• Clinical Endoscopy
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• v.57 no.1
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• pp.11-17
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• 2024

Although magnifying endoscopy with narrow-band imaging is the standard diagnostic test for gastric cancer, diagnosing gastric cancer using this technology requires considerable skill. Artificial intelligence has superior image recognition, and its usefulness in endoscopic image diagnosis has been reported in many cases. The diagnostic performance (accuracy, sensitivity, and specificity) of artificial intelligence using magnifying endoscopy with narrow band still images and videos for gastric cancer was higher than that of expert endoscopists, suggesting the usefulness of artificial intelligence in diagnosing gastric cancer. Histological diagnosis of gastric cancer using artificial intelligence is also promising. However, previous studies on the use of artificial intelligence to diagnose gastric cancer were small-scale; thus, large-scale studies are necessary to examine whether a high diagnostic performance can be achieved. In addition, the diagnosis of gastric cancer using artificial intelligence has not yet become widespread in clinical practice, and further research is necessary. Therefore, in the future, artificial intelligence must be further developed as an instrument, and its diagnostic performance is expected to improve with the accumulation of numerous cases nationwide.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2241-2258
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• 2023

This study developed digital literacy instruments and tested their effectiveness on college students' perceptions of AI technologies. In creating a new digital literacy test tool, we reviewed the concept and scale of digital literacy based on previous studies that identified the characteristics and measurement of AI literacy. We developed 23 preliminary questions for our research instrument and used a quantitative approach to survey 318 undergraduates. After conducting exploratory and confirmatory factor analysis, we found that digital literacy in the age of AI had four ability sub-factors: critical understanding, artificial intelligence social impact recognition, artificial intelligence technology utilization, and ethical behavior. Then we tested the sub-factors' predictive powers on the perception of AI's usefulness and ease of use. The regression result shows that the most common powerful predictor of the usefulness and ease of use of AI technology was the ability to use AI technology. This finding implies that for college students, the ability to use various tools based on AI technology is an essential competency in the AI era.

• The Mathematical Education
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• v.62 no.3
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• pp.401-416
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• 2023

This study systematically examined the influence of preservice teachers' self-efficacy and AI anxiety, on the intention to use AI programs 'knock-knock! mathematics expedition' in mathematics lessons based on a technology acceptance model. The research model was established with variables including self-efficacy, AI anxiety, perceived ease of use, perceived usefulness, and intention of use from 254 pre-service teachers. The structural relationships and direct and indirect effects between these variables were examined through structural equation modeling. The results indicated that self-efficacy significantly affected perceived ease of use, perceived usefulness, and intention to use. In contrast, AI anxiety did not significantly influence perceived ease of use and perceived usefulness. Perceived ease of use significantly affected perceived usefulness and intention to use and perceived usefulness significantly affected intention to use. The findings offer insights and strategies for encouraging the use of 'knock-knock! mathematics expedition' by preservice teachers in mathematics lessons.

• Journal of Convergence for Information Technology
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• v.7 no.4
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• pp.53-60
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• 2017

The purpose of this study was to identify the factors affecting intention and attitude of artificial intelligence technology(AI) of university students in healthcare using UTAUT model. Participants were 278 college students and the data were collected through self-reported questionnaire from May 15 to June 14, 2016. The collected data were analyzed using PASW Statistics/AMOS 22.0. The results were as follows. The effect of expectation factor, social influence, usefulness of work, anxiety factor had a significant effect on use of AI technology Intention. Factor of expectation effect, social influence, usefulness of work, anxiety factor had a significant effect on use of AI technology. As a result of verifying the significance of the indirect effect, it can be seen that the direct effect of the anxiety factor on the attitude factor is partially mediated by the use intention factor and the intention to use was partially mediated in the direct effect of the usefulness factor of the task on the attitude factor. This result means that it is important to increase the expectation factors, social effects, and perceived usefulness through accurate information based on facts and to reduce vague anxiety in order to increase the positive intention and attitude of university students' use of AI technology.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.283-289
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• 2021

