• Korean Journal of Radiology
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• v.23 no.12
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• pp.1241-1250
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• 2022

Objective: To conduct a simulation study to determine whether artificial intelligence (AI)-aided mammography reading can reduce unnecessary recalls while maintaining cancer detection ability in women recalled after mammography screening. Materials and Methods: A retrospective reader study was performed by screening mammographies of 793 women (mean age ± standard deviation, 50 ± 9 years) recalled to obtain supplemental mammographic views regarding screening mammography-detected abnormalities between January 2016 and December 2019 at two screening centers. Initial screening mammography examinations were interpreted by three dedicated breast radiologists sequentially, case by case, with and without AI aid, in a single session. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and recall rate for breast cancer diagnosis were obtained and compared between the two reading modes. Results: Fifty-four mammograms with cancer (35 invasive cancers and 19 ductal carcinomas in situ) and 739 mammograms with benign or negative findings were included. The reader-averaged AUC improved after AI aid, from 0.79 (95% confidence interval [CI], 0.74-0.85) to 0.89 (95% CI, 0.85-0.94) (p < 0.001). The reader-averaged specificities before and after AI aid were 41.9% (95% CI, 39.3%-44.5%) and 53.9% (95% CI, 50.9%-56.9%), respectively (p < 0.001). The reader-averaged sensitivity was not statistically different between AI-unaided and AI-aided readings: 89.5% (95% CI, 83.1%-95.9%) vs. 92.6% (95% CI, 86.2%-99.0%) (p = 0.053), although the sensitivities of the least experienced radiologists before and after AI aid were 79.6% (43 of 54 [95% CI, 66.5%-89.4%]) and 90.7% (49 of 54 [95% CI, 79.7%-96.9%]), respectively (p = 0.031). With AI aid, the reader-averaged recall rate decreased by from 60.4% (95% CI, 57.8%-62.9%) to 49.5% (95% CI, 46.5%-52.4%) (p < 0.001). Conclusion: AI-aided reading reduced the number of recalls and improved the diagnostic performance in our simulation using women initially recalled for supplemental mammographic views after mammography screening.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.3
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• pp.463-468
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• 2022

COVID-19 is a complex disaster, not an infectious disease-level disaster, and it is difficult to respond with the existing disaster response management method. As a result, experiencing psychological stress and trauma such as 'Corona Blue' has emerged as a new social problem. This study examined the changes in non-face-to-face counseling services using ICT technology and the application cases of image, AI, and VR by companies as the transition to the digital economy accelerates. Based on this, disaster psychological recovery support services were considered to improve the psychological recovery and quality of life of the people after a disaster by establishing a more efficient counseling system and developing counseling services using ICT technology.

• Industry Promotion Research
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• v.7 no.3
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• pp.17-26
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• 2022

As the well-being mindset to keep our minds and bodies free and healthy more than anything else in the society we live in is spreading, the meaning of health care has become a key part of the 4th industrial revolution such as big data, IoT, AI, and block chain. The advancement of the advanced medical information service industry is being promoted by utilizing convergence technology. In digital healthcare, the development of intelligent information technology such as artificial intelligence, big data, and cloud is being promoted as a digital transformation of the traditional medical and healthcare industry. In addition, due to rapid development in the convergence of science and technology environment, various issues such as health, medical care, welfare, etc., have been gradually expanded due to social change. Therefore, in this study, first, the general meaning and current status of digital health care medical information is examined, and then, developmental tasks to activate digital health care medical information are analyzed and reviewed. The purpose of this article is to improve usability to fully pursue our human freedom.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.1-7
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• 2023

The advent of deep learning technologies has led to the development of various medical applications, making healthcare services more convenient and effective. Among these applications, heart rate estimation is considered a vital method for assessing an individual's health. Traditional methods, such as photoplethysmography through smart watches, have been widely used but are invasive and require additional hardware. Recent advancements allow for contactless heart rate estimation through facial image analysis, providing a more hygienic and convenient approach. In this paper, we propose a lightweight methodology capable of accurately estimating heart rate in mobile environments, using a specialized 2-channel network structure based on 2D convolution. Our method considers both subtle facial movements and color changes resulting from blood flow and muscle contractions. The approach comprises two major components: an Encoder for analyzing image features and a regression layer for evaluating Blood Volume Pulse. By incorporating both features simultaneously our methodology delivers more accurate results even in computing environments with limited resources. The proposed approach is expected to offer a more efficient way to monitor heart rate without invasive technology, particularly well-suited for mobile devices.

• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.199-208
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• 2023

The emergence of COVID-19 virus has shaken almost every aspect of human life including but not limited to social, financial, and economic changes. One of the most significant impacts was obviously healthcare. Now though the pandemic has been over, its aftereffects are still there. Among them, a prominent one is people lifestyle. Work from home, enhanced screen time, limited mobility and walking habits, junk food, lack of sleep etc. are several factors that have still been affecting human health. Consequently, diseases like diabetes, high blood pressure, anxiety etc. have been emerging at a speed never witnessed before and it mainly includes the people at young age. The situation demands an early prediction, detection, and warning system to alert the people at risk. AI and Machine learning has been investigated tremendously for solving the problems in almost every aspect of human life, especially healthcare and results are promising. This study focuses on reviewing the machine learning based approaches conducted in detection and prediction of diabetes especially during and post pandemic era. That will help find a research gap and significance of the study especially for the researchers and scholars in the same field.

• Journal of Internet Computing and Services
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• v.25 no.5
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• pp.13-22
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• 2024

This study proposes a Korean model for a smart home telehealth service, utilizing advanced ICT technologies such as 5G, artificial intelligence, blockchain, and big data, to effectively manage and treat chronic diseases and mental health-related illnesses in an aging society. By improving the existing telehealth system, which is limited to phone consultations and prescription deliveries, this model integrates various health data, mental health information, and emergency detection data, and includes functions for prescription drug management and welfare counseling, creating an all-in-one system. This model is expected to enhance accessibility for vulnerable populations, reduce medical costs, and increase the efficiency of health management, and contribute to the prevention of solitary deaths.

• Journal of the Health Care and Life Science
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• v.11 no.1
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• pp.67-77
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• 2023

This paper aims as a prior study to cultivate artificial intelligence professionals at the level of colleges in the future by analyzing healthcare services and technologies using artificial intelligence technology. As artificial intelligence technology is recognized as a key engine or core technology in the future that will create national competitiveness and added value, advanced countries are investing a lot of attention and support in developing technologies as well as human resources at the national level. Korea is also promoting national-level R&D manpower training projects such as AI graduate program support projects, and investing heavily in fostering and securing its own artificial intelligence personnel, mainly by large companies, but there is a lack of artificial intelligence experts. This study analyzes the current status of healthcare services and technologies, industries, and artificial intelligence manpower training using artificial intelligence technology, and proposes directions for fostering artificial intelligence personnel at the level of colleges.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.6
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• pp.1207-1212
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• 2022

Realistic media, the next-generation media technology in the era of the 4th industrial revolution, is becoming an issue as a technology to experience through an environment that optimizes user experience, especially! It is rapidly developing into the health and healthcare convergence and complex fields. Realistic media technologies and services are being adopted to solve the problems of the increase in chronic diseases due to the increase in the elderly population and the lack of infrastructure and professional manpower in the fields of cognitive training and rehabilitation. Therefore, in this study, a cognitive training system was designed and implemented for the purpose of improving cognitive ability and daily life activity in subjects with mild cognitive impairment (MCI) who require cognitive rehabilitation. In the future, an integrated service platform with interactive communication and immediate feedback as an intelligent cognitive rehabilitation integrated platform based on AI and BigData is left as a research project.

• Journal of Digital Convergence
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• v.16 no.9
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• pp.229-237
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• 2018

As IoT and AI have recently developed, interest in digital healthcare is increasing. Therefore, this study aims to identify technology trends through a patent analysis on digital healthcare and present future promising areas by analyzing domestic and foreign technology competitiveness and keywords. The detailed technologies to be analyzed were designated as Health Information Measurement Technology, Healthcare Platform Technology and Healthcare Remote Service Technology, and 61,166 patents were analyzed to identify the patent trends of the world's major patent offices and major patent applications. In addition, the analysis of the technological competitiveness of each detailed technology and Korea's technological competitiveness based on its patent activity, the rate of major market securing, and the uses of the patents showed that Korea's technological competitiveness was lower than global technology. In addition, the key keyword analysis showed that the core promising areas of digital healthcare were expected to require a focused strategy for fostering health care platform technologies in Korea.

• Journal of the Korea Convergence Society
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• v.13 no.3
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• pp.67-75
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• 2022

Medical information is variously generated not only from medical devices but also from electronic devices. Recently, related convergence technologies from big data collection in healthcare to medical AI products for patient's condition analysis are rapidly increasing. However, there are difficulties in applying them because of independent developmental procedures. In this paper, we propose an intelligent hospital information system (iHIS) model to simplify and integrate research, development and application of medical AI technology. The proposed model includes (1) real-time patient data management, (2) specialized data management for medical AI development, and (3) real-time monitoring for patient. Using this, real-time biometric data collection and medical AI specialized data generation from patient monitoring devices, as well as specific AI applications of camera-based patient gait analysis and brain MRA-based cerebrovascular disease analysis will be introduced. Based on the proposed model, it is expected that it will be used to improve the HIS by increasing security of data management and improving practical use through consistent interface platformization.