Perceptions Of Intelligence Research Articles

Medical students’ perception of the role of artificial intelligence in healthcare

Introduction. Artificial intelligence is defined as a part of computer science capable of manipulating extensive data through machine learning. The aim of this study is to investigate medical students? perceptions regarding the use of artificial intelligence in the field of healthcare. Material and Methods. This research was conducted as a cross-sectional study using the Computer Assisted Web Interviewing technique for data collection by surveying students through social networks. The sample consists of 160 students who were surveyed in November 2023. The aim was to provide answers to the question of how students perceive the use of new technology - artificial intelligence in the field that represents their future profession. Results. The results have shown a well-developed awareness among students regarding the potential application of artificial intelligence in the medical field, emphasizing a positive perception of the benefits that artificial intelligence can bring. They have also recognized the importance of incorporating artificial intelligence training into medical education. Students have expressed concerns, primarily about potential misuse of artificial intelligence and ethical issues related to its use in medicine. Conclusion. Medical students are aware not only of the benefits but also the risks associated with the implementation of artificial intelligence in medicine.

MASS: Mobility-Aware Sensor Scheduling of Cooperative Perception for Connected Automated Driving

Timely and reliable environment perception is fundamental to safe and efficient automated driving. However, the perception of standalone intelligence inevitably suffers from occlusions. A new paradigm, Cooperative Perception (CP), comes to the rescue by sharing sensor data from another perspective, i.e., from a cooperative vehicle (CoV). Due to the limited communication bandwidth, it is essential to schedule the most beneficial CoV, considering both the viewpoints and communication quality. Existing methods rely on the exchange of meta-information, such as visibility maps, to predict the perception gains from nearby vehicles, which induces extra communication and processing overhead. In this paper, we propose a new approach, learning while scheduling, for distributed scheduling of CP. The solution enables CoVs to predict the perception gains using past observations, leveraging the temporal continuity of perception gains. Specifically, we design a mobility-aware sensor scheduling (MASS) algorithm based on the restless multi-armed bandit (RMAB) theory to maximize the expected average perception gain. An upper bound on the expected average learning regret is proved, which matches the lower bound of any online algorithm up to a logarithmic factor. Extensive simulations are carried out on realistic traffic traces. The results show that the proposed MASS algorithm achieves the best average perception gain and improves recall by up to 3.1 percentage points compared to other learning-based algorithms. Finally, a case study on a trace of LiDAR frames qualitatively demonstrates the superiority of adaptive exploration, the key element of the MASS algorithm.

ОСОБЕННОСТИ ВОСПРИЯТИЯ ПРИВЛЕКАТЕЛЬНОСТИ ЛИЦА ПРИ РАЗНОМ УРОВНЕ ЭМОЦИОНАЛЬНОГО ИНТЕЛЛЕКТА

Background. Many studies have been devoted to the relationship between the perception of facial expression and emotional intelligence (EI). As a rule, the study of this issue involved the tasks of expression recognition with stimuli expressing strong basic emotions. However, diX erences in the success of expression recognition in groups with diX erent levels of EI are not always signiY cantly pronounced. Z is may be connected to the fact, that the recognition of basic expressions is too easy for subjects with both high and low EI scores. In order to conduct a more effective study of this issue, it is necessary to complicate the task of facial expressions recognition. To this end, one of the conY gurational eX ects of E. Brunswick stating that neutral faces with diX erent conY gurations of facial features are perceived as emotionally colored, can be used. Under these conditions, a more adequate task is not to assess the success of expression recognition, but to assess the attractiveness of the face, which is closely related to the perceived facial expression. Z us, faces with positive expressions are more o[ en rated as attractive, and vice versa. It is assumed that the study of the perception of neutral faces will reveal the features of the relationship between the perception of facial expression and EI. Objective. Z e study is meant to explore the relationship between the perception of facial attractiveness and EI. It was assumed that subjects with diX erent levels of EI would evaluate the attractiveness of neutral faces diX erently. Sample. Z e experiment involved 49 people (20 men and 29 women, in the age range of 18–35 years). Methods. Images of faces were presented on a monitor screen. Eye movements were recorded during stimuli presentation. Z e level of EI was assessed based on the results of the “Test of emotional intelligence” (TEI) (Sergienko et al., 2019). We calculated indicators of TEI for all branches of EI (the abilities to recognize, understand, manage, and use emotions), as well as the total score of EI. An analysis of face attractiveness scores as well as eye movement parametres (duration and number of Y xations, duration of saccades) was carried out for two groups of subjects with high and low EI scores.

