• Journal of Information Technology Services
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• v.23 no.1
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• pp.81-95
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• 2024

ChatGPT, a generative artificial intelligence(AI) technology that analyzes conversations to identify users' intentions and generates responses in consideration of the context of the conversation, is attracting attention from a user interface (UI) perspective that it can provide information through natural conversations with users. This study examined the effect of functional and playful values experienced by early users of ChatGPT on reuse intention and verified the structural relationship between technological efficacy, experiential values, and reuse intention. To verify the research model and hypotheses, a survey was conducted on college students who used ChatGPT for the first time. A total of 156 responses were received and 154 responses were used for analysis. As a result, both the functional experiential value and playful experiential value in the initial use process had significant effects on the intention to use ChatGPT. In addition, it was found that technological efficiency had a significant effect on functional and playful experiential values.

• Asia pacific journal of information systems
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• v.29 no.4
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• pp.817-837
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• 2019

The medical industry is rapidly evolving into a combination of artificial intelligence (AI) and ICT technology, such as mobile health, wireless medical, telemedicine and precision medical care. Medical artificial intelligence can be diagnosed and treated, and autonomous surgical robots can be operated. For smart medical services, data such as medical information and personal medical information are needed. AI is being developed to integrate with companies such as Google, Facebook, IBM and others in the health care field. Telemedicine services are also becoming available. However, security issues of medical information for smart medical industry are becoming important. It can have a devastating impact on life through hacking of medical devices through vulnerable areas. Research on medical information is proceeding on the necessity of privacy and privacy protection. However, there is a lack of research on the practical measures for protecting medical information and the seriousness of security threats. Therefore, in this study, we want to confirm the research trend by collecting data related to medical information in recent 5 years. In this study, smart medical related papers from 2014 to 2018 were collected using smart medical topics, and the medical information papers were rearranged based on this. Research trend analysis uses topic modeling technique for topic information. The result constructs topic network based on relation of topics and grasps main trend through topic.

• The Journal of the Korea Contents Association
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• v.20 no.10
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• pp.630-640
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• 2020

With a recent rise of various chatbot services, this study attempts to explore the role of chatbot users'' psychological characteristics by applying the self-determination theory. In particular,this study investigates (a) the relationship between chatbot users' psychological needs(perceived autonomy, perceived competence, perceived relatedness) and user satisfaction, continuous intention to use, and (b) the moderating effect of chatbot type and user motivations. Based upon a survey, the results revealed that perceived autonomy, competence, and relatedness had significant positive effects on user satisfaction and continuous intention to use. The results also confirmed that the user satisfaction had a significant positive effect on the continuous intention to use. The moderating effect of social and relational motivation was found between perceived autonomy, competence, relatedness and user satisfaction. The implications are discussed for furthering the development of chatbot services.

• Electronics and Telecommunications Trends
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• v.36 no.6
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• pp.36-45
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• 2021

As the global demand for satellite IoT services using small satellites increases, interest in their frequency requirements has also increased. Consequently, International Telecommunication Union Radiocommunication Sector (ITU-R) preparatory studies for WRC-23 include AI 1.18, which considers new frequency allocations for narrowband mobile satellites. This agenda item was issued in accordance with Resolution 284 (WRC-19), and contributions and reviews by government and satellite operators are underway at ITU-R SG4 WP4C with the aim of completing the study in 2023. Resolution 248 (WRC-19) considers the conditions for transmission of candidate bands and satellites and terminals for narrowband mobile satellite, and all contributions should satisfy narrowband mobile satellite system characteristics parameters within these conditions. However, among the current transmission specifications, there are several views on the exact definition of satellite e.i.r.p., and the derivation schedule of characteristic system parameters for the study is slower than that of the original work schedule. The goal of this paper is to examine the outline of WRC-23 AI 1.18 and the main content of Resolution 284 (WRC-19) and to determine the status of studies related to WRC-23 AI 1.18. The ITU-R's study on this agenda includes updating work schedules, developing the draft required spectrum and system characteristics parameter reports/recommendations, developing draft CPM reports, and examining the various views of transmission specifications in Resolution 284 (WRC-19). Focusing on candidate bands in Region 1 (Europe and Africa) and Region 2 (America), the current status of use in Korea is investigated and future countermeasures in Korea are investigated. In addition, we would like to examine the trend of narrowband mobile satellite through satellite frequency and service status and planning of satellite IoT operators, such as EchoStar, Omnispace, and Sateliot that are participating in the ITU-R study.

• Journal of Information Technology Services
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• v.20 no.3
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• pp.41-56
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• 2021

New drug discovery and development research enable clinical treatment that saves human life and improves the quality of life, but the possibility of success with new drugs is significantly low despite a long time of 14 to 16 years and a large investment of 2 to 3 trillion won in traditional methods. As artificial intelligence is expected to radically change the new drug development paradigm, artificial intelligence new drug discovery and development projects are underway in various forms of collaboration, such as joint research between global pharmaceutical companies and IT companies, and government-private consortiums. This study uses the TOE framework and the Value-based Adoption Model, and the technical, organizational, and environmental factors that should be considered for the acceptance of AI technology at the level of the new drug research organization are the value of artificial intelligence technology. By analyzing the explanatory power of the relationship between perception and intention to use, it is intended to derive practical implications. Therefore, in this work, we present a research model in which technical, organizational, and environmental factors affecting the introduction of artificial intelligence technologies are mediated by strategic value recognition that takes into account all factors of benefit and sacrifice. Empirical analysis shows that usefulness, technicality, and innovativeness have significantly affected the perceived value of AI drug development systems, and that social influence and technology support infrastructure have significant impact on AI Drug Discovery and Development systems.

