Perception Of Artificial Intelligence Research Articles

Artificial intelligence chatbots and large language models in dental education: Worldwide survey of educators.

Interest is growing in the potential of artificial intelligence (AI) chatbots and large language models like OpenAI's ChatGPT and Google's Gemini, particularly in dental education. To explore dental educators' perceptions of AI chatbots and large language models, specifically their potential benefits and challenges for dental education. A global cross-sectional survey was conducted in May-June 2023 using a 31-item online-questionnaire to assess dental educators' perceptions of AI chatbots like ChatGPT and their influence on dental education. Dental educators, representing diverse backgrounds, were asked about their use of AI, its perceived impact, barriers to using chatbots, and the future role of AI in this field. 428 dental educators (survey views = 1516; response rate = 28%) with a median [25/75th percentiles] age of 45 [37, 56] and 16 [8, 25] years of experience participated, with the majority from the Americas (54%), followed by Europe (26%) and Asia (10%). Thirty-one percent of respondents already use AI tools, with 64% recognising their potential in dental education. Perception of AI's potential impact on dental education varied by region, with Africa (4[4-5]), Asia (4[4-5]), and the Americas (4[3-5]) perceiving more potential than Europe (3[3-4]). Educators stated that AI chatbots could enhance knowledge acquisition (74.3%), research (68.5%), and clinical decision-making (63.6%) but expressed concern about AI's potential to reduce human interaction (53.9%). Dental educators' chief concerns centred around the absence of clear guidelines and training for using AI chatbots. A positive yet cautious view towards AI chatbot integration in dental curricula is prevalent, underscoring the need for clear implementation guidelines.

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

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. A cross-sectional descriptive study was conducted at Batterjee Medical College (BMC), Jeddah (KSA), from November 2022 to April 2023. 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%. 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.

Perceptiveness and Attitude on the use of Artificial Intelligence (AI) in Dentistry among Dentists and Non-Dentists - A Regional Survey

ABSTRACT Artificial intelligence (AI) is an emerging tool in modern medicine and the digital world. AI can help dentists diagnose oral diseases, design treatment plans, monitor patient progress and automate administrative tasks. The aim of this study is to evaluate the perception and attitude on use of artificial intelligence in dentistry for diagnosis and treatment planning among dentists and non-dentists’ population of south Tamil Nadu region in India. Materials and Methods: A cross sectional online survey conducted using 20 close ended questionnaire google forms which were circulated among the dentists and non -dentists population of south Tamil Nadu region in India. The data collected from 264 participants (dentists -158, non-dentists -106) within a limited time frame were subjected to descriptive statistical analysis. Results: 70.9% of dentists are aware of artificial intelligence in dentistry. 40.5% participants were not aware of AI in caries detection but aware of its use in interpretation of radiographs (43.9%) and in planning of orthognathic surgery (42.4%) which are statistically significant P < 0.05.44.7% support clinical experience of a human doctor better than AI diagnosis. Dentists of 54.4% agree to support AI use in dentistry. Conclusion: The study concluded AI use in dentistry knowledge is more with dentists and perception of AI in dentistry is optimistic among dentists than non -dentists, majority of participants support AI in dentistry as an adjunct tool to diagnosis and treatment planning.

AI and Auditing: Enhancing Audit Efficiency and Effectiveness with Artificial Intelligence

The use of automation and artificial intelligence (AI) in audit practice is increasingly becoming a major focus, with significant impact on the profession. This research depicts the current landscape of the use of AI in auditing, highlighting aspects such as automation and empowerment of the workforce in auditing, impact of AI on improving audit quality criteria, key factors in adopting AI-based audit techniques, impact of AI technology on audit evidence , and auditors' perceptions of AI in improving audit quality. The results and discussion show that while there are great benefits from integrating automation and AI in auditing, including improved audit quality, enhanced efficiency, and the ability to perform continuous audits, there are also challenges that need to be overcome, such as high customization costs for specific audit processes industry. The use of AI in auditing requires adaptation from auditors to changes in competencies and workflows to effectively utilize this technology. However, with proper understanding and careful handling of these challenges, AI has great potential to improve overall audit practices.