The Evolution of the Perception of Artificial Intelligence in the EU: The Case of Judicial Administration

The Evolution of the Perception of Artificial Intelligence in the EU: The Case of Judicial Administration

Model Focus Improves Performance of Deep Learning-Based Synthetic Face Detectors

Deep learning-based models generalize better to unknown data samples after being guided “where to look” by incorporating human perception into training strategies. We made an observation that the entropy of the model’s salience trained in that way is lower when compared to salience entropy computed for models training without human perceptual intelligence. The research problem addressed by this paper is whether lowering the entropy of model’s class activation map helps in further increasing the performance, on top of the performance increase we observe for human saliency-based model’s training. In this paper we propose and evaluate four new entropy-based loss functions controlling the model’s focus, covering the full range of the level of such control, from none to its “aggresive” minimization. We show, using a problem of synthetic face detection, that improving the model’s focus, through lowering entropy by the proposed loss components, leads to models that perform better in an open-set scenario (in which the test samples are synthesized by unknown generative models): the obtained average Area Under the ROC curve (AUROC) ranges from 0.72 to 0.78, compared to AUROC = 0.64 observed for a state-of-the-art human-salience-only-based control of the model’s focus. We also show that optimal performance is obtained when the model’s loss function blends three aspects: regular classification performance, low-entropy of the model’s focus, and closeness of the model’s focus to human saliency. The major conclusion from this work is that maximization of the model’s focus is an important regularizer allowing the models to generalize better in an open set scenario. Future work directions include methods of blending classification-, human salience-, and model’s salience entropy-based loss components to achieve optimal performance in other domains than the synthetic face detection.

Knowledge and perception of primary care healthcare professionals on the use of artificial intelligence as a healthcare tool.

The rapid digitisation of healthcare data and the sheer volume being generated means that artificial intelligence (AI) is becoming a new reality in the practice of medicine. For this reason, describing the perception of primary care (PC) healthcare professionals on the use of AI as a healthcare tool and its impact in radiology is crucial to ensure its successful implementation. Observational cross-sectional study, using the validated Shinners Artificial Intelligence Perception survey, aimed at all PC medical and nursing professionals in the health region of Central Catalonia. The survey was sent to 1068 health professionals, of whom 301 responded. And 85.7% indicated that they understood the concept of AI but there were discrepancies in the use of this tool; 65.8% indicated that they had not received any AI training and 91.4% that they would like to receive training. The mean score for the professional impact of AI was 3.62 points out of 5 (standard deviation (SD) = 0.72), with a higher score among practitioners who had some prior knowledge of and interest in AI. The mean score for preparedness for AI was 2.76 points out of 5 (SD = 0.70), with higher scores for nursing and those who use or do not know if they use AI. The results of this study show that the majority of professionals understood the concept of AI, perceived its impact positively, and felt prepared for its implementation. In addition, despite being limited to a diagnostic aid, the implementation of AI in radiology was a high priority for these professionals.

The Relationship between Nursing Students' Smart Devices Addiction and Their Perception of Artificial Intelligence.

