• Journal of Breast Disease
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• v.6 no.2
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• pp.46-51
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• 2018

Purpose: Endocrine therapy is the preferred treatment for hormone receptor (HR)-positive metastatic breast cancer (MBC). We investigated the efficacy of combined aromatase inhibitor (AI) and luteinizing hormone-releasing hormone (LHRH) agonist in premenopausal patients with HR-positive MBC. Methods: We retrospectively analyzed the medical records of 21 HR-positive premenopausal MBC patients treated with combined AI and LHRH agonist therapy. Results: The median follow-up period was 32.9 months. The overall response rate was 47.6%, with three complete responses (14.3%) and seven partial responses (33.3%). Nine patients (42.9%) achieved stable disease lasting more than 6 months; thus, the clinical benefit rate was 90.4%. The median time to progression was 45.4 months. No patients experienced grade 3 or 4 toxicity. Conclusion: Combined AI and LHRH agonist treatment safely and effectively induced remission or prolonged disease stabilization, suggesting that this could be a promising treatment option for HR-positive premenopausal patients with MBC.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.6
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• pp.1283-1292
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• 2022

At present, several artificial intelligence projects in the healthcare and medical field are competing with each other, and the interfaces between the systems lack unified specifications. Thus, this study presents an artificial intelligence platform for healthcare and medical fields which adopts the deep learning technology to provide algorithms, models and service support for the health and medical enterprise applications. The suggested platform can provide a large number of heterogeneous data processing, intelligent services, model managements, typical application scenarios, and other services for different types of business. In connection with the suggested platform application, we represents a medical service which is corresponding to the trusted and comprehensible tracking and analyzing patient behavior system for Health and Medical treatment using Internet of Behavior concept.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.05a
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• pp.282-284
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• 2021

최근 4차 산업혁명으로 의료빅데이터 기반으로 한 AI 기술이 급속도로 발전하고 있다. 특히, 의료영상을 기반으로 병변을 탐색, 분활 및 정량화 그리고 자동진단 및 예측 관련된 기술이 AI 제품으로 출시되고 있다. AI 기술개발은 많은 학습데이터가 요구되며, 임상검증에 단일기관에서 2개 이상 기관의 검증이 요구되고 있다. 그러나 아직까지도 단일기관에서 학습용 데이터와 테스트, 검증용 데이터를 달리하여 기술개발에 활용하고 있다. 본 논문은 AI 기술개발에 필요한 영상데이터에 대한 표준화된 데이터셋 변환 및 관리를 위한 시스템에 대해 기술한다. 다기관 데이터를 수집하기 위해서는 각 기관의 의료영상 데이터 수집 및 저장하는 기준이 명확하지 않아 표준화 작업이 필요하다. 제안한 시스템은 기관 또는 다기관 연구 그룹의 의료영상데이터를 표준화하여 저장할 수 있을 뿐만 아니라 의료영상 뷰어 및 의료영상 리스트를 통해 연구자가 원하는 의료영상 데이터 셋을 검색하여 다양한 데이터셋으로 제공할 수 있기 때문에 수집 및 변환 그리고 관리까지 지원할 수 있는 시스템으로 영상기반의 머신러닝 연구에 활력을 불어넣을 수 있을 것으로 기대하고 있다.

• Journal of Internet Computing and Services
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• v.22 no.4
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• pp.99-110
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• 2021

Governments around the world are actively establishing strategies and initiatives to spread the use of artificial intelligence (AI), for AI is not a mere new technology, but is an innovative technology that brings about extensive changes in industrial and social structures and is a core engine that will lead the 4th Industrial Revolution. The South Korean government has also been paying attention to AI as a technology and tool for innovative growth, but its application to the industries is still rather sluggish. The government has prepared multifarious AI-related policies with the aim of constructing South Korea as an AI powerhouse, but there is no clear strategy on which detailed policies to implement first and which industries to apply AI preferentially. With these limitations of South Korea's AI policies in mind, this paper analyzed the priorities of industries in AI adoption and the priorities of AI-related national policies, using Delphi-AHP method for 30 top-level AI experts in South Korea. The results of analysis show that AI application is urgent and necessary in the fields of medical/healthcare, public and safety, and manufacturing, which seems to reflect the peak of the COVID-19 crisis in the second half of 2020 at the time of the investigation. And it turns out that policies related to AI talent cultivation, data, and R&D investment are important and urgent above all in order for organizations to apply AI. This suggests that strategies are required to focus limited national resources on these industries and policies first.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.1022-1034
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• 2023

