• Journal of the Korea Convergence Society
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• v.9 no.9
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• pp.97-104
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• 2018

The purpose of this study was to investigate the expectation toward medical artificial intelligence(AI) of students in majoring health, and to utilize it as a basic data for widespread use of medical AI for 500 students majoring in health science at Cheonan city. The awareness of AI was 18.6%, the reliability of AI was 24.8%, and agreement to use of medical AI was 38%. Also, the higher the awareness and reliability of AI were, the higher the expectation of AI was. As a result, education on medical AI in the major field should be a cornerstone for the development of an effective healthcare environment utilizing medical AI by raising awareness, reliability and expectation of AI.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.608-610
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• 2023

의료 분야에서 AI 모델의 활용이 증가하고 있지만, 모델의 예측 불확실성을 정확하게 평가하고 표현하는 것이 중요하다. 본 연구는 이러한 문제를 해결하기 위해 AI-driven 방식을 제안하며, 특히 의료 영상 변환 모델에 대한 불확실성 표현과 데이터 한계 극복 방법론을 제안한다. 제안된 AI-driven 안저영상 변환 모델은 기존 GAN과는 다르게 구조가 이루어져 있으며, 신뢰도가 낮은 영역을 구분하고 시각화하여 표현할 수 있다. 실험 결과, 제안된 방법은 기존 모델과 비교하여 영상 변환 성능이 크게 향상되었으며, 불확실성에 대한 정확도 평가에서도 AI-driven 방식이 높은 성능을 보인다. 결론적으로, 본 연구는 AI-driven 방식을 통해 의료 AI에서의 불확실성 표현의 가능성을 확인하였으며, 이 방식이 데이터의 한계와 불확실성을 극복할 수 있을 것으로 기대된다.

• Journal of the Korea Society of Computer and Information
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• v.25 no.9
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• pp.173-182
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• 2020

The purpose of this paper is to study the influence of new service methods of "artificial intelligence (AI) + medical cosmetology", a new service means, on customers' purchase intentions. To AI medical beauty APP sales as an empirical study. This paper designed Likert seven scale to investigate, using SPSS 24.0 statistical analysis software and AMOS24.0 structural equation software to analyze the survey data. The analysis method uses reliability analysis, validity analysis, and construct equation model analysis. Through empirical research, the following results can be found, 1. The system quality of AI medical beauty app will have a positive impact on perceived usefulness and perceived ease of use. 2. The information quality of AI medical beauty app will have a positive impact on perceived ease of use and perceived usefulness. 3. The service quality of AI medical beauty app will have a positive impact on perceived ease of use and perceived usefulness 4. Consumers' perceived ease of use has a positive impact on perceived usefulness and purchase intention. 5. The usefulness of consumers' notification has a positive effect on purchase intention.

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.602-603
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• 2024

이 논문은 코로나 팬데믹 시대에 원격 의료 서비스의 중요성이 부상함에 따라, LLM(Large Language Model)과 웨어러블 기기를 활용한 의료 기술의 발전과 이를 통한 의료 서비스의 혁신에 대해 다루고 있다. 코로나 19 대응을 위해 원격 의료에 대한 법적 제한이 완화되며, 이에 따른 원격 의료 시스템의 확대를 언급하고 있다. LLM 을 활용한 의료 정보 관리와 웨어러블을 통한 건강 모니터링을 소개하며, 대화형 AI 를 통한 문의사항 처리와 2 차 처방, 실시간 번역 AI 기술 등의 기술적 혁신을 언급하고 있다. 이러한 기술들이 의료 서비스의 혁신과 개인 건강 관리에 새로운 차원을 열어주지만, 보안 문제와 디지털 격차 등의 문제가 동반될 수 있다고 경고하며, 이를 극복하기 위한 대책과 지속적인 개선이 필요하다고 강조하고 있다.