인공지능시스템에 대한 호텔 종업원의 양가적(兩價的) 인식과 사용자 저항의 영향 관계

Utilizing the job demands-resources model, this study investigated the impact of hotel employees' perceptions of artificial intelligence(AI) systems (job demands) on user resistance, as mediated by switching costs (stress factors) and switching benefits (motivational factors). Additionally, it sought to explore how these relationships are moderated by the level of organizational support (job resources). To achieve the research objectives, an online survey was conducted targeting hotel employees who experienced AI systems in their work. The research findings are as follows: Firstly, employees' AI perceptions had a positive impact on switching costs but did not significantly affect switching benefits. Secondly, switching costs had a positive influence on user resistance, while switching benefits negatively impacted user resistance. Thirdly, the level of organizational support moderated the relationships between AI perceptions, switching costs, and switching benefits. These study findings contribute to deepening the understanding of hotel employees' resistance to AI systems. It confirms the significant role of the level of organizational support when introducing AI systems. Consequently, this study suggests theoretical and practical implications for developing effective employee resource management strategies for hotel organizations that have introduced or plan to use extended AI systems.

Artificial Intelligence in Healthcare: A Study of Physician Attitudes and Perceptions in Jeddah, Saudi Arabia.

Background Artificial intelligence (AI) in healthcare is rapidly advancing, reshaping diagnostic, prognostic, and operational tasks in healthcare institutions. The adoption of AI among physicians is varied, with concerns over job loss, medical errors, and lack of emotional intelligence. This study aimed to assess physicians' attitudes and perceptions toward AI in clinical practice in Jeddah, Saudi Arabia, and the factors affecting these attitudes and perceptions. Methodology A cross-sectional study was conducted among physicians at two major hospitals in Jeddah. An in-person digital survey consisted of questions regarding demographic characteristics, attitudes toward clinical AI, and perceptions of AI's impact on healthcare. Results Of the 205 participants, 76% agreed on the accuracy of AI systems, and 60% acknowledged their efficiency as a factor that could influence their willingness to use clinical AI. However, only 25.9% reported using these systems in the past year, with the majority, 74.1%, indicating they had never used them. Notably, there was a significant association between gender and attitude toward AI, with males being more likely to have a positive attitude (p = 0.01). Conclusions While the majority of participants recognized the potential benefits of AI in healthcare, its actual utilization was low. The findings suggest the need for increased AI-related training and education among physicians and the fostering of collaboration between computer scientists, engineers, and medical professionals to accelerate the development of clinically relevant AI tools.

Patients' Perceptions and Attitudes to the Use of Artificial Intelligence in Breast Cancer Diagnosis: A Narrative Review.

Breast cancer remains the most prevalent cancer among women worldwide, necessitating advancements in diagnostic methods. The integration of artificial intelligence (AI) into mammography has shown promise in enhancing diagnostic accuracy. However, understanding patient perspectives, particularly considering the psychological impact of breast cancer diagnoses, is crucial. This narrative review synthesizes literature from 2000 to 2023 to examine breast cancer patients' attitudes towards AI in breast imaging, focusing on trust, acceptance, and demographic influences on these views. Methodologically, we employed a systematic literature search across databases such as PubMed, Embase, Medline, and Scopus, selecting studies that provided insights into patients' perceptions of AI in diagnostics. Our review included a sample of seven key studies after rigorous screening, reflecting varied patient trust and acceptance levels towards AI. Overall, we found a clear preference among patients for AI to augment rather than replace the diagnostic process, emphasizing the necessity of radiologists' expertise in conjunction with AI to enhance decision-making accuracy. This paper highlights the importance of aligning AI implementation in clinical settings with patient needs and expectations, emphasizing the need for human interaction in healthcare. Our findings advocate for a model where AI augments the diagnostic process, underlining the necessity for educational efforts to mitigate concerns and enhance patient trust in AI-enhanced diagnostics.

