• Electronics and Telecommunications Trends
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• v.36 no.1
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• pp.81-88
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• 2021

Post COVID-19, the medical legacy system will be transformed for utilizing medical resources efficiently, minimizing medical service imbalance, activating remote medical care, and strengthening private-public medical cooperation. This can be realized by achieving an entire medical paradigm shift and not simply via the application of advanced technologies such as AI. We propose a medical system configuration named "Medical AI Hub" that can realize the shift of the existing paradigm. The development stage of this configuration is categorized into "AI Cooperation Hospital," "AI Base Hospital," and "AI Hub Hospital." In the "AI Hub Hospital" stage, the medical intelligence in charge of individual patients cooperates and communicates autonomously with various medical intelligences, thereby achieving synchronous evolution. Thus, this medical intelligence supports doctors in optimally treating patients. The core technologies required during configuration development and their current R&D trends are described in this paper. The realization of the central configuration of medical AI through the development of these core technologies will induce a paradigm shift in the new medical system by innovating all medical fields with influences at the individual, society, industry, and public levels and by making the existing medical system more efficient and intelligent.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.2
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• pp.1145-1152
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• 2015

This study examined the serum lipid levels according to the obesity indices, and obtained the relationship between obesity indices and serum lipid levels among adults who underwent a health checkup in a university hospital. The subjects for this study were 4,112 adults aged 18 to 77 years who underwent medical examinations at the health center of a university hospital in Daejeon city from Jan 2012 to Dec 2013. The serum lipid levels (TC, HDL-C, LDL-C, TG, AI) and obesity indices (height, weight, waist circumference, body fat rate, BMI, WHR WSR) of the study subjects were surveyed from self-recorded questionnaires and medical examination charts of the hospital. As a result, the serum lipid levels (TC, HDL-C, LDL-C, TG, AI) of the study subjects were increased significantly with higher level of obesity indices (WC, body fat rate, BMI, WHR WSR) in both sexes. The TC, LDL-C, TG, and AI showed that positive correlated with the WC, body fat rate, BMI, and WSR in both sexes, but HDL-C was negatively correlated with the WC, body fat rate, BMI, and WSR in both sexes. The above results suggest that the obesity indices and the serum lipid levels are closely related, i.e., the serum lipid levels increase with increasing obesity indices.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.8
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• pp.5009-5022
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• 2014

The purpose of this study was to obtain the serum lipid levels according to the lifestyles, and examine the influence of lifestyles on the serum lipid levels among adults who examined the health checkup in an university hospital. The subjects for this study were 4,112 adults who underwent medical examinations at the health center of a university hospital in Daejeon city from Jan 2012 to Dec 2013. The lifestyles and serum lipid levels of study subjects were obtained from self-recorded questionnaires and medical examination charts of the hospital. As a result, the mean values of the serum lipid levels (TC, HDL-C. LDL-C and TG) and atherogenic index (AI) of the study subjects showed a significantly difference according to the lifestyle, such as age, alcohol consumption, smoking, regular exercise, overeating and meat consumption in both sexes. The TC, HDL-C. LDL-C, TG and AI showed a positive correlation with age, AUDIT score, but the HPI score showed a negative correlation in both sexes. In the age-adjusted odds ratio, the risk ratio of an abnormality of TC, HDL-C. LDL-C, TG and AI increased significantly because there was an increase in the group of everyday overeating and meat consumption, smoking group, no exercise group, and low HPI group than their respective counterparts in both sexes. The above results suggested that the serum lipid levels of the subjects was closely related to increasing age, and lifestyles, such as alcohol consumption, smoking, regular exercise, overeating, and meat consumption.

• Nutrition Research and Practice
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• v.16 no.6
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• pp.801-812
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• 2022

BACKGROUND/OBJECTIVES: Diet planning in childcare centers is difficult because of the required knowledge of nutrition and development as well as the high design complexity associated with large numbers of food items. Artificial intelligence (AI) is expected to provide diet-planning solutions via automatic and effective application of professional knowledge, addressing the complexity of optimal diet design. This study presents the results of the evaluation of the utility of AI-generated diets for children and provides related implications. MATERIALS/METHODS: We developed 2 AI solutions for children aged 3-5 yrs using a generative adversarial network (GAN) model and a reinforcement learning (RL) framework. After training these solutions to produce daily diet plans, experts evaluated the human- and AI-generated diets in 2 steps. RESULTS: In the evaluation of adequacy of nutrition, where experts were provided only with nutrient information and no food names, the proportion of strong positive responses to RL-generated diets was higher than that of the human- and GAN-generated diets (P < 0.001). In contrast, in terms of diet composition, the experts' responses to human-designed diets were more positive when experts were provided with food name information (i.e., composition information). CONCLUSIONS: To the best of our knowledge, this is the first study to demonstrate the development and evaluation of AI to support dietary planning for children. This study demonstrates the possibility of developing AI-assisted diet planning methods for children and highlights the importance of composition compliance in diet planning. Further integrative cooperation in the fields of nutrition, engineering, and medicine is needed to improve the suitability of our proposed AI solutions and benefit children's well-being by providing high-quality diet planning in terms of both compositional and nutritional criteria.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.9
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• pp.4394-4401
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• 2013

