• 한국IT서비스학회지
• /
• 제18권2호
• /
• pp.123-141
• /
• 2019

The goal of this study is to review the major research trend on the convergence studies of AI and healthcare technologies. For the study, 15,260 English articles on AI and healthcare related topics were collected from Scopus for 55 years from 1963, and text mining techniques were conducted. As a result, seven key research topics were defined : "AI for Clinical Decision Support System (CDSS)", "AI for Medical Image", "Internet of Healthcare Things (IoHT)", "Big Data Analytics in Healthcare", "Medical Robotics", "Blockchain in Healthcare", and "Evidence Based Medicine (EBM)". The result of this study can be utilized to set up and develop the appropriate healthcare R&D strategies for the researchers and government. In this study, text mining techniques such as Text Analysis, Frequency Analysis, Topic Modeling on LDA (Latent Dirichlet Allocation), Word Cloud, and Ego Network Analysis were conducted.

• 품질경영학회지
• /
• 제52권3호
• /
• pp.557-574
• /
• 2024

Purpose: This study investigates whether adoption of AI-based systems and technologies improve operational efficiency in healthcare organizations through a systematic review of the literature and real-world examples. Methods: In this study, we divided the AI application cases into care services and administrative functions, then we explored opportunities and challenges in each area. Results: The analysis results indicate that the care service field primarily uses AI-based systems and technologies for quick disease diagnosis and treatment, surgery and disease prediction, and the provision of personalized healthcare services. In the administrative field, AI-based systems and technologies are used to streamline processes and automate tasks for the following functions: patient monitoring through virtual care support systems; automating patient management systems for appointment times, reservations, changes, and no-shows; facilitating patient-medical staff interaction and feedback through interaction support systems; and managing admission and discharge procedures. Conclusion: The results of this study provide valuable insights and significant implications about the application of AI-based systems or technologies for various innovation opportunities in healthcare organizations. As digital transformation accelerates across all industries, these findings provide valuable information to managers of hospitals that are interested in AI adoption, as well as for policymakers involved in the formulation of medical regulations and laws.

• International Journal of Internet, Broadcasting and Communication
• /
• 제16권1호
• /
• pp.307-313
• /
• 2024

This study explores the integration of Explainable Artificial Intelligence (XAI) and network science in healthcare, focusing on enhancing healthcare data interpretation and improving diagnostic and treatment methods. Key methodologies like Graph Neural Networks, Community Detection, Overlapping Network Models, and Time-Series Network Analysis are examined in depth for their potential in patient health management. The research highlights the transformative role of XAI in making complex AI models transparent and interpretable, essential for accurate, data-driven decision-making in healthcare. Case studies demonstrate the practical application of these methodologies in predicting diseases, understanding drug interactions, and tracking patient health over time. The study concludes with the immense promise of these advancements in healthcare, despite existing challenges, and underscores the need for ongoing research to fully realize the potential of AI in this field.

• 의학교육논단
• /
• 제22권2호
• /
• pp.99-114
• /
• 2020

The rapid development of artificial intelligence (AI), including deep learning, has led to the development of technologies that may assist in the diagnosis and treatment of diseases, prediction of disease risk and prognosis, health index monitoring, drug development, and healthcare management and administration. However, in order for AI technology to improve the quality of medical care, technical problems and the efficacy of algorithms should be evaluated in real clinical environments rather than the environment in which algorithms are developed. Further consideration should be given to whether these models can improve the quality of medical care and clinical outcomes of patients. In addition, the development of regulatory systems to secure the safety of AI medical technology, the ethical and legal issues related to the proliferation of AI technology, and the impacts on the relationship with patients also need to be addressed. Systematic training of healthcare personnel is needed to enable adaption to the rapid changes in the healthcare environment. An overall review and revision of undergraduate medical curriculum is required to enable extraction of significant information from rapidly expanding medical information, data science literacy, empathy/compassion for patients, and communication among various healthcare providers. Specialized postgraduate AI education programs for each medical specialty are needed to develop proper utilization of AI models in clinical practice.