Background: The concept of addiction in relation to cellphone and smartphone use is not new, with several researchers already having explored this phenomenon. Artificial intelligence has become important in the rapid development of the technology field in recent years. It has a very positive impact on our day-to-day life. Aim: To investigate the relationship between nursing students’ addiction to smart devices and their perceptions of artificial intelligence. Methods: A cross-sectional design was applied. The data were collected from 697 nursing students over three months at the College of Nursing, Princess Nourah bint Abdulrahman University. Results: The correlation test shows a significant correlation between smart device addiction and the artificial intelligence of the respondents (p-value < 0.05). In addition, the majority of the students, 72.7% (507), are moderately addicted to smartphones, 21.8% (152) are highly addicted, and only 5.5% (38) have a low addiction. Meanwhile, 83.6% (583) of them have high levels of perception of artificial intelligence and the rest, 16.4% (114), have a moderate level. Conclusions: The nursing students’ perception of artificial intelligence varies significantly according to their level of addiction to smart device utilization.

The role of valence, dominance, and pitch in perceptions of artificial intelligence (AI) conversational agents’ voices

There is growing concern that artificial intelligence conversational agents (e.g., Siri, Alexa) reinforce voice-based social stereotypes. Because little is known about social perceptions of conversational agents’ voices, we investigated (1) the dimensions that underpin perceptions of these synthetic voices and (2) the role that acoustic parameters play in these perceptions. Study 1 (N = 504) found that perceptions of synthetic voices are underpinned by Valence and Dominance components similar to those previously reported for natural human stimuli and that the Dominance component was strongly and negatively related to voice pitch. Study 2 (N = 160) found that experimentally manipulating pitch in synthetic voices directly influenced dominance-related, but not valence-related, perceptions. Collectively, these results suggest that greater consideration of the role that voice pitch plays in dominance-related perceptions when designing conversational agents may be an effective method for controlling stereotypic perceptions of their voices and the downstream consequences of those perceptions.

Generation Z’s perception of artificial intelligence used in selection processes

The objective of this study was to identify and analyze the perceptions of Generation Z (born between 1995 and 2009) regarding new practices, processes, and emerging topics in people management, with emphasis on the application of artificial intelligence, since this is a very recent generation, and authors are only beginning to consider it in sociodemographic studies. A survey with online questionnaires was used in the study. It was forwarded to the social networks of Generation Z students from a university in the city of São Paulo to identify how this generation has behaved in selection processes, in view of new trends, especially when artificial intelligence is used. Artificial intelligence use in selection processes is not very well accepted by the members of this generation, contradicting what is generally propagated and disclosed in the media. The respondents are more attracted to companies that offer growth opportunities, good salaries, and benefits than to companies that offer independence and autonomy.

The relationship between teachers' emotional intelligence and teaching for creativity: The mediating role of working engagement.

Teaching for creativity (TfC) has received increasing attention as an important way to cultivate students' creative thinking and behaviors. The purpose of this study is to examine the mediating role of teachers' work engagement (WE) on the relationship between their emotional intelligence (EI) and teaching for creativity. The study is a cross-sectional design. The sample of the study is 3,307 secondary school English teachers working in Jilin Province, China. The findings show that the teachers' perceptions of emotional intelligence, work engagement and teaching for creativity are relatively high. The findings confirm the hypotheses. The results of structural equation modeling and bootstrapping show that teachers' emotional intelligence is positively correlated with work engagement and teaching for creativity, and teachers' work engagement mediates the relationship between emotional intelligence and teaching for creativity.

"Nothing works without the doctor:" Physicians' perception of clinical decision-making and artificial intelligence.

Artificial intelligence-driven decision support systems (AI-DSS) have the potential to help physicians analyze data and facilitate the search for a correct diagnosis or suitable intervention. The potential of such systems is often emphasized. However, implementation in clinical practice deserves continuous attention. This article aims to shed light on the needs and challenges arising from the use of AI-DSS from physicians' perspectives. The basis for this study is a qualitative content analysis of expert interviews with experienced nephrologists after testing an AI-DSS in a straightforward usage scenario. The results provide insights on the basics of clinical decision-making, expected challenges when using AI-DSS as well as a reflection on the test run. While we can confirm the somewhat expectable demand for better explainability and control, other insights highlight the need to uphold classical strengths of the medical profession when using AI-DSS as well as the importance of broadening the view of AI-related challenges to the clinical environment, especially during treatment. Our results stress the necessity for adjusting AI-DSS to shared decision-making. We conclude that explainability must be context-specific while fostering meaningful interaction with the systems available.

