• Review of Korea Contents Association
• /
• v.16 no.4
• /
• pp.20-23
• /
• 2018

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.16 no.5
• /
• pp.279-291
• /
• 2023

In recent years, language model-based generative AI technologies have made remarkable progress. In particular, it has attracted a lot of attention due to its increasing potential in various fields such as summarization and code writing. As a reflection of this interest, the number of patent applications related to generative AI has been increasing rapidly. In order to understand these trends and develop strategies accordingly, future forecasting is key. Predictions can be used to better understand the future trends in the field of technology and develop more effective strategies. In this paper, we analyzed patents filed to date to identify the direction of development of language model-based generative AI. In particular, we took an in-depth look at research and invention activities in each country, focusing on application trends by year and detailed technology. Through this analysis, we tried to understand the detailed technologies contained in the core patents and predict the future development trends of generative AI.

• Progress in Medical Physics
• /
• v.30 no.2
• /
• pp.49-58
• /
• 2019

Purpose: Automated analytical systems have begun to emerge as a database system that enables the scanning of medical images to be performed on computers and the construction of big data. Deep-learning artificial intelligence (AI) architectures have been developed and applied to medical images, making high-precision diagnosis possible. Materials and Methods: For diagnosis, the medical images need to be labeled and standardized. After pre-processing the data and entering them into the deep-learning architecture, the final diagnosis results can be obtained quickly and accurately. To solve the problem of overfitting because of an insufficient amount of labeled data, data augmentation is performed through rotation, using left and right flips to artificially increase the amount of data. Because various deep-learning architectures have been developed and publicized over the past few years, the results of the diagnosis can be obtained by entering a medical image. Results: Classification and regression are performed by a supervised machine-learning method and clustering and generation are performed by an unsupervised machine-learning method. When the convolutional neural network (CNN) method is applied to the deep-learning layer, feature extraction can be used to classify diseases very efficiently and thus to diagnose various diseases. Conclusions: AI, using a deep-learning architecture, has expertise in medical image analysis of the nerves, retina, lungs, digital pathology, breast, heart, abdomen, and musculo-skeletal system.

• Journal of the Korea Convergence Society
• /
• v.8 no.12
• /
• pp.77-84
• /
• 2017

With the entry into the aging society, we are increasingly interested in wellness, and personalized medical services through artificial intelligence are expanding. In order to provide personalized medical services, it is difficult to provide accurate medical analysis services only with the existing hospital system components PM / PA, OCS, EMR, PACS, and LIS. Therefore, it is necessary to present the hospital system model and the construction plan suitable for personalized medical service. Currently, some medical cloud services and artificial intelligence diagnosis services using Watson are being introduced in domestic. However, there are not many examples of systematic hospital system construction. Therefore, this paper proposes a hospital system model suitable for personalized medical service. To do this, we design a model that integrates medical big data construction and AI medical analysis system into the existing hospital system components, and suggest development plan of each module. The proposed model is meaningful as a basic research that provides guidelines for the construction of new hospital system in the future.

• Asia pacific journal of information systems
• /
• v.29 no.4
• /
• pp.817-837
• /
• 2019

The medical industry is rapidly evolving into a combination of artificial intelligence (AI) and ICT technology, such as mobile health, wireless medical, telemedicine and precision medical care. Medical artificial intelligence can be diagnosed and treated, and autonomous surgical robots can be operated. For smart medical services, data such as medical information and personal medical information are needed. AI is being developed to integrate with companies such as Google, Facebook, IBM and others in the health care field. Telemedicine services are also becoming available. However, security issues of medical information for smart medical industry are becoming important. It can have a devastating impact on life through hacking of medical devices through vulnerable areas. Research on medical information is proceeding on the necessity of privacy and privacy protection. However, there is a lack of research on the practical measures for protecting medical information and the seriousness of security threats. Therefore, in this study, we want to confirm the research trend by collecting data related to medical information in recent 5 years. In this study, smart medical related papers from 2014 to 2018 were collected using smart medical topics, and the medical information papers were rearranged based on this. Research trend analysis uses topic modeling technique for topic information. The result constructs topic network based on relation of topics and grasps main trend through topic.

• Journal of the korean veterinary medical association
• /
• v.51 no.11
• /
• pp.646-647
• /
• 2015

• Journal of the korean veterinary medical association
• /
• v.44 no.5
• /
• pp.396-399
• /
• 2008

• Journal of the korean veterinary medical association
• /
• v.50 no.2
• /
• pp.86-89
• /
• 2014

• Health Policy and Management
• /
• v.33 no.4
• /
• pp.479-488
• /
• 2023

Background: By applying the suggested criteria for needs-based chronic medical care and long-term care delivery system for the elderly, the current status of delivery system was identified and regional delivery systems were categorized according to quantity and quality of delivery system. Methods: National claims data were used for this study. All claims data of medical and long-term care uses by the elderly and all claims data from long-term care hospitals and nursing homes in 2016 were analyzed to categorize the regional medical and long-term care delivery system. The current status of the delivery system with a high possibility of transition to a needs-based appropriate delivery system was identified. The necessary and actual amount of regional supply was calculated based on their needs, and the structure of delivery systems was evaluated in terms of the needs-based quality of the system. Finally, all regions were categorized into 15 types of medical and care delivery systems for the elderly. Results: Of the total 55 regions, 89.1% of regions had an oversupply of elderly medical and care services compared to the necessary supply based on their needs. However, 69.1% of regions met the criteria for less than two types of needs groups, and 21.8% of regions were identified as regions where the numbers of institutions or regions with a high possibility of transition to an appropriate delivery system were below the average levels for all four needs groups. Conclusion: In order to establish an appropriate community-based integrated elderly care system, it is necessary to analyze the characteristics of the regional delivery system categories and to plan a needs-based delivery system regionally.

• Radiation Oncology Journal
• /
• v.17 no.3
• /
• pp.203-208
• /
• 1999

Purpose : To evaluate whether spontaneous apoptosis has prognostic value among patients with squamous cell carcinoma of lung. Materials and Methods : Material from 19 patients who received thoracic irradiation between 1990 and 1994 was analyzed. Their stages were II (1), IIIa (8), IIIb (5), and IV (5). Patients were observed from 5 to 67 months (median : 17 months). The spontaneous apoptosis index (AI) and p53 mutation were measured by immunohistochemical stains. Results : AI was found to range from 0 to $1\%$ (median $0.4\%$). Patients with low AI ($AI{\leq}$median) had a much higher distant metastasis rate at diagnosis than patients with high AI. By analysis of prognostic factors for survival, M stage was significant in univariate analysis. AI, chemotherapy, M stage, T stage, and stage were significant in multivariate analysis. The correlation between the AI and p53 mutation was not seen. Conclusion : AI was related with distant metastasis at diagnosis and not with p53 mutation. Also low AI group tended to have shorter survival time than high AI group.