• Knowledge Management Research
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• v.25 no.2
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• pp.155-176
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• 2024

This study explores variations in preferences for human and AI agents within the medical and financial services. Study 1 investigates whether there are preferential disparities between human and AI agents across these service domains. It finds that human agents are favored over AI agents in medical services, while AI agents receive greater preference in the financial services. Study 2 delves into the underlying reasons for the preference differentials between human and AI agents by assessing the significance of certain capabilities as perceived by users in each domain. The findings reveal a mediating role of perceived empathy importance in the effect of service domains on human-AI preference. Furthermore, perceived empathy is deemed a more critical capability by users for preferring human over AI agents across both service domains compared to other capabilities such as experience and agency. This research is noteworthy for elucidating the variances in preferences for human and AI agents across medical and financial services and the rationale behind these differences. It enhances our theoretical comprehension of the pivotal factors influencing preferences for human and AI agents, underscoring the significance of human experiential capabilities like empathy.

• Journal of the Korean Society of Radiology
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• v.17 no.6
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• pp.881-887
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• 2023

This study aims to analyze the development and current trends of AI-based medical imaging devices commercialized in South Korea. As of September 30, 2023, there were a total of 186 AI-based medical devices licensed, certified, and reported to the Korean Ministry of Food and Drug Safety, of which 138 were related to imaging. The study comprehensively examined the yearly approval trends, equipment types, application areas, and key functions from 2018 to 2023. The study found that the number of AI medical devices started from four products in 2018 and grew steadily until 2023, with a sharp increase after 2020. This can be attributed to the interaction between the advancement of AI technology and the increasing demand in the medical field. By equipment, AI medical devices were developed in the order of CT, X-ray, and MR, which reflects the characteristics and clinical importance of the images of each equipment. This study found that the development of AI medical devices for specific areas such as the thorax, cranial nerves, and musculoskeletal system is active, and the main functions are medical image analysis, detection and diagnosis assistance, and image transmission. These results suggest that AI's pattern recognition and data analysis capabilities are playing an important role in the medical imaging field. In addition, this study examined the number of Korean products that have received international certifications, particularly the US FDA and European CE. The results show that many products have been certified by both organizations, indicating that Korean AI medical devices are in line with international standards and are competitive in the global market. By analyzing the impact of AI technology on medical imaging and its potential for development, this study provides important implications for future research and development directions. However, challenges such as regulatory aspects, data quality and accessibility, and clinical validity are also pointed out, requiring continued research and improvement on these issues.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.9
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• pp.317-322
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• 2022

The rapid development of information technology is also bringing about many changes in the medical environment. In particular, it is leading the rapid change of medical image information systems using big data and artificial intelligence (AI). The prescription delivery system (OCS), which consists of an electronic medical record (EMR) and a medical image storage and transmission system (PACS), has rapidly changed the medical environment from analog to digital. When combined with multiple solutions, PACS represents a new direction for advancement in security, interoperability, efficiency and automation. Among them, the combination with artificial intelligence (AI) using big data that can improve the quality of images is actively progressing. In particular, AI PACS, a system that can assist in reading medical images using deep learning technology, was developed in cooperation with universities and industries and is being used in hospitals. As such, in line with the rapid changes in the medical image information system in the medical environment, structural changes in the medical market and changes in medical policies to cope with them are also necessary. On the other hand, medical image information is based on a digital medical image transmission device (DICOM) format method, and is divided into a tomographic volume image, a volume image, and a cross-sectional image, a two-dimensional image, according to a generation method. In addition, recently, many medical institutions are rushing to introduce the next-generation integrated medical information system by promoting smart hospital services. The next-generation integrated medical information system is built as a solution that integrates EMR, electronic consent, big data, AI, precision medicine, and interworking with external institutions. It aims to realize research. Korea's medical image information system is at a world-class level thanks to advanced IT technology and government policies. In particular, the PACS solution is the only field exporting medical information technology to the world. In this study, along with the analysis of the medical image information system using big data, the current trend was grasped based on the historical background of the introduction of the medical image information system in Korea, and the future development direction was predicted. In the future, based on DICOM big data accumulated over 20 years, we plan to conduct research that can increase the image read rate by using AI and deep learning algorithms.

