• Childhood Kidney Diseases
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• v.28 no.3
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• pp.93-98
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• 2024

Artificial intelligence (AI) is revolutionizing healthcare by providing tools for disease prediction, diagnosis, and patient management. This review focuses on two key AI methodologies in healthcare: predictive modeling and causal inference. Predictive models excel in identifying patterns to forecast outcomes but are limited in explaining the underlying causes. In contrast, causal inference focuses on understanding cause-and-effect relationships, which makes effective medical interventions possible. Although randomized controlled trials (RCTs) are the gold standard for causal inference, they face limitations including cost and ethical concerns. As alternatives, emulated RCTs and advanced machine learning techniques have emerged for estimating causal effects, bridging the gap between prediction and causality. Additionally, Shapley values and Local Interpretable Model-Agnostic Explanations improve the interpretability of complex AI models, making them more actionable in clinical settings. Integrating prediction and causal inference holds great promise for advancing personalized medicine, enhancing patient outcomes, and optimizing healthcare delivery. However, careful application of AI tools is crucial to avoid misinterpretation and maximize their potential.

• Journal of Oral Medicine and Pain
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• v.47 no.2
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• pp.107-108
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• 2022

Artificial intelligence (AI) refers to the use of machines to mimic intelligent human behavior. It involves interactions with humans in clinical settings, and augmented intelligence is considered as a cognitive extension of AI. The importance of AI in healthcare and medicine has been emphasized in recent studies. Machine learning models, such as genetic algorithms, artificial neural networks (ANNs), and fuzzy logic, can learn and examine data to execute various functions. Among them, ANN is the most popular model for diagnosis based on image data. AI is rapidly becoming an adjunct to healthcare professionals and is expected to be human-independent in the near future. The introduction of AI to the diagnosis and treatment of oral diseases worldwide remains in the preliminary stage. AI-based or assisted diagnosis and decision-making will increase the accuracy of the diagnosis and render treatment more precise and personalized. Therefore, dental professionals must actively initiate and lead the development of AI, even if they are unfamiliar with it.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.4
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• pp.375-384
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• 2024

As Artificial Intelligence (AI) continues to permeate various sectors such as healthcare, finance, and transportation, the importance of securing AI systems against emerging threats has become increasingly critical. The proliferation of AI across these industries not only introduces opportunities for innovation but also exposes vulnerabilities that could be exploited by malicious actors. This comprehensive review delves into the current landscape of AI security, providing an in-depth analysis of the threats, challenges, and mitigation strategies associated with AI technologies. The paper discusses key threats such as adversarial attacks, data poisoning, and model inversion, all of which can severely compromise the integrity, confidentiality, and availability of AI systems. Additionally, the paper explores the challenges posed by the inherent complexity and opacity of AI models, particularly deep learning networks. The review also evaluates various mitigation strategies, including adversarial training, differential privacy, and federated learning, that have been developed to safeguard AI systems. By synthesizing recent advancements and identifying gaps in existing research, this paper aims to guide future efforts in enhancing the security of AI applications, ultimately ensuring their safe and ethical deployment in both critical and everyday environments.

• Clinical and Experimental Reproductive Medicine
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• v.32 no.1
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• pp.27-32
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• 2005

Objective: To evaluate the effectiveness of aromatase inhibitor (AI) for ovulation induction in polycystic ovary syndrome (PCOS) patients with thin endometrium, hyper-responsiveness after clomiphene citrate (CC) treatment. Material and Methods: A prospective study was performed in 43 PCOS patients (50 cycles) with ovulatory dysfunction between March 2004 and September 2004. AI group (total 36 cycles) included the patients 1) with thin endometrium below 6 mm on hCG day after CC (n=17), 2) with more than 5 ovulatory follicles after 50mg of CC (n=4), 3) who do not want multiple pregnancy (n=14). Patients were treated with Letrozole 2.5mg for days 3 to 7 of the menstrual cycle. CC group (total 14 cycles) were treated with CC 50~100 mg. Results: In PCOS patients, ovulation was occurred 97.2% after AI use. Between AI group and CC group, there was no significant difference in the mean age, duration of infertility, interval of menstruation, basal FSH, prior treatment cycles, and the day of hCG administration. But, the number of mature follicles (${\geq}15mm$) was lower in the AI group ($1.08{\pm}0.45$ vs. $1.64{\pm}0.75$) (p=0.018), and the thickness of endometrium (mm) was significantly thicker in the AI group ($10.35{\pm}1.74$ vs. $9.23{\pm}1.61$) (p=0.044), and E2 (pg/ml) concentration on hCG day was lower in the AI group ($116.9{\pm}75.8$ vs. $479.5{\pm}300.8$) (p=0.001). Among the AI group, patients with prior thin endometrium (below 6 mm) during CC treatment showed $10.6{\pm}1.6mm$ in the endometrial thickness and $106.6{\pm}66.8pg/ml$ in $E_2$ concentration. Patients with more than 5 ovulatory follicles after CC showed decreased follicle number ($1.25{\pm}0.5$) compared to prior CC cycle. Conclusions: In PCOS patients, AI group showed significantly thicker endometrium, lesser number of mature follicles, and lower E2 concentration on hCG day than CC group. AI might be useful alternative treatment for ovulation induction in PCOS patients with thin endometrium and hyper-responsiveness after CC treatment.