Artificial intelligence-assisted full-mouth radiograph mounting in dental education.

With the increasing prevalence of artificial intelligence (AI) and the significant research gap in the application of AI within dentistry, this study aimed to (1) evaluate the efficiency and accuracy of dental students in full-mouth radiograph series (FMS) mounting with and without AI assistance, and (2) assess dental students' perceptions of AI in clinical education to address the impact of AI in dental education. An AI-based interface for mounting radiographs on FMS templates was designed and implemented in the study. Forty third-year dental students were randomly assigned to control and test groups. The control group manually mounted FMS radiographs, while the test group reviewed AI-pre-mounted radiographs for adjustments. Students' performance in efficiency and accuracy was evaluated. Pre- and post-study surveys were conducted to gauge students' confidence levels and opinions regarding the usefulness of the AI-assisted program. The test group (using AI) demonstrated significantly faster radiograph mounting times than the control group (manual) (p<0.05). Accuracy was lower in the test groups, when comparing AI-assisted and manual mounting of FMS (p<0.01). Self-confidence and confidence in AI were consistent between the control and test groups, both before and after the study. Students with AI presented with a decreased accuracy in FMS radiograph mounting. Therefore, AI automation could potentially have negative impacts in a learning environment with inexperienced clinicians.

Artificial Intelligence in Health Care – A Study on Perceptions of and Readiness for Artificial Intelligence in Health-care Professionals

Abstract Background: With a call to action from the health-care industry and the Indian government, there are significant gaps in health-care professionals’ uptake and utilization of artificial intelligence (AI)-based tools. This study attempts to explore the current perceptions and readiness for AI among health-care workers. Methods: A web-based questionnaire comprising seven sections on descriptive educational and occupational data, AI familiarity level, role-specific training benefits, training advantages, implementation issues, driving factors, and perceived risks was designed from a literature search. Two additional domains of perception on professional impact and preparedness for AI in health care were estimated using a prevalidated Shinners AI Perception tool. Results: Of the 402 study participants, 192 (47.9%) were doctors from diverse specializations, and the remaining 209 (52.1%) were undergraduate medical and nursing students and affiliated health professionals. Although 79.8% of participants had never attended a course on AI, 82% agreed on the need for training in AI to explore new opportunities in their respective fields. 72.1% of participants agreed that data privacy and confidentiality posed the most significant challenge to AI implementation among the studied factors. Conclusion: This survey reveals awareness regarding AI, which is attributable to a lack of formal training received by health-care professionals. Most participants believed that AI could improve population health outcomes, and collective efforts are needed to make this belief a reality.

Nurses’ perceptions, experience and knowledge regarding artificial intelligence: results from a cross-sectional online survey in Germany

BackgroundNursing faces increasing pressure due to changing demographics and a shortage of skilled workers. Artificial intelligence (AI) offers an opportunity to relieve nurses and reduce pressure. The perception of AI by nurses is crucial for successful implementation. Due to a limited research state, our study aims to investigate nurses’ knowledge and perceptions of AI.MethodsIn June 2023, we conducted a cross-sectional online survey of nurses in Bavaria, Germany. A convenience sample via care facilities was used for the questionnaire oriented on existing AI surveys. Data analysis was performed descriptively, and we used a template analysis to evaluate free-text answers.Results114 (♀67.5 %, ♂32.5 %) nurses participated. Results show that knowledge about AI is limited, as only 25.2 % can be described as AI experts. German nurses strongly associate AI with (i) computers and hardware, (ii) programming-based software, (iii) a database tool, (iv) learning, and (v) making decisions. Two-thirds of nurses report AI as an opportunity. Concerns arise as AI is seen as uncontrollable or threat. Administration staff are seen as the biggest profiteers.ConclusionEven though there is a lack of clear understanding of AI technology among nurses, the majority recognizes the benefits that AI can bring in terms of relief or support. We suggest that nurses should be better prepared for AI in the future, e.g., through training and continuing education measures. Nurses are the working group that uses AI and are crucial for implementing nursing AI.