• Journal of Positioning, Navigation, and Timing
• /
• 제13권1호
• /
• pp.15-23
• /
• 2024

One of the core keywords in the fourth industrial revolution is convergence, and the convergence of the production, distribution, and consumption processes of services is particularly important. The convergence of user services is underway in various industrial fields including mobile communications, healthcare, mobility, artificial intelligence, etc. In order to offer these converged services efficiently, it is necessary to provide accurate user-centric location information, which can be obtained by employing the global navigation satellite system (GNSS). In addition, as we have entered the post-COVID era, the demand for various fields such as a healthcare, customized tourism services, and aviation services based on accurate location information is exploding. In this paper, we present the results of a case study on the current research trends of GNSS used in telemedicine services and AI & IoT fields, and also analyze these results.

• 의학교육논단
• /
• 제18권2호
• /
• pp.51-57
• /
• 2016

This literature review explores artificial intelligence (AI) technology trends and IBM Watson health and medical references. This study explains how healthcare will be changed by the evolution of AI technology, and also summarizes key technologies in AI, specifically the technology of IBM Watson. We look at this issue from the perspective of 'information overload,' in that medical literature doubles every three years, with approximately 700,000 new scientific articles being published every year, in addition to the explosion of patient data. Estimates are also forecasting a shortage of oncologists, with the demand expected to grow by 42%. Due to this projected shortage, physicians won't likely be able to explore the best treatment options for patients in clinical trials. This issue can be addressed by the AI Watson motivation to solve healthcare industry issues. In addition, the Watson Oncology solution is reviewed from the end user interface point of view. This study also investigates global company platform business to explain how AI and machine learning technology are expanding in the market with use cases. It emphasizes ecosystem partner business models that can support startup and venture businesses including healthcare models. Finally, we identify a need for healthcare company partnerships to be reviewed from the aspect of solution transformation. AI and Watson will change a lot in the healthcare business. This study addresses what we need to prepare for AI, Cognitive Era those are understanding of AI innovation, Cloud Platform business, the importance of data sets, and needs for further enhancement in our knowledge base.

• 디지털융복합연구
• /
• 제20권2호
• /
• pp.325-338
• /
• 2022

본 연구는 코로나 상황에서 한시적으로 허용된 비대면 진료에 대한 대학생의 수용행동을 탐색하기 위하여 진행되었다. 비대면 진료의 의료 서비스 및 디지털 기술 간 융합 특성을 반영하여, 각 분야의 수용행동을 이해하기 위하여 널리 연구되어 온 사회인지이론과 기술수용모델을 기반으로 비대면 진료에 대한 대학생의 인식 및 수용의향의 영향요인을 조사하였다. 연구모델 및 가설 검증을 위하여 비대면 진료 사용 경험이 없는 대학생을 대상으로 2021년 9월 8일부터 10일까지 온라인 설문조사를 시행하였다. 총 184개의 데이터가 수집되었으며, SPSS 28.0 프로그램을 이용하여 다중회귀분석 등을 실시하였다. 분석 결과, 건강기술 자기효능감, 유용성 이점, 편의성 이점, 사회적 규범, 비대면 진료 제공자에 대한 신뢰가 대학생의 비대면 진료 수용의향에 긍정적 영향을 주는 것으로 나타났다. 본 연구는 디지털 네이티브 세대인 대학생을 비대면 진료의 새로운 타겟으로 보고, 이들을 공략하기 위한 전략의 기초 방향을 제시했음에 의의가 있다.

