• The Journal of Korea Robotics Society
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• v.16 no.2
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• pp.172-178
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• 2021

The COVID-19 pandemic has been reshaping the world by accelerating non-contact services and technologies in various domains. Hospitals as a healthcare system lie at the center of the dramatic change because of their fundamental roles: medical diagnosis and treatments. Leading experts in health, science, and technologies have predicted that robotics and artificial intelligence (AI) can drive such a hospital transformation. Accordingly, several government-led projects have been developed and started toward smarter hospitals, where robots and AI replace or support healthcare personnel, particularly in the diagnosis and non-surgical treatment procedures. This article inspects the remaining element of healthcare services, i.e., surgical treatment, focusing on evaluating whether or not currently available laparoscopic surgical robotic systems are sufficiently preparing for the era of post-COVID-19 when contactless is the new normal. Challenges and future directions towards an effective, fully non-contact surgery are identified and summarized, including remote surgery assistance, domain-expansion of robotic surgery, and seamless integration with smart operating rooms, followed by emphasis on robot tranining for surgical staff.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.6
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• pp.1283-1292
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• 2022

At present, several artificial intelligence projects in the healthcare and medical field are competing with each other, and the interfaces between the systems lack unified specifications. Thus, this study presents an artificial intelligence platform for healthcare and medical fields which adopts the deep learning technology to provide algorithms, models and service support for the health and medical enterprise applications. The suggested platform can provide a large number of heterogeneous data processing, intelligent services, model managements, typical application scenarios, and other services for different types of business. In connection with the suggested platform application, we represents a medical service which is corresponding to the trusted and comprehensible tracking and analyzing patient behavior system for Health and Medical treatment using Internet of Behavior concept.

• Journal of the Korean Medical Association
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• v.61 no.12
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• pp.765-775
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• 2018

With growing interest in novel digital healthcare devices, such as artificial intelligence (AI) software for medical diagnosis and prediction, and their potential impacts on healthcare, discussions have taken place regarding the regulatory approval, coverage, and clinical implementation of these devices. Despite their potential, 'digital exceptionalism' (i.e., skipping the rigorous clinical validation of such digital tools) is creating significant concerns for patients and healthcare stakeholders. This white paper presents the positions of the Korean Society of Radiology, a leader in medical imaging and digital medicine, on the clinical validation, regulatory approval, coverage decisions, and clinical implementation of novel digital healthcare devices, especially AI software for medical diagnosis and prediction, and explains the scientific principles underlying those positions. Mere regulatory approval by the Food and Drug Administration of Korea, the United States, or other countries should be distinguished from coverage decisions and widespread clinical implementation, as regulatory approval only indicates that a digital tool is allowed for use in patients, not that the device is beneficial or recommended for patient care. Coverage or widespread clinical adoption of AI software tools should require a thorough clinical validation of safety, high accuracy proven by robust external validation, documented benefits for patient outcomes, and cost-effectiveness. The Korean Society of Radiology puts patients first when considering novel digital healthcare tools, and as an impartial professional organization that follows scientific principles and evidence, strives to provide correct information to the public, make reasonable policy suggestions, and build collaborative partnerships with industry and government for the good of our patients.

• Review of Korea Contents Association
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• v.17 no.1
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• pp.18-22
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• 2019

• Journal of Digital Convergence
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• v.19 no.6
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• pp.419-427
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• 2021

The purpose of this study is to identify the recognition and utilization of health information by acquisition channels for preliminary and elderly people. For the survey data of 200 people aged 55 to 64 and 200 seniors aged 65 or older, the chi-square test and Fisher's precision test were performed using MINITAB17. Although the two age groups were similar in obtaining health information through health professionals, preliminary elderly were more likely to obtain health information through mass media and Internet sites. In particular, the collection of health information through internet sites was more than four times higher than that of the elderly. While the preliminary people focused on searching the information on the health care and prevention, older people explored comprehensive information on health, including disease prevention and treatment, through each channel. Both groups showed positive recognition about the acquired health information. The results of this study confirmed that all channels, including internet sites, can be usefully used in the delivery of health-related information to the elderly in the future. In addition, it is suggested to consider age characteristics and health information utilized by each channel in the development of various contents for the improvement of self-health management of the elderly.