Ascribing consciousness to artificial intelligence: human-AI interaction and its carry-over effects on human-human interaction.

The question of whether artificial intelligence (AI) can be considered conscious and therefore should be evaluated through a moral lens has surfaced in recent years. In this paper, we argue that whether AI is conscious is less of a concern than the fact that AI can be considered conscious by users during human-AI interaction, because this ascription of consciousness can lead to carry-over effects on human-human interaction. When AI is viewed as conscious like a human, then how people treat AI appears to carry over into how they treat other people due to activating schemas that are congruent to those activated during interactions with humans. In light of this potential, we might consider regulating how we treat AI, or how we build AI to evoke certain kinds of treatment from users, but not because AI is inherently sentient. This argument focuses on humanlike, social actor AI such as chatbots, digital voice assistants, and social robots. In the first part of the paper, we provide evidence for carry-over effects between perceptions of AI consciousness and behavior toward humans through literature on human-computer interaction, human-AI interaction, and the psychology of artificial agents. In the second part of the paper, we detail how the mechanism of schema activation can allow us to test consciousness perception as a driver of carry-over effects between human-AI interaction and human-human interaction. In essence, perceiving AI as conscious like a human, thereby activating congruent mind schemas during interaction, is a driver for behaviors and perceptions of AI that can carry over into how we treat humans. Therefore, the fact that people can ascribe humanlike consciousness to AI is worth considering, and moral protection for AI is also worth considering, regardless of AI's inherent conscious or moral status.

Assessing the Impact of AI Education on Hispanic Healthcare Professionals' Perceptions and Knowledge.

This study investigates the awareness and perceptions of artificial intelligence (AI) among Hispanic healthcare-related professionals, focusing on integrating AI in healthcare. The study participants were recruited from an asynchronous course offered twice within a year at the University of Puerto Rico Medical Science Campus, titled "Artificial Intelligence and Machine Learning Applied to Health Disparities Research", which aimed to bridge the gaps in AI knowledge among participants. The participants were divided into Experimental (n = 32; data-illiterate) and Control (n = 18; data-literate) groups, and pre-test and post-test surveys were administered to assess knowledge and attitudes toward AI. Descriptive statistics, power analysis, and the Mann-Whitney U test were employed to determine the influence of the course on participants' comprehension and perspectives regarding AI. Results indicate significant improvements in knowledge and attitudes among participants, emphasizing the effectiveness of the course in enhancing understanding and fostering positive attitudes toward AI. Findings also reveal limited practical exposure to AI applications, highlighting the need for improved integration into education. This research highlights the significance of educating healthcare professionals about AI to enable its advantageous incorporation into healthcare procedures. The study provides valuable perspectives from a broad spectrum of healthcare workers, serving as a basis for future investigations and educational endeavors aimed at AI implementation in healthcare.

Artificial Intelligence in the Eyes of Society: Assessing Social Risk and Social Value Perception in a Novel Classification

Artificial intelligence (AI) is a rapidly developing technology that has the potential to create previously unimaginable chances for our societies. Still, the public’s opinion of AI remains mixed. Since AI has been integrated into many facets of daily life, it is critical to understand how people perceive these systems. The present work investigated the perceived social risk and social value of AI. In a preliminary study, AI’s social risk and social value were first operationalized and explored by adopting a correlational approach. Results highlighted that perceived social value and social risk represent two significant and antagonistic dimensions driving the perception of AI: the higher the perceived risk, the lower the social value attributed to AI. The main study considered pretested AI applications in different domains to develop a classification of AI applications based on perceived social risk and social value. A cluster analysis revealed that in the two-dimensional social risk × social value space, the considered AI technologies grouped into six clusters, with the AI applications related to medical care (e.g., assisted surgery) unexpectedly perceived as the riskiest ones. Understanding people’s perceptions of AI can guide researchers, developers, and policymakers in adopting an anthropocentric approach when designing future AI technologies to prioritize human well-being and ensure AI’s responsible and ethical development in the years to come.

Empowering Radiographers: A Call for Integrated AI Training in University Curricula.