• Annual Conference of KIPS
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• 2019.10a
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• pp.879-881
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• 2019

전자의무기록 (EMR)과 같은 의료 현장에서 수집되는 대용량의 데이터는 임상 해석적으로 잠재가치가 크고 활용도가 다양하나 결측값이 많아 희소성이 크다는 한계점이 있어 분석이 어렵다. 특히 EMR의 정보수집과정에서 발생하는 결측값은 무작위적이고 임의적이어서 분석 정확도를 낮추고 예측 모델의 성능을 저하시키는 주된 요인으로 작용하기 때문에, 결측치 대체는 필수불가결하다. 최근 통상적으로 활용되어지던 통계기반 알고리즘기반의 결측치 대체 알고리즘보다는 딥러닝 기술을 활용한 알고리즘들이 새로이 등장하고 있다. 본 논문에서는 Generative Adversarial Network를 기반한 최신 결측값 대치 알고리즘인 Generative Adversarial Imputation Nets을 적용하여 EMR에서의 성능을 분석해보고자 하였다.

• Journal of Convergence for Information Technology
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• v.7 no.4
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• pp.53-60
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• 2017

The purpose of this study was to identify the factors affecting intention and attitude of artificial intelligence technology(AI) of university students in healthcare using UTAUT model. Participants were 278 college students and the data were collected through self-reported questionnaire from May 15 to June 14, 2016. The collected data were analyzed using PASW Statistics/AMOS 22.0. The results were as follows. The effect of expectation factor, social influence, usefulness of work, anxiety factor had a significant effect on use of AI technology Intention. Factor of expectation effect, social influence, usefulness of work, anxiety factor had a significant effect on use of AI technology. As a result of verifying the significance of the indirect effect, it can be seen that the direct effect of the anxiety factor on the attitude factor is partially mediated by the use intention factor and the intention to use was partially mediated in the direct effect of the usefulness factor of the task on the attitude factor. This result means that it is important to increase the expectation factors, social effects, and perceived usefulness through accurate information based on facts and to reduce vague anxiety in order to increase the positive intention and attitude of university students' use of AI technology.

• Health Policy and Management
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• v.34 no.2
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• pp.103-105
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• 2024

Artificial intelligence (AI) technology is rapidly spreading across various industries. Accordingly, interest in ethical issues arising from the use of AI is also increasing. This is particularly true in the healthcare sector, where AI-related ethical issues are a significant topic due to its focus on health and life. Hence, this issue aims to examine the ethical concerns when using AI tools during research and publication processes. One of the key concerns is the potential for unintended plagiarism when researchers use AI tools for tasks such as translation, citation, and editing. Currently, as AI is not given authorship, the researcher is held accountable for any ethical problems arising from using AI tools. Researchers are advised to specify which AI tools were used and how they were employed in their research papers. As more cases of ethical issues related to AI tools accumulate, it is expected that various guidelines will be developed. Therefore, researchers should stay informed about global consensus and guidelines regarding the use of AI tools in the research and publication process.

• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.9-16
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• 2024

AI or Artificial Intelligence has been a significant tool used in the organisational backgrounds for an effective improvement in the management methods. The processing of the information and the analysis of the data for the further achievement of heightened efficiency can be performed by AI through its data analytics measures. In the medical field, AI has been integrated for an improvement within the management of the medical services and to note a rise in the levels of customer satisfaction. With the benefits of reasoning and problem solving, AI has been able to initiate a range of benefits for both the consumers and the medical personnel. The main benefits which have been noted in the integration of AI would be integrated into the study. The issues which are noted with the integrated AI usage for the medical sector would also be identified in the study. Medical Image Processing has been seen to integrate 3D image datasets with the medical industry, in terms of Computed Tomography (CT) or Magnetic Resonance Imaging (MRI). The usage of such medical devices have occurred in the diagnosis of the patients, the development of guidance towards medical intervention and an overall increase in the medical efficiency. The study would focus on such different tools, adhered with AI for increased medical improvement.

• Journal of Digital Contents Society
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• v.19 no.5
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• pp.981-993
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• 2018

Smart healthcare is convergence of ICT and healthcare services, and interdisciplinary research has been actively conducted in various fields. The objective of this study is to investigate trends of smart healthcare research using topic modeling and ego network analysis. Text analysis, frequency analysis, topic modeling, word cloud, and ego network analysis were conducted for the abstracts of 2,690 articles in Scopus from 2001 to April 2018. Topic Modeling analysis resulted in eight topics, Topics included "AI in healthcare", "Smart hospital", "Healthcare platform", "Blockchain in healthcare", "Smart health data", "Mobile healthcare", " Wellness care", "Cognitive healthcare". In order to examine the topic modeling results core deeply, we analyzed word cloud and ego network analysis for eight topics. This study aims to identify trends in smart healthcare research and suggest implications for establishing future research direction.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2021.11a
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• pp.60-62
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• 2021

항로표지는 해상상황 정보를 수집하고 선박들의 항해에 안전을 도모하기 위해 설치 및 운용되고 있다. 관련해 개별 지방청에서 운영되는 데이터를 빅데이터 형태로 활용하고자 하는 경우 수집된 데이터의 품질에 대한 평가가 이루어져야 한다. 본 논문에서는 수집된 항로표지 데이터의 누락 정보를 중심으로 데이터 수집에 있어 장애 생성의 주된 원인을 찾고자 하였다. 수집된 데이터의 분석 결과 기상악화와 표지의 전압이 하락한 날에 데이터 결측 발생률이 톺음을 확인할 수 있었다. 이를 통해 기상 상황, 표지의 전압 상태 그리고 수집된 데이터 개수의 비교를 통해 기상악화가 영향을 미쳤을 수 있음을 확인하였다.