Analysis of the Links between Social Intelligence and Coping Strategies of Business Managers in Terms of Development of Their Potential

The social as well as psychological development of individuals’ potential is influenced by many factors, including managerial competences such as social intelligence and ways of coping with stress. This paper presents the links between social intelligence and strategies for coping with demanding situations by business managers, as well as gender differences in the perception of social intelligence and in the preference for coping strategies between male and female business managers. The research sample consisted of 149 business managers, of which 76 (51%) were male and 73 (49%) were female managers. The results were obtained through research based on two methodologies: MESI for the detection of social intelligence, and Brief COPE, designed to identify coping strategies. Based on the research results, it can be concluded that the links between the social intelligence factors and coping strategies, as well as gender differences in the perception of social intelligence and coping strategies by male and female business managers, have been confirmed. Research into the relationship between these two aspects and its results could contribute to the elimination of undesirable factors influencing the work process and to the subsequent development of the psychological, social and work potential of business managers.

AI meets UAVs: A survey on AI empowered UAV perception systems for precision agriculture

Precision Agriculture (PA) promises to boost crop productivity while reducing agricultural costs and environmental footprints, and therefore is attracting ever-increasing interests in both academia and industry. This management strategy is underpinned by various advanced technologies including Unmanned Aerial Vehicle (UAV) sensing systems and Artificial Intelligence (AI) perception algorithms. In particular, due to their unique advantages such as a low cost, high spatio-temporal resolutions, flexibility, automation functions and minimized risk of operation, UAV sensing systems have been extensively applied in many civilian applications including PA since 2010. In parallel, AI algorithms (deep learning since 2012 in particular) are also drawing ever-increasing attention in different fields, since they are able to analyse an unprecedented volume/velocity/variety of data (semi-) automatically, which are also becoming computationally practical with the advancements of cloud computing, Graphics Processing Units and parallel computing. In this survey paper, therefore, a thorough review is performed on recent use of UAV sensing systems (e.g., UAV platforms, external sensing units) and AI algorithms (mainly supervised learning algorithms) in PA applications throughout the crop life-cycle, as well as the challenges and prospects for future development of UAVs and AI in agriculture sector. It is envisioned that this review is able to provide a timely technical reference, demystifying and promoting research, deployment and successful exploitation of AI empowered UAV perception systems for PA, and therefore contributing to addressing future agricultural and human nutrition challenges.

Do Perceptions of Privilege Enhance—or Impede—Perceptions of Intelligence? Evidence from a National Survey Experiment

Do Perceptions of Privilege Enhance—or Impede—Perceptions of Intelligence? Evidence from a National Survey Experiment

Clinicians' Perceptions of an Artificial Intelligence-Based Blood Utilization Calculator: Qualitative Exploratory Study.