Artificial intelligence (AI) applications are rapidly advancing in the field of medical imaging. This study is aimed at investigating the perception and knowledge of radiographers towards artificial intelligence. An online survey employing Google Forms consisting of 20 questions regarding the radiographers' perception of AI. The questionnaire was divided into two parts. The first part consisted of demographic information as well as whether the participants think AI should be part of medical training, their previous knowledge of the technologies used in AI, and whether they prefer to receive training on AI. The second part of the questionnaire consisted of two fields. The first one consisted of 16 questions regarding radiographers' perception of AI applications in radiology. Descriptive analysis and logistic regression analysis were used to evaluate the effect of gender on the items of the questionnaire. Familiarity with AI was low, with only 52 out of 100 respondents (52%) reporting good familiarity with AI. Many participants considered AI useful in the medical field (74%). The findings of the study demonstrate that nearly most of the participants (98%) believed that AI should be integrated into university education, with 87% of the respondents preferring to receive training on AI, with some already having prior knowledge of AI used in technologies. The logistic regression analysis indicated a significant association between male gender and experience within the range of 23-27 years with the degree of familiarity with AI technology, exhibiting respective odds ratios of 1.89 (COR = 1.89) and 1.87 (COR = 1.87). This study suggests that medical practices have a favorable attitude towards AI in the radiology field. Most participants surveyed believed that AI should be part of radiography education. AI training programs for undergraduate and postgraduate radiographers may be necessary to prepare them for AI tools in radiology development.

High Schoolers’ Perceptions of Artificial Intelligence in Medicine

Artificial Intelligence (AI) is a rising technology with endless benefits and applications especially in the field of medicine. Unfortunately, ethical issues such as accountability, privacy, safety, security, and transparency have created a divide between the public and AI. A key population to understand is high school students as they will be affected more and more as AI continues to be developed. This begs the question: What are American high school students’ perceptions of artificial intelligence in medicine? The mixed method approach for this study used a survey aligned with Aggarwal et al. (2021) to gather quantitative results about student opinions on AI. Structure interviews aligned with Amann et al. (2023) were also conducted to find underlying themes between participants’ opinions. There were a total of 153 participants in this study. The students had self-reported low levels of understanding of AI as 44.44% disagreed with the statement that they had knowledge of AI technology in medicine while 30.72% were neutral. Additionally, only 23.53% of students trusted AI which contradicted the fact that students have a great amount of exposure to new technologies. There were significant differences in the levels of knowledge and trust in AI that high school students had in this study. A need for education on AI and its use in medicine as well as other sectors may be beneficial in closing the gap between the public and AI.

Exploring the Relationship between the Coverage of AI in WIRED Magazine and Public Opinion Using Sentiment Analysis

The presence and significance of artificial intelligence (AI) technology in society have been steadily increasing since 2000. While its potential benefits are widely acknowledged, concerns about its impact on society, the economy, and ethics have also been raised. Consequently, artificial intelligence has garnered widespread attention in news media and popular culture. As mass media plays a pivotal role in shaping public perception, it is crucial to evaluate opinions expressed in these outlets. Understanding the public’s perception of artificial intelligence is essential for effective public policy and decision making. This paper presents the results of a sentiment analysis study conducted on WIRED magazine’s coverage of artificial intelligence between January 2018 and April 2023. The objective of the study is to assess the prevailing opinions towards artificial intelligence in articles from WIRED magazine, which is widely recognized as one of the most reputable and influential publications in the field of technology and innovation. Using two sentiment analysis techniques, AFINN and VADER, a total of 4265 articles were analyzed for positive, negative, and neutral sentiments. Additionally, a term frequency analysis was conducted to categorize articles based on the frequency of mentions of artificial intelligence. Finally, a linear regression analysis of the mean positive and negative sentiments was performed to examine trends for each month over a five-year period. The results revealed a leading pattern: there was a predominant positive sentiment with an upward trend in both positive and negative sentiments. This polarization of sentiment suggests a shift towards more extreme positions, which should influence public policy and decision making in the near future.