Many people with disabilities have shown interest in artificial intelligence speakers that serves as the main hub of the smart home. Therefore, the purpose of this study was to identify the intention of people with disabilities to use such speakers. The focus is on those with physical disabilities, a segment that accounts for the largest number of disability types. Based on the theoretical model of technology acceptance, the effect of perceived ease of use and perceived usefulness of artificial intelligence speakers by people with disabilities was analyzed using Structural Equation Modeling (SEM). Research has confirmed that the technology acceptance model is suitable for identifying the intention to use artificial intelligence speakers by people with disabilities, and specifically that the perceived ease of use has a significant impact on usefulness. Furthermore, the perceived ease of use for people with disabilities did not have a statistically significant effect on their intent to use whereas the perceived usefulness was shown to have a significant effect on the same. This study is meaningful as a foundation for developing customized artificial intelligence speaker services and improving the use of artificial intelligence speakers by people with disabilities.

• Journal of the Health Care and Life Science
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• v.10 no.2
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• pp.413-421
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• 2022

The online questionnaire platform was conducted with Korean medicine doctors to analyses the recognition of applicability of artificial intelligence(AI) to the field of application and department of Korean medicine. Most of all respondents did not have a chance to participate academic experience or research experience related to AI, but had a high willingness to participate in further learning and research. The level of AI understanding was supervised learning When AI is introduced to Korean medicine, the mean predicted usefulness scores to each application field for research and development of oriental medicine(74.60 points) and social policy establishment(73.68 points) are significantly higher than other of Korean medicine field of application, while those of Sasang constitutional department(66.61 points) and Korean medicine rehabilitation(65.91 points) were evaluated higher than other fields of treatment of Korean medicine. Respondents judged that the introduction of AI could be realistically useful in relatively formal fields of Korean medicine, while it would be difficult in non-formal fields.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.231-252
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• 2021

Artificial intelligence (AI) is a key technology that will change the future the most. It affects the industry as a whole and daily life in various ways. As data availability increases, artificial intelligence finds an optimal solution and infers/predicts through self-learning. Research and investment related to automation that discovers and solves problems on its own are ongoing continuously. Automation of artificial intelligence has benefits such as cost reduction, minimization of human intervention and the difference of human capability. However, there are side effects, such as limiting the artificial intelligence's autonomy and erroneous results due to algorithmic bias. In the labor market, it raises the fear of job replacement. Prior studies on the utilization of artificial intelligence have shown that individuals do not necessarily use the information (or advice) it provides. Algorithm error is more sensitive than human error; so, people avoid algorithms after seeing errors, which is called "algorithm aversion." Recently, artificial intelligence has begun to be understood from the perspective of the augmentation of human intelligence. We have started to be interested in Human-AI collaboration rather than AI alone without human. A study of 1500 companies in various industries found that human-AI collaboration outperformed AI alone. In the medicine area, pathologist-deep learning collaboration dropped the pathologist cancer diagnosis error rate by 85%. Leading AI companies, such as IBM and Microsoft, are starting to adopt the direction of AI as augmented intelligence. Human-AI collaboration is emphasized in the decision-making process, because artificial intelligence is superior in analysis ability based on information. Intuition is a unique human capability so that human-AI collaboration can make optimal decisions. In an environment where change is getting faster and uncertainty increases, the need for artificial intelligence in decision-making will increase. In addition, active discussions are expected on approaches that utilize artificial intelligence for rational decision-making. This study investigates the impact of artificial intelligence on decision-making focuses on human-AI collaboration and the interaction between the decision maker personal traits and advisor type. The advisors were classified into three types: human, artificial intelligence, and human-AI collaboration. We investigated perceived usefulness of advice and the utilization of advice in decision making and whether the decision-maker's personal traits are influencing factors. Three hundred and eleven adult male and female experimenters conducted a task that predicts the age of faces in photos and the results showed that the advisor type does not directly affect the utilization of advice. The decision-maker utilizes it only when they believed advice can improve prediction performance. In the case of human-AI collaboration, decision-makers higher evaluated the perceived usefulness of advice, regardless of the decision maker's personal traits and the advice was more actively utilized. If the type of advisor was artificial intelligence alone, decision-makers who scored high in conscientiousness, high in extroversion, or low in neuroticism, high evaluated the perceived usefulness of the advice so they utilized advice actively. This study has academic significance in that it focuses on human-AI collaboration that the recent growing interest in artificial intelligence roles. It has expanded the relevant research area by considering the role of artificial intelligence as an advisor of decision-making and judgment research, and in aspects of practical significance, suggested views that companies should consider in order to enhance AI capability. To improve the effectiveness of AI-based systems, companies not only must introduce high-performance systems, but also need employees who properly understand digital information presented by AI, and can add non-digital information to make decisions. Moreover, to increase utilization in AI-based systems, task-oriented competencies, such as analytical skills and information technology capabilities, are important. in addition, it is expected that greater performance will be achieved if employee's personal traits are considered.