According to the US Food and Drug Administration Center for Biologics Evaluation and Research, health care systems have been experiencing blood transfusion overuse. To minimize the overuse of blood product transfusions, a proprietary artificial intelligence (AI)-based blood utilization calculator (BUC) was developed and integrated into a US hospital's electronic health record. Despite the promising performance of the BUC, this technology remains underused in the clinical setting. This study aims to explore how clinicians perceived this AI-based decision support system and, consequently, understand the factors hindering BUC use. We interviewed 10 clinicians (BUC users) until the data saturation point was reached. The interviews were conducted over a web-based platform and were recorded. The audiovisual recordings were then anonymously transcribed verbatim. We used an inductive-deductive thematic analysis to analyze the transcripts, which involved applying predetermined themes to the data (deductive) and consecutively identifying new themes as they emerged in the data (inductive). We identified the following two themes: (1) workload and usability and (2) clinical decision-making. Clinicians acknowledged the ease of use and usefulness of the BUC for the general inpatient population. The clinicians also found the BUC to be useful in making decisions related to blood transfusion. However, some clinicians found the technology to be confusing due to inconsistent automation across different blood work processes. This study highlights that analytical efficacy alone does not ensure technology use or acceptance. The overall system's design, user perception, and users' knowledge of the technology are equally important and necessary (limitations, functionality, purpose, and scope). Therefore, the effective integration of AI-based decision support systems, such as the BUC, mandates multidisciplinary engagement, ensuring the adequate initial and recurrent training of AI users while maintaining high analytical efficacy and validity. As a final takeaway, the design of AI systems that are made to perform specific tasks must be self-explanatory, so that the users can easily understand how and when to use the technology. Using any technology on a population for whom it was not initially designed will hinder user perception and the technology's use.

Perceptions of US Medical Students on Artificial Intelligence in Medicine: Mixed Methods Survey Study.

Given the rapidity with which artificial intelligence is gaining momentum in clinical medicine, current physician leaders have called for more incorporation of artificial intelligence topics into undergraduate medical education. This is to prepare future physicians to better work together with artificial intelligence technology. However, the first step in curriculum development is to survey the needs of end users. There has not been a study to determine which media and which topics are most preferred by US medical students to learn about the topic of artificial intelligence in medicine. We aimed to survey US medical students on the need to incorporate artificial intelligence in undergraduate medical education and their preferred means to do so to assist with future education initiatives. A mixed methods survey comprising both specific questions and a write-in response section was sent through Qualtrics to US medical students in May 2021. Likert scale questions were used to first assess various perceptions of artificial intelligence in medicine. Specific questions were posed regarding learning format and topics in artificial intelligence. We surveyed 390 US medical students with an average age of 26 (SD 3) years from 17 different medical programs (the estimated response rate was 3.5%). A majority (355/388, 91.5%) of respondents agreed that training in artificial intelligence concepts during medical school would be useful for their future. While 79.4% (308/388) were excited to use artificial intelligence technologies, 91.2% (353/387) either reported that their medical schools did not offer resources or were unsure if they did so. Short lectures (264/378, 69.8%), formal electives (180/378, 47.6%), and Q and A panels (167/378, 44.2%) were identified as preferred formats, while fundamental concepts of artificial intelligence (247/379, 65.2%), when to use artificial intelligence in medicine (227/379, 59.9%), and pros and cons of using artificial intelligence (224/379, 59.1%) were the most preferred topics for enhancing their training. The results of this study indicate that current US medical students recognize the importance of artificial intelligence in medicine and acknowledge that current formal education and resources to study artificial intelligence-related topics are limited in most US medical schools. Respondents also indicated that a hybrid formal/flexible format would be most appropriate for incorporating artificial intelligence as a topic in US medical schools. Based on these data, we conclude that there is a definitive knowledge gap in artificial intelligence education within current medical education in the US. Further, the results suggest there is a disparity in opinions on the specific format and topics to be introduced.

Originality in online dating profile texts: How does perceived originality affect impression formation and what makes a text original?

This paper investigates origins and consequences of perceived profile text originality. The first goal was to examine whether the perceived originality of authentic online dating profile texts affects online daters’ perceptions of attractiveness, and whether perceptions of (less) desired partner personality traits mediate this effect. Results showed the positive impact of perceived profile text originality on impression formation: text originality positively affects perceptions of intelligence and sense of humor, which improve impressions of attractiveness and boost dating intention. The second goal was to explore what profile text features increase perceptions of profile text originality. Results revealed profile texts which were stylistically original (e.g., include metaphors) and contained more and concrete self-disclosure statements were considered more original, explaining almost half of the variance in originality scores. Taken together, our results suggest that perceived originality in profile texts is manifested in both meaning and form, and is a balancing act between novelty and appropriateness.

