• 한국인공지능학회지
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• 제11권3호
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• pp.23-27
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• 2023

The purpose of this study is to identify and analyze suicide factors of adolescents using the Random Forest algorithm. According to statistics on the cause of death by the National Statistical Office in 2019, suicide was the highest cause of death in the 10-19 age group, which is a major social problem. Using machine learning algorithms, research can predict whether individual adolescents think of suicide without investigating suicidal ideation and can contribute to protecting adolescents and analyzing factors that affect suicide, establishing effective intervention measures. As a result of predicting with the random forest algorithm, it can be said that the possibility of identifying and predicting suicide factors of adolescents was confirmed. To increase the accuracy of the results, continuous research on the factors that induce youth suicide is necessary.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2021년도 추계학술발표대회
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• pp.805-807
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• 2021

In-hospital cardiac arrest is a significant problem for medical systems. Although the traditional early warning systems have been widely applied, they still contain many drawbacks, such as the high false warning rate and low sensitivity. This paper proposed a strategy that involves a deep learning approach based on a novel interpretable deep tabular data learning architecture, named TabNet, for the Rapid Response System. This study has been processed and validated on a dataset collected from two hospitals of Chonnam National University, Korea, in over 10 years. The learning metrics used for the experiment are the area under the receiver operating characteristic curve score (AUROC) and the area under the precision-recall curve score (AUPRC). The experiment on a large real-time dataset shows that our method improves compared to other machine learning-based approaches.

• Korean Journal of Radiology
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• 제25권5호
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• pp.493-498
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• 2024

• Archives of Plastic Surgery
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• 제51권2호
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• pp.262-264
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• 2024

• 한국의학물리학회지:의학물리
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• 제30권2호
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• pp.49-58
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• 2019

Purpose: Automated analytical systems have begun to emerge as a database system that enables the scanning of medical images to be performed on computers and the construction of big data. Deep-learning artificial intelligence (AI) architectures have been developed and applied to medical images, making high-precision diagnosis possible. Materials and Methods: For diagnosis, the medical images need to be labeled and standardized. After pre-processing the data and entering them into the deep-learning architecture, the final diagnosis results can be obtained quickly and accurately. To solve the problem of overfitting because of an insufficient amount of labeled data, data augmentation is performed through rotation, using left and right flips to artificially increase the amount of data. Because various deep-learning architectures have been developed and publicized over the past few years, the results of the diagnosis can be obtained by entering a medical image. Results: Classification and regression are performed by a supervised machine-learning method and clustering and generation are performed by an unsupervised machine-learning method. When the convolutional neural network (CNN) method is applied to the deep-learning layer, feature extraction can be used to classify diseases very efficiently and thus to diagnose various diseases. Conclusions: AI, using a deep-learning architecture, has expertise in medical image analysis of the nerves, retina, lungs, digital pathology, breast, heart, abdomen, and musculo-skeletal system.

• Korean Journal of Radiology
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• 제24권11호
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• pp.1061-1080
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• 2023

Artificial intelligence (AI) in radiology is a rapidly developing field with several prospective clinical studies demonstrating its benefits in clinical practice. In 2022, the Korean Society of Radiology held a forum to discuss the challenges and drawbacks in AI development and implementation. Various barriers hinder the successful application and widespread adoption of AI in radiology, such as limited annotated data, data privacy and security, data heterogeneity, imbalanced data, model interpretability, overfitting, and integration with clinical workflows. In this review, some of the various possible solutions to these challenges are presented and discussed; these include training with longitudinal and multimodal datasets, dense training with multitask learning and multimodal learning, self-supervised contrastive learning, various image modifications and syntheses using generative models, explainable AI, causal learning, federated learning with large data models, and digital twins.

• 한국인공지능학회지
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• 제12권3호
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• pp.33-39
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• 2024

In this paper, we explore the application of Kodaly hand signs in enhancing children's music education, performances, and auditory assistance technologies. This research focuses on improving the recognition rate of Multilayer Perceptron (MLP) models in identifying Kodaly hand sign scales through the integration of Artificial Neural Networks (ANN). We developed an enhanced MLP model by augmenting it with additional parameters and optimizing the number of hidden layers, aiming to substantially increase the model's accuracy and efficiency. The augmented model demonstrated a significant improvement in recognizing complex hand sign sequences, achieving a higher accuracy compared to previous methods. These advancements suggest that our approach can greatly benefit music education and the development of auditory assistance technologies by providing more reliable and precise recognition of Kodaly hand signs. This study confirms the potential of parameter augmentation and hidden layers optimization in refining the capabilities of neural network models for practical applications.

• 한국인공지능학회지
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• 제2권1호
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• pp.4-6
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• 2014

These days, aging, the aged and patients rapidly increased to produce problems, for instance, rapid increase of demand on medical service, higher medical expenses, low quality of the elderly's lives, shortage of physicians and nurses, and others [1]. These days, not only IT technology but also medical technology has taken the lead in settlement of the problems. Patients see a doctor to be given medical treatment and service when they are sick to have difficulty. The study investigated lifestyle monitoring system of chronic disease patients to indicate variation depending upon time. The health care is likely to solve problems of the elderly and chronic disease patients and to satisfy desire of better life quality by living healthy life and to diagnose diseases and give medical treatment and to give solutions in accordance with changes of paradigm of medical services.

• 의학교육논단
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• 제18권2호
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• pp.58-64
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• 2016

The birth of the scientific revolution, brought forth by Vesalius and Copernicus in 1543, marked the beginning of a new age. However, the changes such as treatment effectiveness, survival rate, prevalence of specific diseases, etc. had not yet become clear during the 16th century. In the early 17th century, Boerhaave emphasized bedside teaching and practice. His attitude influenced numerous students and educators, so many medical students visited hospital wards where he worked. From the late 18th to 19th centuries, Jenner's smallpox vaccination, Pasteur's anthrax and rabies vaccinations, and Koch's four postulates used to detect pathogens were developed using the scientific research method, which initiated big changes for medicine. Flexner, credited for reporting the new medical education system, adopted scientific medicine. He believed medical students must study basic medical science since it could be the foundation of clinical medicine and lead to a revolution in the field. He proposed a new medical curriculum composed of two-years of basic medicine and two-years of clinical medicine, which has been used more than 100 years. During the late 20th century, bedside teaching rounds decreased gradually as scientific medicine has become popular. Many medical educators in many articles have proposed bedside education as an effective method for medical learning. Despite the advent of the age of artificial intelligence and the changing of medical environments in the near future, bedside education will be more useful and important for medical students, educators, and patients as it is a traditional method and essential for patients who desire a more personal approach.

• Journal of Medicine and Life Science
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• 제20권2호
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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.