• 중환자간호학회지
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• 제12권3호
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• pp.13-23
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• 2019

Purpose : The purpose of this study was to determine whether appreciative inquiry (AI) is an effective intervention for increasing the positive psychological capital and organizational commitment of new nurses. Method : The study used a nonequivalent control group pretest-posttest design. The participants were 60 new nurses in a tertiary hospital in Seoul. The experimental group received 2 classes of AI education and in-unit AI activities. The control group received the existing education program. Results : There was no statistically significant difference in the positive psychological capital and organizational commitment between the experimental group and the control group over time. Satisfaction with the AI education scored 3.69, which was higher than the average. The reason why the experimental group members were satisfied with the program was that AI education helped them to adapt and the in-unit AI activities made staff more cooperative and the atmosphere of the unit more positive. Conclusion : When applying AI activities to new nurses to promote positive psychological capital and organizational commitment, it is necessary to provide a workshop in which the participants can fully concentrate on education and to extend the period of use to one year in order to maintain the effect of AI activities.

• Journal of Veterinary Science
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• 제24권1호
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• pp.5.1-5.16
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• 2023

The H9N2 avian influenza (AI) has become endemic in poultry in many countries since the 1990s, which has caused considerable economic losses in the poultry industry. Considering the long history of the low pathogenicity H9N2 AI in many countries, once H9N2 AI is introduced, it is more difficult to eradicate than high pathogenicity AI. Various preventive measures and strategies, including vaccination and active national surveillance, have been used to control the Y439 lineage of H9N2 AI in South Korea, but it took a long time for the H9N2 virus to disappear from the fields. By contrast, the novel Y280 lineage of H9N2 AI was introduced in June 2020 and has spread nationwide. This study reviews the history, genetic and pathogenic characteristics, and control strategies for Korean H9N2 AI. This review may provide some clues for establishing control strategies for endemic AIV and a newly introduced Y280 lineage of H9N2 AI in South Korea.

• 한국산업보건학회지
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• 제33권4호
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• pp.412-418
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• 2023

As advances in artificial intelligence (AI) increasingly approach areas once relegated to the realm of science fiction, there is growing public interest in using these technologies for practical everyday tasks in both the home and the workplace. This paper explores the applications of and implications for of using ChatGPT, a conversational AI model based on GPT-3.5 and GPT-4.0, in the field of occupational health and safety. After gaining over one million users within five days of its launch, ChatGPT has shown promise in addressing issues ranging from emergency response to chemical exposure to recommending personal protective equipment. However, despite its potential usefulness, the integration of AI into scientific work and professional settings raises several concerns. These concerns include the ethical dimensions of recognizing AI as a co-author in academic publications, the limitations and biases inherent in the data used to train these models, legal responsibilities in professional contexts, and potential shifts in employment following technological advances. This paper aims to provide a comprehensive overview of these issues and to contribute to the ongoing dialogue on the responsible use of AI in occupational health and safety.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 춘계학술대회
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• pp.12-14
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• 2021

For the past couple of years, the medical data has been stored in centralized systems which is not the ideal storage technique since all data can be altered, stolen, or even used for evil purposes and, furthermore, the data cannot be safely shared with other doctors and hospitals in case of patient's transfer, change of state or country, in addition, patient's health status cannot be tracked and the patient's medical history is unknown. Therefore, powerful decentralized technologies and expertise can help provide better health information and help doctors and patients to better understand the situations before and after treatment, and do more research based on immutable and trusted data. One of the proposed solutions is storing and securing data on the blockchain which is less scalable, slow and expensive. Introducing a scalable, robust medical data storage and sharing system based on AI/ML, IoT, IPFS, and blockchain.

• 한국진공학회지
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• 제6권S1호
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• pp.16-22
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• 1997

ion beam processing of materials for medical application has gained increasing interest in the last decade and the implantation of nitrogen into TI-6AI-4V to improve corrosive-wear performance is currently used for processing of total hip and knee joints. Oxides and nitrides of Ti, Zr, Al, Cr were deposited on TI-6AI-4V substrates by DC magnetron sputtering dual ion beam sputtering and ion beam assisted deposition. The cytotoxicity of these films were investigated by MTT method and showed comparable to untreated TI-6AI-4V Plasm-sprayed hydroxyapatite(HAp) coatings showed excellent cytotoxicity regardless of heat treatment. intermediate layer coatings of nitrides and oxides increased the bond strength of HAp to substrate by intrdducing chemical bond at interface. Heat treatment of HAp coatings also improved the chemical bond at interfaces and increased the bond strength of untreated TI-6AI-4V to 16.4 kg/$\textrm{cm}^2$ but still lower than 33.1 kg./$\textrm{cm}^2$ of ir oxide as a imtermediate layer caoting.