Anthropomorphic response: Understanding interactions between humans and artificial intelligence agents

This study of anthropomorphic response to artificial intelligence begins with an extensive review of the literature and an identification of conceptual distinctions between anthropomorphism and anthropomorphic response. The authors develop an instrument for measuring how users form anthropomorphic response to interactions with AI chatbots. Amazon MTurk is used to recruit 120 users for a pilot study and 303 users for the main study. Participants respond to six scenarios depicting interactions with banking service chatbots of varying appearance and intelligence. Results show that anthropomorphic response depend on perceptions of agent appearance, cognitive intelligence, and emotional intelligence. Users perceive more humanness in highly intelligent but disembodied agents rather than in highly intelligent agents that have poorly designed appearances. And users who have strong tendencies to anthropomorphize non-sentient entities are less likely to form anthropomorphic response when interacting with agents with high cognitive intelligence. The study enhances understandings about human/AI interactions. It provides directions for future research regarding anthropomorphic response and provide directions for future research on designing and using artificial agents.

Perception and Utilization of Artificial Intelligence (AI) among Dental Professionals in Saudi Arabia

Objective: Artificial intelligence (AI) is the new buzzword that is trendy in multiple branches of dentistry. The aim of this study was to assess perceptions and utilization of AI among dental professionals in Saudi Arabia. Methods: This was a cross-sectional study investigating 389 dental students and dentists from 22 cities in Saudi Arabia using a validated self-reported questionnaire. Results: A total of 49.4% of participants reported knowing what AI is; 44.5% reported having basic knowledge of AI principles, and 42.2% know of AI uses in dentistry. The most common AI information source was social media (66.07%). Out of 17 AI attitude items, 16 were scored above the midpoint. A total of 75.0% of participants agreed or strongly agreed AI will lead to major advances in dentistry. In contrast, 49.1% agreed or strongly agreed that AI could replace dentists in the future. There were no significant differences by gender or region, but students and interns had significantly higher attitude scores than did dentists. There was a widespread desire to take professional courses in dental AI use (69.7%), and some had used dental AI applications (25.4%) or taken an AI course (18.5%–20.3%). The most common barriers to dental AI use were non-availability of courses (73%) and lack of time (68.9%). Conclusion: Dental professionals in Saudi Arabia have moderate awareness levels and high rates of good attitudes about AI in dentistry. However, AI use in practice is limited. Incorporating AI in dental curricula is crucial due to the worldwide digital transformation.

A perceptual study of relationship between emotional intelligence and job performance among higher education sector employees in Saudi Arabia

PurposeThe purpose of this research is to investigate faculties' perceptions of emotional intelligence about job performance (task and contextual performance (TP and CP)) in the higher education sector in the Kingdom of Saudi Arabia.Design/methodology/approachThis research employed an explanatory research design method. A quantitative design approach is adopted by implementing a survey-based study. Quantitative data have been collected anonymously from faculty members (n = 277) working in different higher educational institutes in Saudi Arabia.FindingsThe research findings revealed a positive relationship between others' emotions appraisal and use of emotions with CP, whilst all the dimensions of emotional intelligence, namely self-emotions appraisal (SEA), others' emotions appraisal (OEA), use of emotions (UOE) and regulation of emotions (ROE) revealed a significant positive relationship with the TP. Nevertheless, the research supports the argument that emotional intelligence is considered an essential contributor to faculty members' job performance.Practical implicationsThis research study provides empirical support for the argument that emotional intelligence is a direct driver for enhancing job performance through the appraisal, use and regulation of emotions. In terms of practical implications, the research findings will encourage higher education institutions to take specific actions that will help to enhance awareness of and build emotional intelligence skills amongst faculty staff.Originality/valueThis study is the first that has sought to investigate the impact of emotional intelligence on employees' performance in the Saudi Arabian higher education sector. Additionally, this study has developed a new psychometric scale that is useful in the higher education sector.