The Impact of Artificial Intelligence on Students in the First Three Grades in Basic Schools in the City of Amman-Jordan from The Perspective of Their Teachers

Nowadays Artificial Intelligence (AI) affects most of our lives; it is positive in some ways, but there are limitations in others. Teachers' perceptions of AI are still poorly studied, despite ongoing debate and growing research in the field. One of its main effects is on students in the first grades; however, this study aimed to investigate the effect of AI on the first three grades of students in basic schools in the governorate of the capital Amman Jordan, from the perspective of its teachers as a case study. The study was performed using a scientific questionnaire, and 125 participants completed this survey. The findings of an analysis of teachers' beliefs regarding using AI technologies to teach the first three grades in elementary schools in Jordan's capital, Amman Governorate, are presented in detail. As a result of the significance of these axes in educational curricula and strategies, as well as related sustainability, the focus was on five axes in the questionnaire questions: the validity of information derived from artificial intelligence, supporting students' technical knowledge and conceptual knowledge, focusing on development practices for students' skills, and finally achieving educational outcomes. The findings show that in-service teachers need to be trained to use current AI-based tools more effectively. To integrate AI into regular education, teachers must participate in the process of co-designing materials while taking into account contextual circumstances and, most importantly, curricula. Teachers' input during the development can help put AI in perspective, resulting in tangible effects and significant educational advances.

Allied health professionals’ perceptions of artificial intelligence in the clinical setting: a cross sectional survey (Preprint)

Allied health professionals’ perceptions of artificial intelligence in the clinical setting: a cross sectional survey (Preprint)

AI for Social Good: Leveraging Artificial Intelligence for Community Development

This research explores the impact of Artificial Intelligence (AI) on community development, spanning healthcare, education, environmental sustainability, and community empowerment. The purpose of this study is to comprehensively analyze the perceptions and experiences of participants in underserved communities regarding AI applications. Employing a mixed-methods approach, quantitative surveys provide statistical insights, complemented by qualitative narratives to capture nuanced perspectives. The methodology involves surveying 120 participants from diverse occupations and age groups, utilizing Likert scales and regression analysis. The results reveal a positive perception of AI across domains, emphasizing its potential for positive societal outcomes. Noteworthy is the statistical significance of AI's impact on healthcare, education, and environmental sustainability. The inclusion of qualitative narratives enriches the findings, providing depth to statistical measures. The novelty of this study lies in its holistic exploration of AI's impact on community development, integrating quantitative and qualitative dimensions. The research contributes to the field by providing nuanced insights into the multifaceted aspects of AI in community contexts. In conclusion, this study underscores the need for responsible AI deployment, aligning with community values, as communities navigate the evolving technological landscape.

I Know This Looks Bad, But I Can Explain: Understanding When AI Should Explain Actions In Human-AI Teams

Explanation of artificial intelligence (AI) decision-making has become an important research area in human–computer interaction (HCI) and computer-supported teamwork research. While plenty of research has investigated AI explanations with an intent to improve AI transparency and human trust in AI, how AI explanations function in teaming environments remains unclear. Given that a major benefit of AI giving explanations is to increase human trust understanding how AI explanations impact human trust is crucial to effective human-AI teamwork. An online experiment was conducted with 156 participants to explore this question by examining how a teammate’s explanations impact the perceived trust of the teammate and the effectiveness of the team and how these impacts vary based on whether the teammate is a human or an AI. This study shows that explanations facilitate trust in AI teammates when explaining why AI disobeyed humans’ orders but hindered trust when explaining why an AI lied to humans. In addition, participants’ personal characteristics (e.g., their gender and the individual’s ethical framework) impacted their perceptions of AI teammates both directly and indirectly in different scenarios. Our study contributes to interactive intelligent systems and HCI by shedding light on how an AI teammate’s actions and corresponding explanations are perceived by humans while identifying factors that impact trust and perceived effectiveness. This work provides an initial understanding of AI explanations in human-AI teams, which can be used for future research to build upon in exploring AI explanation implementation in collaborative environments.