• Journal of radiological science and technology
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• v.43 no.6
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• pp.461-467
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• 2020

In the field of nuclear medicine, errors are sometimes generated because the assessment of the uniformity of gamma cameras relies on the naked eye of the evaluator. To minimize these errors, we created an artificial intelligence model based on CNN algorithm and wanted to assess its usefulness. We produced 20,000 normal images and partial cold region images using Python, and conducted artificial intelligence training with Resnet18 models. The training results showed that accuracy, specificity and sensitivity were 95.01%, 92.30%, and 97.73%, respectively. According to the results of the evaluation of the confusion matrix of artificial intelligence and expert groups, artificial intelligence was accuracy, specificity and sensitivity of 94.00%, 91.50%, and 96.80%, respectively, and expert groups was accuracy, specificity and sensitivity of 69.00%, 64.00%, and 74.00%, respectively. The results showed that artificial intelligence was better than expert groups. In addition, by checking together with the radiological technologist and AI, errors that may occur during the quality control process can be reduced, providing a better examination environment for patients, providing convenience to radiologists, and improving work efficiency.

• Child Health Nursing Research
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• v.28 no.4
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• pp.291-298
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• 2022

Purpose: This study aimed to identify students' awareness of the use of a chatbot (A-uC), a type of artificial intelligence technology, for violence prevention among elementary school students. Methods: The participants comprised 215 students in the fourth to sixth grades in Chuncheon, South Korea, and data were collected via a self-reported questionnaire. Results: The mean A-uC score was 3.43±0.83 out of 5 points. The mean scores for the 4 sub-dimensions of the A-uC tool were 3.48±0.80 for perceived value, 3.44±0.98 for perceived usefulness, 3.63±0.92 for perceived ease of use, and 3.15±1.07 for intention to use. Significant differences were observed in A-uC scores (F=59.26, p<.001) according to the need for the use of chatbots in violence prevention education. The relationships between intention to use and the other A-uC sub-dimensions showed significant correlations with perceived value (r=.85, p<.001), perceived usefulness (r=.76, p<.001), and perceived ease of use (r=.64, p<.001). Conclusion: The results of this study suggest that chatbots can be used in violence prevention education for elementary school students.

• Informatization Policy
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• v.29 no.1
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• pp.60-83
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• 2022

The purpose of this study is to empirically analyze the effect of individual perception of artificial intelligence and the level of digital literacy on the acceptance of artificial intelligence-based public services. For empirical analysis, a research model was set up based on the technology acceptance model and planned behavior theory using survey data of 2017 and analyzed through structural equations. To summarize the results of the analysis, firstly, the positive perception of individuals about artificial intelligence technology plays a role in reinforcing attitudes toward benefits and reducing concerns about public service in which artificial intelligence technology has been introduced. Secondly, the level of digital literacy reinforces both benefits and concerns about artificial intelligence technology, but it was found that the intention to use public services was reinforced through the benefits of artificial intelligence technology perceived by individuals, rather than privacy concerns about artificial intelligence technology. Thirdly, it was confirmed that the perceived benefits of individuals on artificial intelligence technology reinforced the intention to use public civil services, and privacy concerns negatively influenced the intention to use. It was confirmed that the influence of a perceived ease of use and usefulness, as opposed to privacy concerns, further reinforces the intention to use. Both citizens' positive perceptions regarding the accuracy and reliability of information provided through artificial intelligence technology and institutional complementation of responsibility for errors caused by artificial intelligence technology are strengthened, and technical problems related to privacy protection are solved.