The purpose of this study was to investigate the association of blood pressures and serum lipids, obesity indices in adults who examined health checkup. Study subjects were 2,734 adults of 20 years and over (1,344 males, 1,390 females), who underwent health package check-up at an university hospital during one year of 2012. We observed the age, sex, SBP, DBP, TC, TG, HDL-C, LDL-C, AI, BMI, body fat rate and waist circumference of study subjects after confirmed IRB of the hospital. As a results, TC, TG, AI was significantly higher in the group of hypertension than that of normotensive in both sex, but in only female, HDL-C and LDL-C was higher in the each group of normotensive and hypertension. BMI, body fat rate and waist circumference was significantly higher in the group of hypertension than that of normotensive in both sex. SBP and DBP was positive correlated with TC, TG, AI, BMI, body fat rate and waist circumference, but HDL-C and LDL-C was each correlated with positive and negative in female only. Age-adjusted odds ratio for hypertension by blood serum lipids and obesity indices was significantly increased in the group of abnormal than normal of TC, TG, BMI, but HDL-C and waist circumference was each increased in the abnormal group of female, and in the abnormal group of male. Above results suggested that the blood pressure related with serum lipid levels and obesity indices.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.9
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• pp.317-322
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• 2022

The rapid development of information technology is also bringing about many changes in the medical environment. In particular, it is leading the rapid change of medical image information systems using big data and artificial intelligence (AI). The prescription delivery system (OCS), which consists of an electronic medical record (EMR) and a medical image storage and transmission system (PACS), has rapidly changed the medical environment from analog to digital. When combined with multiple solutions, PACS represents a new direction for advancement in security, interoperability, efficiency and automation. Among them, the combination with artificial intelligence (AI) using big data that can improve the quality of images is actively progressing. In particular, AI PACS, a system that can assist in reading medical images using deep learning technology, was developed in cooperation with universities and industries and is being used in hospitals. As such, in line with the rapid changes in the medical image information system in the medical environment, structural changes in the medical market and changes in medical policies to cope with them are also necessary. On the other hand, medical image information is based on a digital medical image transmission device (DICOM) format method, and is divided into a tomographic volume image, a volume image, and a cross-sectional image, a two-dimensional image, according to a generation method. In addition, recently, many medical institutions are rushing to introduce the next-generation integrated medical information system by promoting smart hospital services. The next-generation integrated medical information system is built as a solution that integrates EMR, electronic consent, big data, AI, precision medicine, and interworking with external institutions. It aims to realize research. Korea's medical image information system is at a world-class level thanks to advanced IT technology and government policies. In particular, the PACS solution is the only field exporting medical information technology to the world. In this study, along with the analysis of the medical image information system using big data, the current trend was grasped based on the historical background of the introduction of the medical image information system in Korea, and the future development direction was predicted. In the future, based on DICOM big data accumulated over 20 years, we plan to conduct research that can increase the image read rate by using AI and deep learning algorithms.

• Journal of Korean Academy of Nursing Administration
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• v.9 no.3
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• pp.495-505
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• 2003

Purpose: The purpose of this study was to develop 360 feedback for nursing unit manager based on the current personnel evaluation system and to evaluate the new tool according to evaluation subjects. Total of 277 subjects of nurse unit managers and staff nurses were participated in this study. Method: The study was conducted in three phases each for development, application, and analysis of 360 feedback. SAS program was utilized for data analysis with descriptive statistics, t-test, and analysis of variance. Result: The evaluation criteria of the developed 360 feedback tool consisted of 13 subscales such as professional knowledge, apprehension & judgement, job performance, applicability, creativity, leadership, responsibility, promptness & accuracy, administrative ability & sense of mission, activeness, cooperation, communicability, and general attitude. The internal consistency of the tool was Cronbach's alpha .939. The evaluation score by! peers(M=4.30) was the highest one, followed by self-evaluation(M=4.23), evaluation by supervisor(M=4.17), and evaluation by subordinate(M=4.10). The differences in the total evaluation scores among the subjects supervisor, self, peer, and subordinate were not statistically significant, but significant differences were found in some subscales scores. Conclusion: Further research is required to test the reliability and validity of the $360^{\circ}$ feedback tool, and to test the outcome and the process of $360^{\circ}$ feedback system.