• Korean Journal of Radiology
• /
• 제25권5호
• /
• pp.414-425
• /
• 2024

Objective: This study aims to explore the opinions on the insurance coverage of artificial intelligence (AI), as categorized based on the distinct value elements offered by AI, with a specific focus on patient-centered outcomes (PCOs). PCOs are distinguished from traditional clinical outcomes and focus on patient-reported experiences and values such as quality of life, functionality, well-being, physical or emotional status, and convenience. Materials and Methods: We classified the value elements provided by AI into four dimensions: clinical outcomes, economic aspects, organizational aspects, and non-clinical PCOs. The survey comprised three sections: 1) experiences with PCOs in evaluating AI, 2) opinions on the coverage of AI by the National Health Insurance of the Republic of Korea when AI demonstrated benefits across the four value elements, and 3) respondent characteristics. The opinions regarding AI insurance coverage were assessed dichotomously and semi-quantitatively: non-approval (0) vs. approval (on a 1-10 weight scale, with 10 indicating the strongest approval). The survey was conducted from July 4 to 26, 2023, using a web-based method. Responses to PCOs and other value elements were compared. Results: Among 200 respondents, 44 (22%) were patients/patient representatives, 64 (32%) were industry/developers, 60 (30%) were medical practitioners/doctors, and 32 (16%) were government health personnel. The level of experience with PCOs regarding AI was low, with only 7% (14/200) having direct experience and 10% (20/200) having any experience (either direct or indirect). The approval rate for insurance coverage for PCOs was 74% (148/200), significantly lower than the corresponding rates for other value elements (82.5%-93.5%; P ≤ 0.034). The approval strength was significantly lower for PCOs, with a mean weight ± standard deviation of 5.1 ± 3.5, compared to other value elements (P ≤ 0.036). Conclusion: There is currently limited demand for insurance coverage for AI that demonstrates benefits in terms of non-clinical PCOs.

• 한국정보통신학회:학술대회논문집
• /
• 한국정보통신학회 2022년도 춘계학술대회
• /
• pp.406-409
• /
• 2022

최근 몇 년 동안, AI 시스템, 블록체인, VR/AR, 3D 프린팅, 로봇 공학, 나노 기술과 같은 기술의 발전은 바로 우리 눈앞에서 건강 관리의 미래를 재편하고 있습니다. 또한, 의료는 소비자의 요구에 초점을 맞춘 예방 중심의 의학으로 패러다임이 전환되었습니다. Covid-19와 같은 전염병의 확산으로 의료 및 치료 시설의 정의가 변경되어 병원의 물리적 환경을 재설계하고 사회적 거리 두기 요구사항을 해결하도록 통신 모델을 조정하고 가상 의료 솔루션을 구현하고 새로운 임상 프로토콜을 수립하기 위한 즉각적인 조치가 필요하게 되었습니다. 전통적으로 의료 시스템의 허브 역할을 해 온 병원은 이러한 환경에 맞서 스스로를 재정립하는 것을 추구하거나 강요당하고 있습니다. 미래의 건강관리는 질병을 치료하는 것뿐만 아니라 건강과 예방에 초점을 맞출 것으로 예상됩니다. 개인화된 진료에서는 장기적인 예방 전략, 원격 모니터링, 조기 진단 및 탐지가 매우 중요합니다. 이러한 현대 기술로 정의되는 스마트 헬스케어에 대한 관심이 높아짐에 따라, 본 연구는 스마트 헬스케어의 정의와 서비스 종류를 조사했습니다. 스마트 병원의 배경과 기술적 측면도 문헌 검토를 통해 탐구했습니다.

• 한국전자통신학회논문지
• /
• 제19권3호
• /
• pp.633-640
• /
• 2024

이 연구는 헬스케어 분야에서 활용 가능한 AI 기반의 3D 체형 모델링 기술을 개발하고, 이를 통해 사용자의 체형 변화와 건강 상태를 모니터링 할 수 있는 시스템을 제안한다. 사이즈코리아의 데이터를 활용하여 2D 이미지로부터 3D 체형 이미지를 생성하는 모델을 개발하고, 다양한 모델을 비교하여 가장 성능이 우수한 모델을 선정하였다. 최종적으로, 개발된 기술을 통해 개인 맞춤형 건강 관리, 운동 추천, 식단 제안 등의 시스템 프로세스를 제안함으로써 질병 예방 및 건강 증진에 기여하고자 하였다.