• The Korean Society of Law and Medicine
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• v.24 no.4
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• pp.67-101
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• 2023

The utilization of generative AI in the medical field is also being rapidly researched. Access to vast data sets reduces the time and energy spent in selecting information. However, as the effort put into content creation decreases, there is a greater likelihood of associated issues arising. For example, with generative AI, users must discern the accuracy of results themselves, as these AIs learn from data within a set period and generate outcomes. While the answers may appear plausible, their sources are often unclear, making it challenging to determine their veracity. Additionally, the possibility of presenting results from a biased or distorted perspective cannot be discounted at present on ethical grounds. Despite these concerns, the field of generative AI is continually advancing, with an increasing number of users leveraging it in various sectors, including biomedical and life sciences. This raises important legal considerations regarding who bears responsibility and to what extent for any damages caused by these high-performance AI algorithms. A general overview of issues with generative AI includes those discussed above, but another perspective arises from its fundamental nature as a large-scale language model ('LLM') AI. There is a civil law concern regarding "the memorization of training data within artificial neural networks and its subsequent reproduction". Medical data, by nature, often reflects personal characteristics of patients, potentially leading to issues such as the regeneration of personal information. The extensive application of generative AI in scenarios beyond traditional AI brings forth the possibility of legal challenges that cannot be ignored. Upon examining the technical characteristics of generative AI and focusing on legal issues, especially concerning the protection of personal information, it's evident that current laws regarding personal information protection, particularly in the context of health and medical data utilization, are inadequate. These laws provide processes for anonymizing and de-identification, specific personal information but fall short when generative AI is applied as software in medical devices. To address the functionalities of generative AI in clinical software, a reevaluation and adjustment of existing laws for the protection of personal information are imperative.

• Electronics and Telecommunications Trends
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• v.36 no.1
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• pp.81-88
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• 2021

Post COVID-19, the medical legacy system will be transformed for utilizing medical resources efficiently, minimizing medical service imbalance, activating remote medical care, and strengthening private-public medical cooperation. This can be realized by achieving an entire medical paradigm shift and not simply via the application of advanced technologies such as AI. We propose a medical system configuration named "Medical AI Hub" that can realize the shift of the existing paradigm. The development stage of this configuration is categorized into "AI Cooperation Hospital," "AI Base Hospital," and "AI Hub Hospital." In the "AI Hub Hospital" stage, the medical intelligence in charge of individual patients cooperates and communicates autonomously with various medical intelligences, thereby achieving synchronous evolution. Thus, this medical intelligence supports doctors in optimally treating patients. The core technologies required during configuration development and their current R&D trends are described in this paper. The realization of the central configuration of medical AI through the development of these core technologies will induce a paradigm shift in the new medical system by innovating all medical fields with influences at the individual, society, industry, and public levels and by making the existing medical system more efficient and intelligent.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.544-546
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• 2020

의료 데이터를 이용하여 인공지능 기계학습 연구를 수행할 때 자주 마주하는 문제는 데이터 불균형, 데이터 부족 등이며 특히 정제된 충분한 데이터를 구하기 힘들다는 것이 큰 문제이다. 본 연구에서는 이를 해결하기 위해 GAN(Generative Adversarial Network) 기반 고해상도 의료 영상을 생성하는 프레임워크를 개발하고자 한다. 각 해상도 마다 Scale 의 Gradient 를 동시에 학습하여 빠르게 고해상도 이미지를 생성해낼 수 있도록 했다. 고해상도 이미지를 생성하는 Neural Network 를 고안하였으며, PGGAN, Style-GAN 과의 성능 비교를 통해 제안된 모델이 양질의 고해상도 의료영상 이미지를 더 빠르게 생성할 수 있음을 확인하였다. 이를 통해 인공지능 기계학습 연구에 있어서 의료 영상의 데이터 부족, 데이터 불균형 문제를 해결할 수 있는 Data augmentation 이나, Anomaly detection 등의 연구에 적용할 수 있다.