• 한국정보디스플레이학회:학술대회논문집
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• 2009.10a
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• pp.489-492
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• 2009

We report white OLED with high color temperature based on simple stacked structure for medical display applications. White OLED was fabricated with two emitting materials of fluorescent blue dopant and phosphorescent yellow dopant. We achieved luminance efficiency of 16.2cd/A and CIE color coordinates of (0.305, 0.317) at 10mA/$cm^2$. In particular, the correlated color temperature was higher than 7,000K, enough for display applications.

• International Journal of Advanced Culture Technology
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• v.8 no.4
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• pp.212-219
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• 2020

We conducted this study to identify the accommodation applications users' lifestyle types and the composition factors for consumers' accommodation applications selection attributes and to identify the difference in the selection attributes perception of accommodation applications between groups classified by user's lifestyle types. According to the study, 6 factors were derived as the accommodation applications users' lifestyle types and were named social/leisure-oriented type, fashion-seeking type, culture-seeking type, self-examining type, self-centered type, family-oriented type. Also 6 factors were derived as the accommodation applications selection attributes and were named convenience, interactivity, economic efficiency, transaction reliability, product reliability and informativeness. Valid clusters were divided into four groups and were named culture/tourism group, self-examining group, passive and cautious group and Social and practicality-seeking group. Most of the selection attributes perception of accommodation applications between groups had statistically significant differences(p<.05), except for some items of transaction reliability. Based on the results of this study, we should strive to establish effective marketing strategies that reflect differences in the selection attributes perception of the accommodation application between groups classified by users' lifestyle types.

• Healthcare Informatics Research
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• v.24 no.4
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• pp.394-401
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• 2018

Objectives: Augmented reality (AR) technology has become rapidly available and is suitable for various medical applications since it can provide effective visualization of intricate anatomical structures inside the human body. This paper describes the procedure to develop an AR app with Unity3D and Vuforia software development kit and publish it to a smartphone for the localization of critical tissues or organs that cannot be seen easily by the naked eye during surgery. Methods: In this study, Vuforia version 6.5 integrated with the Unity Editor was installed on a desktop computer and configured to develop the Android AR app for the visualization of internal organs. Three-dimensional segmented human organs were extracted from a computerized tomography file using Seg3D software, and overlaid on a target body surface through the developed app with an artificial marker. Results: To aid beginners in using the AR technology for medical applications, a 3D model of the thyroid and surrounding structures was created from a thyroid cancer patient's DICOM file, and was visualized on the neck of a medical training mannequin through the developed AR app. The individual organs, including the thyroid, trachea, carotid artery, jugular vein, and esophagus were localized by the surgeon's Android smartphone. Conclusions: Vuforia software can help even researchers, students, or surgeons who do not possess computer vision expertise to easily develop an AR app in a user-friendly manner and use it to visualize and localize critical internal organs without incision. It could allow AR technology to be extensively utilized for various medical applications.

• Proceedings of the KSMRM Conference
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• 2000.11a
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• pp.74-74
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• 2000

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.10a
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• pp.135-138
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• 2005

Robotic devices have been widely used in many medical applications due to their accuracy and programming ability. One of the applications is a virtual reality medical simulator, which trains medical personnel in a computer generated environment. In this paper, we are going to present an application, an epidural anesthesia trainer. Because performing epidural injections is a delicate task, it demands a high level of skill and precision from the physician. This trainer uses a robotic device and computer controlled solenoid valve to recreate interaction forces between the needle and the various layers of tissues around the spinal cord. The robotic device is responsible for generation of interaction forces in real time and can be used to be haptic guidance that allows the user to follow a previous recorded expert procedure and feel the encountered forces.

• Proceedings of the KSMRM Conference
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• 2005.09a
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• pp.45-46
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• 2005

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2001.11a
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• pp.125-126
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• 2001

• The Korean Journal of Physiology and Pharmacology
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• v.28 no.5
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• pp.393-401
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• 2024

Large language models (LLMs) are rapidly transforming medical writing and publishing. This review article focuses on experimental evidence to provide a comprehensive overview of the current applications, challenges, and future implications of LLMs in various stages of academic research and publishing process. Global surveys reveal a high prevalence of LLM usage in scientific writing, with both potential benefits and challenges associated with its adoption. LLMs have been successfully applied in literature search, research design, writing assistance, quality assessment, citation generation, and data analysis. LLMs have also been used in peer review and publication processes, including manuscript screening, generating review comments, and identifying potential biases. To ensure the integrity and quality of scholarly work in the era of LLM-assisted research, responsible artificial intelligence (AI) use is crucial. Researchers should prioritize verifying the accuracy and reliability of AI-generated content, maintain transparency in the use of LLMs, and develop collaborative human-AI workflows. Reviewers should focus on higher-order reviewing skills and be aware of the potential use of LLMs in manuscripts. Editorial offices should develop clear policies and guidelines on AI use and foster open dialogue within the academic community. Future directions include addressing the limitations and biases of current LLMs, exploring innovative applications, and continuously updating policies and practices in response to technological advancements. Collaborative efforts among stakeholders are necessary to harness the transformative potential of LLMs while maintaining the integrity of medical writing and publishing.

• Korean Journal of Radiology
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• v.22 no.12
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• pp.2073-2081
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• 2021

Deep learning-based applications have great potential to enhance the quality of medical services. The power of deep learning depends on open databases and innovation. Radiologists can act as important mediators between deep learning and medicine by simultaneously playing pioneering and gatekeeping roles. The application of deep learning technology in medicine is sometimes restricted by ethical or legal issues, including patient privacy and confidentiality, data ownership, and limitations in patient agreement. In this paper, we present an open platform, MI2RLNet, for sharing source code and various pre-trained weights for models to use in downstream tasks, including education, application, and transfer learning, to encourage deep learning research in radiology. In addition, we describe how to use this open platform in the GitHub environment. Our source code and models may contribute to further deep learning research in radiology, which may facilitate applications in medicine and healthcare, especially in medical imaging, in the near future. All code is available at https://github.com/mi2rl/MI2RLNet.

• Genomics & Informatics
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• v.11 no.4
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• pp.200-210
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• 2013

Studying biological networks, such as protein-protein interactions, is key to understanding complex biological activities. Various types of large-scale biological datasets have been collected and analyzed with high-throughput technologies, including DNA microarray, next-generation sequencing, and the two-hybrid screening system, for this purpose. In this review, we focus on network-based approaches that help in understanding biological systems and identifying biological functions. Accordingly, this paper covers two major topics in network biology: reconstruction of gene regulatory networks and network-based applications, including protein function prediction, disease gene prioritization, and network-based genome-wide association study.