• Journal of Information Technology Services
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• v.20 no.6
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• pp.1-16
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• 2021

The health functional food market continues to grow, and according to that trend, the subdivision sales of personalized health functional foods, which have been legally prohibited, will be operated as a special regulatory pilot project. Personalized health functional food recommendations have a variety of personalized indicators to consider, and it is believed that algorithmic methods will be needed to proceed in a customized manner considering all of them. This study aims to contribute to the development of the AI-based health functional food recommendation service by studying factors that affect the use of the AI-based health functional food recommendation service. This paper analyzed the intention of use for AI-based health functional food recommendation service based on the information system success model and Technology Acceptance Model. This study considered information quality factors, service quality factor, and system quality factor as independent variables influencing perceived usefulness, perceived ease of use and trust. For empirical analysis, 406 questionnaires were used and the collected data were performed using AMOS 22.0 and SPSS 22.0. Research has shown that the accuracy, timeliness, empathy and availability have a positive effect on usefulness. Understandability and availability has been shown to have a positive effect on ease of use. The accuracy, understandability, empathy and availibility has been shown to have a positive impact on Trust. Usefulness, ease of use and trust all have been shown to have a positive influence on intention of use.

• Journal of Service Research and Studies
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• v.14 no.1
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• pp.13-26
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• 2024

Multi-modal generation is the process of generating results based on a variety of information, such as text, images, and audio. With the rapid development of AI technology, there is a growing number of multi-modal based systems that synthesize different types of data to produce results. In this paper, we present an AI system that uses speech and text recognition to describe a person and generate a montage image. While the existing montage generation technology is based on the appearance of Westerners, the montage generation system developed in this paper learns a model based on Korean facial features. Therefore, it is possible to create more accurate and effective Korean montage images based on multi-modal voice and text specific to Korean. Since the developed montage generation app can be utilized as a draft montage, it can dramatically reduce the manual labor of existing montage production personnel. For this purpose, we utilized persona-based virtual person montage data provided by the AI-Hub of the National Information Society Agency. AI-Hub is an AI integration platform aimed at providing a one-stop service by building artificial intelligence learning data necessary for the development of AI technology and services. The image generation system was implemented using VQGAN, a deep learning model used to generate high-resolution images, and the KoDALLE model, a Korean-based image generation model. It can be confirmed that the learned AI model creates a montage image of a face that is very similar to what was described using voice and text. To verify the practicality of the developed montage generation app, 10 testers used it and more than 70% responded that they were satisfied. The montage generator can be used in various fields, such as criminal detection, to describe and image facial features.

• Convergence Security Journal
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• v.21 no.2
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• pp.67-79
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• 2021

Lessons Learned(LL) is a military term defined as all activities that promote future development by finding problems and need improvement in education and reality in the field of warfare development. In this paper, we focus on presenting actual examples and applying AI analysis inference techniques to solve revealed problems in promoting LL activities, such as long-term analysis, budget problems, and necessary expertise. AI legal advice services using cognitive computing-related technologies that have already been practical and in use, were judged to be the best examples to solve the problems of LL. This paper presents intelligent LL inference techniques, which utilize AI. To this end, we want to explore theoretical backgrounds such as LL analysis definitions and examples, evolution of AI into Machine Learning, cognitive computing, and apply it to new technologies in the defense sector using the newly proposed L²-OODA ensemble algorithm to contribute to implementing existing power improvement and optimization.

• Journal of Digital Convergence
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• v.19 no.6
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• pp.299-308
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• 2021

This study aimed at identifying the significant effects and effectiveness of patients with mild dementia when using personalized reminiscence therapy digital services using AI voice technology. In the process of interpreting the results of stakeholder interviews, the design idea of personal customization using voice AI technology was derived, and prototypes were created and usability tests were conducted in the first and second rounds. The main results are as follows: Since reminiscence therapy itself is highly influenced by personal experience and can receive customized care guides based on treatment status and results through customized treatment programs, the concept of personalization can improve the quality of treatment than existing treatment methods. However, it is expected that the usability of the service will further increase if we study micro-interactions that can prevent errors and increase usability, as issues that may arise due to the forgetting cognitive characteristics of mild dementia patients are observed.

• Journal of Medicine and Life Science
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• v.20 no.2
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• pp.53-59
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• 2023

This study explores the potential use of artificial intelligence (AI)-based services, specifically ChatGPT-3.5, in medical education. The application of this technology is acknowledged as a valuable tool for simulating authentic clinical scenarios and enhancing learners' diagnostic and communication skills. To construct a case, students received ChatGPT training using a clinical ethics casebook titled "Clinical Ethics Cases and Commentaries for Medical Students and Physicians." Subsequently, a role-play script was generated based on this training. The initial draft of the script was reviewed by two medical professors and was further optimized using ChatGPT-3.5. Consequently, a comprehensive role-play script, accurately reflecting real-world clinical situations, was successfully developed. This study demonstrates the potential for effectively integrating AI technology into medical education and provides a solution to overcome limitations in developing role-play scripts within conventional educational settings. However, the study acknowledges that AI cannot always generate flawless role-play scripts and recognizes the necessity of addressing these limitations and ethical concerns. The research explores both the potential and limitations of employing AI in the early stages of medical education, suggesting that future studies should focus on overcoming these limitations while further investigating the potential applications of AI in this field.