• Clinical Endoscopy
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• v.55 no.5
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• pp.594-604
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• 2022

Over the past decade, technological advances in deep learning have led to the introduction of artificial intelligence (AI) in medical imaging. The most commonly used structure in image recognition is the convolutional neural network, which mimics the action of the human visual cortex. The applications of AI in gastrointestinal endoscopy are diverse. Computer-aided diagnosis has achieved remarkable outcomes with recent improvements in machine-learning techniques and advances in computer performance. Despite some hurdles, the implementation of AI-assisted clinical practice is expected to aid endoscopists in real-time decision-making. In this summary, we reviewed state-of-the-art AI in the field of gastrointestinal endoscopy and offered a practical guide for building a learning image dataset for algorithm development.

• Journal of Venture Innovation
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• v.7 no.2
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• pp.143-152
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• 2024

Recently, as a lot of software with AI functions has been developed, the number of software products with various prediction functions is increasing, and as a result, the importance of software quality has increased. In particular, as consideration for functional safety of products with AI functions increases, software quality management is being conducted at a national level. In particular, the GS Quality Certification System is a quality certification system for software products that is being implemented at the national level, and the GS Certification System is also researching quality evaluation methods for AI products. In this study, we attempt to present an evaluation model that satisfies the basic conditions of software quality based on international standards among the various quality evaluation models presented to verify software reliability. Considering the software quality characteristics of the artificial intelligence sector, we study quality evaluation models, diagnose quality, and predict failures. .In this study, we propose an international standard model for artificial intelligence based on the software reliability growth model, present an evaluation model, and present a method for quality diagnosis through the model. In this respect, this study is considered to be important in that it can predict failures in advance and find failures in advance to prevent risks by predicting the failure time that will occur in software in the future. In particular, it is believed that predicting failures will be important in various safety-related software.

• Education of Primary School Mathematics
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• v.26 no.4
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• pp.333-348
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• 2023

Clear analysis and diagnosis of various characteristic factors of individual students is the most important in order to realize individual customized teaching and learning, which is considered the most essential function of math artificial intelligence-based digital textbooks. In this study, analysis factors and tools for individual customized learning diagnosis and construction models for data collection and analysis were derived from mathematical AI digital textbooks. To this end, according to the Ministry of Education's recent plan to apply AI digital textbooks, the demand for AI digital textbooks in mathematics, personalized learning and prior research on data for it, and factors for learner analysis in mathematics digital platforms were reviewed. As a result of the study, the researcher summarized the factors for learning analysis as factors for learning readiness, process and performance, achievement, weakness, and propensity analysis as factors for learning duration, problem solving time, concentration, math learning habits, and emotional analysis as factors for confidence, interest, anxiety, learning motivation, value perception, and attitude analysis as factors for learning analysis. In addition, the researcher proposed noon data on the problem, learning progress rate, screen recording data on student activities, event data, eye tracking device, and self-response questionnaires as data collection tools for these factors. Finally, a data collection model was proposed that time-series these factors before, during, and after learning.

• Parasites, Hosts and Diseases
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• v.46 no.4
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• pp.261-263
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• 2008

Strongyloides stercoralis is a human intestinal parasite which may lead to complicated strongyloidiasis in immunocompromised. Here, a case of complicated strongyloidiasis in a patient with chronic lymphocytic leukemia is reported. Presence of numerous S. stercoralis larvae in feces and sputum confirmed the diagnosis of hyperinfection syndrome in this patient. Following recovery of filariform larvae from agar plate culture of the stool, the isolate was characterized for the ITS1 region of ribosomal DNA gene by nested-PCR and sequencing. Albendazole therapy did not have cure effects; and just at the beginning of taking ivermectin, the patient died. The most important clue to prevent such fatal consequences is early diagnosis and proper treatment.

• Korean Medical Education Review
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• v.22 no.2
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• pp.99-114
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• 2020

The rapid development of artificial intelligence (AI), including deep learning, has led to the development of technologies that may assist in the diagnosis and treatment of diseases, prediction of disease risk and prognosis, health index monitoring, drug development, and healthcare management and administration. However, in order for AI technology to improve the quality of medical care, technical problems and the efficacy of algorithms should be evaluated in real clinical environments rather than the environment in which algorithms are developed. Further consideration should be given to whether these models can improve the quality of medical care and clinical outcomes of patients. In addition, the development of regulatory systems to secure the safety of AI medical technology, the ethical and legal issues related to the proliferation of AI technology, and the impacts on the relationship with patients also need to be addressed. Systematic training of healthcare personnel is needed to enable adaption to the rapid changes in the healthcare environment. An overall review and revision of undergraduate medical curriculum is required to enable extraction of significant information from rapidly expanding medical information, data science literacy, empathy/compassion for patients, and communication among various healthcare providers. Specialized postgraduate AI education programs for each medical specialty are needed to develop proper utilization of AI models in clinical practice.