• 항공우주의학회지
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• 제29권2호
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• pp.59-66
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• 2019

Artificial Intelligence (AI) born in 1956 is a general term that implies the use of a computer to make intelligent machines with minimal human intervention. AI is a topic dominating diverse discussions on the future of professional employment, change in the social standard and economic performance. In this paper, I describe fundamental concepts underlying AI and their significance to various fields including aviation and medicine. I highlight issues involved and describe the potential impacts and challenges to the industrial fields. While many benefits are expected in human life with AI integration, problems are needed to be identified and discussed with respect to ethical issues and the future roles of professionals and specialists for their wider application of AI.

• Investigative Magnetic Resonance Imaging
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• 제26권1호
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• pp.1-9
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• 2022

Artificial intelligence (AI) powered by deep learning (DL) has shown remarkable progress in image recognition tasks. Over the past decade, AI has proven its feasibility for applications in medical imaging. Various aspects of clinical practice in neuroimaging can be improved with the help of AI. For example, AI can aid in detecting brain metastases, predicting treatment response of brain tumors, generating a parametric map of dynamic contrast-enhanced MRI, and enhancing radiomics research by extracting salient features from input images. In addition, image quality can be improved via AI-based image reconstruction or motion artifact reduction. In this review, we summarize recent clinical applications of DL in various aspects of neuroimaging.

• 한국인공지능학회지
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• 제9권2호
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• pp.15-21
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• 2021

This paper conducted a feasibility study by applying an Internet of Things and Artificial intelligence-based management system for the elderly living alone in an aging society. The number of single-person families over the age of 50 is expected to increase, and problems such as health, safety, and loneliness may occur due to aging. Therefore, by establishing an IoT-based care system for the elderly living alone, a stable service was developed through securing a rapid response system for the elderly living alone and automatically reporting 119. The participants of the demonstration test were subjects under the jurisdiction of the "Seongnam Senior Complex," and the data collection rate between the IoT sensor and the emergency safety gateway was high. During the demonstration period, as a result of evaluating the satisfaction of the IoT-based care system for the elderly living alone, 90 points were achieved. We are currently in the COVID-19 situation. Therefore, the number of elderly living alone is continuously increasing, and the number of people who cannot benefit from care services will continue to occur. Also, even if the COVID-19 situation is over, the epidemic will happen again. So the care system is essential. The elderly care system developed in this way will provide safety management services based on artificial intelligence-based activity pattern analysis, improving the quality of in-house safety services.

• 스마트미디어저널
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• 제10권2호
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• pp.22-29
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• 2021

In the past decade, deep learning has been applied to various medical image analysis tasks. Skeletal bone age estimation is clinically important as it can help prevent age-related illness and pave the way for new anti-aging therapies. Recent research has applied deep learning techniques to the task of bone age assessment and achieved positive results. In this paper, we propose a bone age prediction method using a deep convolutional neural network. Specifically, we first train a classification model that automatically localizes the most discriminative region of an image and crops it from the original image. The regions of interest are then used as input for a regression model to estimate the age of the patient. The experiments are conducted on a whole-body scintigraphy dataset that was collected by Chonnam National University Hwasun Hospital. The experimental results illustrate the potential of our proposed method, which has a mean absolute error of 3.35 years. Our proposed framework can be used as a robust supporting tool for clinicians to prevent age-related diseases.

• 한국인공지능학회지
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• 제12권1호
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• pp.1-6
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• 2024

Recently, various techniques are being applied through the development of medical AI, and research has been conducted on the application of super-resolution AI models. In this study, evaluate the results of the application of the super-resolution AI model to brain CT as the basic data for future research. Acquiring CT images of the brain, algorithm for brain and bone windowing setting, and the resolution was downscaled to 5 types resolution image based on the original resolution image, and then upscaled to resolution to create an LR image and used for network input with the original imaging. The SRCNN model was applied to each of these images and analyzed using PSNR, SSIM, Loss. As a result of quantitative index analysis, the results were the best at 256×256, the brain and bone window setting PSNR were the same at 33.72, 35.2, and SSIM at 0.98 respectively, and the loss was 0.0004 and 0.0003, respectively, showing relatively excellent performance in the bone window setting CT image. The possibility of future studies aimed image quality and exposure dose is confirmed, and additional studies that need to be verified are also presented, which can be used as basic data for the above studies.