Various aspects of artificial intelligence (AI) have become of significant interest to academia and industry in recent times. To satisfy these academic and industrial interests, it is necessary to comprehensively investigate trends in AI-related changes of diverse areas. In this study, we identified and predicted emerging convergences with the help of AI-associated research abstracts collected from the SCOPUS database. The bidirectional encoder representations obtained via the transformers-based topic discovery technique were subsequently deployed to identify emerging topics related to AI. The topics discovered concern edge computing, biomedical algorithms, predictive defect maintenance, medical applications, fake news detection with block chain, explainable AI and COVID-19 applications. Their convergences were further analyzed based on the shortest path between topics to predict emerging convergences. Our findings indicated emerging AI convergences towards healthcare, manufacturing, legal applications, and marketing. These findings are expected to have policy implications for facilitating the convergences in diverse industries. Potentially, this study could contribute to the exploitation and adoption of AI-enabled convergences from a practical perspective.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.2
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• pp.111-118
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• 2016

Artificial intelligence (AI) is making computer systems intelligent to do right thing. The AI is used today in a variety of fields, such as journalism, medical, industry as well as entertainment. The impact of AI is becoming larger day after day. In general, the AI system has to lead the optimal decision under uncertainty. But it is difficult for the AI system can derive the best conclusion. In addition, we have a trouble to represent the intelligent capacity of AI in numeric values. Statistics has the ability to quantify the uncertainty by two approaches of frequentist and Bayesian. So in this paper, we propose a methodology of the connection between statistics and AI efficiently. We compute a fixed value for estimating the population parameter using the frequentist learning. Also we find a probability distribution to estimate the parameter of conceptual population using Bayesian learning. To show how our proposed research could be applied to practical domain, we collect the patent big data related to Apple company, and we make the AI more intelligent to understand Apple's technology.

• Electronics and Telecommunications Trends
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• v.35 no.5
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• pp.69-82
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• 2020

In many technologies, artificial intelligence (AI) is becoming an important topic for areas based on the field of big data. However, applied AI cases and the research status of radio wave technology are not widely known to the public. The spread of AI to other areas is being followed by radio wave technologies, and much effort is being taken to evolve it into intelligent radio wave technologies in the future. This paper presents the recent areas of interest in radio wave technology, such as spectral sharing, illegal spectrum monitoring, radar detection, radio wave medical imaging, and channel modeling; examines the requirements for applying AI; and describes the applied cases, research trends, and standardization efforts that apply AI technology to them. On this basis, we will discuss the prospects of AI application to the expected radio wave technology of the future.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.415-420
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• 2021

In a situation where the medical care field is developing from various angles, medical promotion video analysis has an important meaning. It is important as a matter of improving competitiveness, and in the era of acceleration of AI systems, medical care is also the leading field. Accordingly, the importance of videos on publicity, advertisements, and explanations is very important, and it is also an important direction to change the image of a company. In this study, the design characteristics and differences in the video were compared, focusing on the comparative analysis of professional videos of AI medical brands, with two foreign major companies (Stryker and Hill-rom) and one domestic leading company (Nine Bell), and detailed part analysis and section analysis were performed accordingly. As a technical partial analysis of image editing, the transition method and infographic graphics were considered. In an in-depth comparison, we found that AI medical imaging Points such as differences in image tone and image color harmony were analyzed and compared. For a detailed analysis in the video image determination part, we compared and studied the differentiated elements appearing in the promotional design and specific scenes of the video intro part and the product description video part of each video.

• Review of Korea Contents Association
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• v.16 no.4
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• pp.20-23
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• 2018

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.5
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• pp.279-291
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• 2023

In recent years, language model-based generative AI technologies have made remarkable progress. In particular, it has attracted a lot of attention due to its increasing potential in various fields such as summarization and code writing. As a reflection of this interest, the number of patent applications related to generative AI has been increasing rapidly. In order to understand these trends and develop strategies accordingly, future forecasting is key. Predictions can be used to better understand the future trends in the field of technology and develop more effective strategies. In this paper, we analyzed patents filed to date to identify the direction of development of language model-based generative AI. In particular, we took an in-depth look at research and invention activities in each country, focusing on application trends by year and detailed technology. Through this analysis, we tried to understand the detailed technologies contained in the core patents and predict the future development trends of generative AI.