• Educational Technology International
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• v.25 no.2
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• pp.297-329
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• 2024

The recent advancements of AI-based educational platforms are opening up a new era for personalized learning as an alternative for future education. This study explores the structures of personalized learning in the current AI-based educational platforms to interpret their meaning from the perspective of Learned Curriculum. For this, three leading AI-based educational platforms, Classting (Korea), Squirrel AI Learning (China), and Khan Academy (USA) were described with a focus on the personalized contents, methods, and pace to meet students' needs. The results are as follows: First, the personalized contents offered by the platforms are the sequenced contents within the total content structure that students are required to learn; Second, student choice for learning methods is partially being provided within the platforms; Lastly, personalized learning provided by the platforms ultimately means the personalization of pacing based on assessment results. Consequently, the discourse surrounding personalized learning provided by AI-based educational platforms needs to expand beyond personalization within the predetermined content areas to encompass curriculum-level openness. Moreover, the "students' needs" used for diagnosis for personalization should include not only the assessment results of Given Curriculum contents, but also the students' personal interests or goals as guiding objectives for each student's Learned Curriculum.

• Journal of IKEEE
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• v.27 no.4
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• pp.601-609
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• 2023

In this paper, we propose an artificial intelligence-based high-speed fault diagnosis method using an FPGA in the event of a ground fault in a DC system. When applying artificial intelligence algorithms to fault diagnosis, a substantial amount of computation and real-time data processing are required. By employing an FPGA with AI-based high-speed fault diagnosis, the DC breaker can operate more rapidly, thereby reducing the breaking capacity of the DC breaker. therefore, in this paper, an intelligent high-speed diagnosis algorithm was implemented by collecting fault data through fault simulation of a DC system using Matlab/Simulink. Subsequently, the proposed intelligent high-speed fault diagnosis algorithm was applied to the FPGA, and performance verification was conducted.

• Korean Journal of Radiology
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• v.22 no.3
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• pp.442-453
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• 2021

Artificial intelligence (AI) will likely affect various fields of medicine. This article aims to explain the fundamental principles of clinical validation, device approval, and insurance coverage decisions of AI algorithms for medical diagnosis and prediction. Discrimination accuracy of AI algorithms is often evaluated with the Dice similarity coefficient, sensitivity, specificity, and traditional or free-response receiver operating characteristic curves. Calibration accuracy should also be assessed, especially for algorithms that provide probabilities to users. As current AI algorithms have limited generalizability to real-world practice, clinical validation of AI should put it to proper external testing and assisting roles. External testing could adopt diagnostic case-control or diagnostic cohort designs. A diagnostic case-control study evaluates the technical validity/accuracy of AI while the latter tests the clinical validity/accuracy of AI in samples representing target patients in real-world clinical scenarios. Ultimate clinical validation of AI requires evaluations of its impact on patient outcomes, referred to as clinical utility, and for which randomized clinical trials are ideal. Device approval of AI is typically granted with proof of technical validity/accuracy and thus does not intend to directly indicate if AI is beneficial for patient care or if it improves patient outcomes. Neither can it categorically address the issue of limited generalizability of AI. After achieving device approval, it is up to medical professionals to determine if the approved AI algorithms are beneficial for real-world patient care. Insurance coverage decisions generally require a demonstration of clinical utility that the use of AI has improved patient outcomes.

• International Journal of Advanced Culture Technology
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• v.9 no.4
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• pp.274-281
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• 2021

In the field of artificial intelligence education, discussions on the direction of artificial intelligence education are actively underway, and it is necessary to establish a foundation for future information education. It is necessary to design a creative convergence teaching-learning and evaluation method. Although AI experience coding education has been applied, the evaluation stage is insufficient. In this paper, we propose an evaluation system that can verify the validity of the proposed education model to find a way to supplement the existing learning module. The core components of this proposed system are Assessment-Factor, Self-Diagnosis, Item Bank, and Evaluation Result modules, which are designed to enable system access according to the roles of administrator, instructors and learners. This system enables individualized learning through online and offline connection.

• Journal of the Korean Society of Radiology
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• v.17 no.6
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• pp.873-879
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• 2023

This study explored the use of artificial intelligence(AI) to detect foreign bodies in chest X-ray images. Medical imaging, especially chest X-rays, plays a crucial role in diagnosing diseases such as pneumonia and lung cancer. With the increase in imaging tests, AI has become an important tool for efficient and fast diagnosis. However, images can contain foreign objects, including everyday jewelry like buttons and bra wires, which can interfere with accurate readings. In this study, we developed an AI algorithm that accurately identifies these foreign objects and processed the National Institutes of Health chest X-ray dataset based on the YOLOv8 model. The results showed high detection performance with accuracy, precision, recall, and F1-score all close to 0.91. Despite the excellent performance of AI, the study solved the problem that foreign objects in the image can distort the reading results, emphasizing the innovative role of AI in radiology and its reliability based on accuracy, which is essential for clinical implementation.