• Korean Journal of Radiology
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• v.25 no.7
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• pp.613-622
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• 2024

Objective: In Korea, radiology has been positioned towards the early adoption of artificial intelligence-based software as medical devices (AI-SaMDs); however, little is known about the current usage, implementation, and future needs of AI-SaMDs. We surveyed the current trends and expectations for AI-SaMDs among members of the Korean Society of Radiology (KSR). Materials and Methods: An anonymous and voluntary online survey was open to all KSR members between April 17 and May 15, 2023. The survey was focused on the experiences of using AI-SaMDs, patterns of usage, levels of satisfaction, and expectations regarding the use of AI-SaMDs, including the roles of the industry, government, and KSR regarding the clinical use of AI-SaMDs. Results: Among the 370 respondents (response rate: 7.7% [370/4792]; 340 board-certified radiologists; 210 from academic institutions), 60.3% (223/370) had experience using AI-SaMDs. The two most common use-case of AI-SaMDs among the respondents were lesion detection (82.1%, 183/223), lesion diagnosis/classification (55.2%, 123/223), with the target imaging modalities being plain radiography (62.3%, 139/223), CT (42.6%, 95/223), mammography (29.1%, 65/223), and MRI (28.7%, 64/223). Most users were satisfied with AI-SaMDs (67.6% [115/170, for improvement of patient management] to 85.1% [189/222, for performance]). Regarding the expansion of clinical applications, most respondents expressed a preference for AI-SaMDs to assist in detection/diagnosis (77.0%, 285/370) and to perform automated measurement/quantification (63.5%, 235/370). Most respondents indicated that future development of AI-SaMDs should focus on improving practice efficiency (81.9%, 303/370) and quality (71.4%, 264/370). Overall, 91.9% of the respondents (340/370) agreed that there is a need for education or guidelines driven by the KSR regarding the use of AI-SaMDs. Conclusion: The penetration rate of AI-SaMDs in clinical practice and the corresponding satisfaction levels were high among members of the KSR. Most AI-SaMDs have been used for lesion detection, diagnosis, and classification. Most respondents requested KSR-driven education or guidelines on the use of AI-SaMDs.

• Journal of the Korea Society of Computer and Information
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• v.28 no.7
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• pp.29-37
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• 2023

Due to climate change, interest in crop production and distribution is increasing, and attempts are being made to use bigdata and AI to predict production volume and control shipments and distribution stages. Prediction of agricultural product imports not only affects prices, but also controls shipments of farms and distributions of distribution companies, so it is important information for establishing marketing strategies. In this paper, we create an artificial intelligence prediction model that predicts the future import volume based on the wholesale market melon import volume data disclosed by the agricultural statistics information system and evaluate its accuracy. We create prediction models using three models: the Neural Prophet technique, the Ensembled Neural Prophet model, and the GRU model. As a result of evaluating the performance of the model by comparing two major indicators, MAE and RMSE, the Ensembled Neural Prophet model predicted the most accurately, and the GRU model also showed similar performance to the ensemble model. The model developed in this study is published on the web and used in the field for 1 year and 6 months, and is used to predict melon production in the near future and to establish marketing and distribution strategies.

• Journal of the Korea Computer Graphics Society
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• v.28 no.3
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• pp.23-29
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• 2022

Virtual humans are implemented with dedicated modeling tools like Unity 3D Engine in virtual space (virtual reality, mixed reality, metaverse, etc.). Various human modeling tools have been introduced to implement virtual human-like appearance, voice, expression, and behavior similar to real people, and virtual humans implemented via these tools can communicate with users to some extent. However, most of the virtual humans so far have stayed unimodal using only text or speech. As AI technologies advance, the outdated machine-centered dialogue system is now changing to a human-centered, natural multi-modal system. By using several pre-trained networks, we implemented an emotion-based multi-modal dialogue system, which generates human-like utterances and displays appropriate facial expressions in real-time.

• Journal of the Korean Society of Radiology
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• v.14 no.2
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• pp.121-127
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• 2020

The era of the Fourth Industrial Revolution is increasingly demanding mathematical competencies for virtual reality (VR), artificial intelligence (AI) and the like. In this context, this study intended to identify the basic mathematical competency levels of university freshman students in radiology department and to provide basic data thereon. For this, the diagnostic assessment of basic learning competencies for the domain of mathematics was conducted from June 17, 2019 to June 28, 2019 among 78 freshman students of radiology department at S university and D university. As a result, the university students' overall basic mathematical competency levels were diagnosed to be excellent. However, their levels in the sectors of the geometry and vector and the probability and statistics were diagnosed to be moderate, with the mean scores of 2.61 points and 2.64 points, respectively, which were found to be lower than those of the other sections. As for basic mathematical competency levels according to genders, the levels of male students and female students were diagnosed to be excellent, with the mean scores of 17.48 points and 16.29 points, respectively, showing no statistically significant difference (p>0.05). Given the small number of subjects and regional restriction, there might be some limitations in the generalization of the findings of the present study to all university freshman students and all departments. The above results suggest that it is necessary to implement various programs such as student level-based special lectures for enhancing basic mathematical competencies relating to major in order to improve the basic mathematical competencies of freshman students in radiology department, and that it is necessary to increase the students' mathematical competencies by offering major math courses in the curriculum and applying teaching-learning methods matching students' levels.