• Electronics and Telecommunications Trends
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• v.35 no.2
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• pp.1-16
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• 2020

"Artificial Intelligence+" is a key strategic direction that has garnered the attention of several global medical device manufacturers and internet companies. Large hospitals are actively involved in different types of medical AI research and cooperation projects. Medical AI is expected to create numerous opportunities and advancements in areas such as medical imaging, computer aided diagnostics and clinical decision support, new drug development, personal healthcare, pathology analysis, and genetic disease prediction. On the contrary, some studies on the limitations and problems in current conditions such as lack of clinical validation, difficulty in performance comparison, lack of interoperability, adversarial attacks, and computational manipulations are being published. Overall, the medical AI field is in a paradigm shift. Regarding international standardization, the work on the top 10 standardization issues is witnessing rapid progress and the competition for standard development has become fierce.

• Journal of Internet Computing and Services
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• v.25 no.5
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• pp.53-65
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• 2024

Currently, artificial intelligence (AI) is being applied in the medical field to collect and analyze data such as personal genetic information, medical information, and lifestyle information. In particular, in the medical imaging field, AI is being applied to the medical imaging field to analyze patients' medical image data and diagnose diseases. Deep learning (DL) of deep neural networks such as CNN and GAN have been introduced to medical image analysis and medical data augmentation to facilitate lesion detection, quantification, and classification. In this paper, we examine AI used in the medical imaging field and review related medical image data acquisition devices, medical information systems for transmitting medical image data, problems with medical image data, and the current status of explainable artificial intelligence (XAI) that has been actively applied recently. In the future, the continuous development of AI and information and communication technology (ICT) is expected to make it easier to analyze medical image data in the medical field, enabling disease diagnosis, prognosis prediction, and improvement of patients' quality of life. In the future, AI medicine is expected to evolve from the existing treatment-centered medical system to personalized healthcare through preemptive diagnosis and prevention.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.6
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• pp.1249-1254
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• 2021

Computing technology and networking technology in the era of the 4th industrial revolution are rapidly evolving into an intelligent information society. AR, VR, and MR technologies, which are dual immersive media fields, are being applied in many convergence technologies, especially! The development of the health and healthcare field is actively progressing. In the field of health and healthcare, there are many problems due to aging of the population, increase in chronic progress, lack of infrastructure, and lack of professional manpower. services in the field are adopted. Therefore, this study applies cognitive evaluation through a computing system to the mild cognitive impairment, designs and develops a cognitive judgment platform using augmented reality based on the cognitive judgment technology system design, and integrates AI and BigData-based intelligent cognitive rehabilitation in the future. It is used as basic data for service platform development.

• Journal of the Korean Society of Radiology
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• v.83 no.4
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• pp.759-770
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• 2022

The importance of ethics in research and the use of artificial intelligence (AI) is increasingly recognized not only in the field of healthcare but throughout society. This article intends to provide domestic readers with practical points regarding the ethical issues of using radiological images for AI research, focusing on data security and privacy protection and the right to data. Therefore, this article refers to related domestic laws and government policies. Data security and privacy protection is a key ethical principle for AI, in which proper de-identification of data is crucial. Sharing healthcare data to develop AI in a way that minimizes business interests is another ethical point to be highlighted. The need for data sharing makes the data security and privacy protection even more important as data sharing increases the risk of data breach.

• Journal of Convergence for Information Technology
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• v.11 no.4
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• pp.83-101
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• 2021

This study is to investigate, using AHP, what technologies should be applied first to Korean SMEs in order to respond to the 4th industrial revolution and change to a smart enterprise. To this end, technologies related to the 4th industrial revolution and smart factory are synthesized, and the classification criteria of Dae-Hoon Kim et al. (2019) are applied, but additional opinions of experts are collected and related technologies are converted to artificial intelligence (AI), Big Data, and Cloud Computing. As a base technology, mobile, Internet of Things (IoT), block chain as hyper-connected technology, unmanned transportation (autonomous driving), robot, 3D printing, drone as a convergence technology, smart manufacturing and logistics, smart healthcare, smart transportation and smart finance were classified as smart industrial technologies. As a result of confirming the priorities for technical use by AHP analysis and calculating the total weight, manufacturing companies have a high ranking in mobile, artificial intelligence (AI), big data, and robots, while service companies are in big data and robots, artificial intelligence (AI), and smart healthcare are ranked high, and in all companies, it is in the order of big data, artificial intelligence (AI), robot, and mobile. Through this study, it was clearly identified which technologies should be applied first in order to respond to the 4th industrial revolution and change to a smart company.