study explored university students’ perceptions of artificial intelligence (AI) literacy and AI education. Specifically, the authors sought to understand the level of AI literacy among university students, the extent of their prior exposure to AI education, and the factors that affect their AI education. The study was conducted through an online survey distributed among 300 university students at a Korean university. The study findings indicate that most university students are interested in learning AI. Moreover, the results showed that students with prior experience with software (SW) education showed superior knowledge, a better grasp of AI concepts, and more confidence in using AI technologies, especially in computer coding skills. On the other hand, students with less exposure to prior AI education expressed a need for more learning opportunities and sufficient knowledge of computer coding skills. In addition, we found that students who had received SW education during their K-12 schooling showed better math skills than those who had only received short-term training in university. These findings underscore the importance of addressing the educational barriers that impede university students’ ability to harness this interest effectively. In conclusion, our study provides valuable insights into university students’ perceptions regarding AI literacy and the factors that affect their AI education.

IntroductionRadiographer reporting is accepted practice in the UK. With a national shortage of radiographers and radiologists, artificial intelligence (AI) support in reporting may help minimise the backlog of unreported images. Modern AI is not well understood by human end-users. This may have ethical implications and impact human trust in these systems, due to over- and under-reliance. This study investigates the perceptions of reporting radiographers about AI, gathers information to explain how they may interact with AI in future and identifies features perceived as necessary for appropriate trust in these systems. MethodsA Qualtrics® survey was designed and piloted by a team of UK AI expert radiographers. This paper reports the third part of the survey, open to reporting radiographers only. Results86 responses were received. Respondents were confident in how an AI reached its decision (n = 53, 62%). Less than a third of respondents would be confident communicating the AI decision to stakeholders. Affirmation from AI would improve confidence (n = 49, 57%) and disagreement would make respondents seek a second opinion (n = 60, 70%). There is a moderate trust level in AI for image interpretation. System performance data and AI visual explanations would increase trust. ConclusionsResponses indicate that AI will have a strong impact on reporting radiographers’ decision making in the future. Respondents are confident in how an AI makes decisions but less confident explaining this to others. Trust levels could be improved with explainable AI solutions. Implications for practiceThis survey clarifies UK reporting radiographers’ perceptions of AI, used for image interpretation, highlighting key issues with AI integration.