• Journal of Medicine and Life Science
• /
• v.20 no.2
• /
• pp.53-59
• /
• 2023

This study explores the potential use of artificial intelligence (AI)-based services, specifically ChatGPT-3.5, in medical education. The application of this technology is acknowledged as a valuable tool for simulating authentic clinical scenarios and enhancing learners' diagnostic and communication skills. To construct a case, students received ChatGPT training using a clinical ethics casebook titled "Clinical Ethics Cases and Commentaries for Medical Students and Physicians." Subsequently, a role-play script was generated based on this training. The initial draft of the script was reviewed by two medical professors and was further optimized using ChatGPT-3.5. Consequently, a comprehensive role-play script, accurately reflecting real-world clinical situations, was successfully developed. This study demonstrates the potential for effectively integrating AI technology into medical education and provides a solution to overcome limitations in developing role-play scripts within conventional educational settings. However, the study acknowledges that AI cannot always generate flawless role-play scripts and recognizes the necessity of addressing these limitations and ethical concerns. The research explores both the potential and limitations of employing AI in the early stages of medical education, suggesting that future studies should focus on overcoming these limitations while further investigating the potential applications of AI in this field.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2023.11a
• /
• pp.1080-1081
• /
• 2023

항만 사고 안전을 예방하기 위해 본 챗봇을 만들었다. 다양한 기술들을 사용하여 사용자들이 항만과 관련된 폭넓은 지식을 제공하기 위해 노력했고, 사람들이 안전 수칙을 최대한 지켰으면 하는 바람에서 안전 수칙들을 지속해 환기해 주는 기능들을 첨가했다.

• Journal of the Korean Society of Radiology
• /
• v.16 no.4
• /
• pp.453-462
• /
• 2022

In this paper, the artificial intelligence (AI) technology used in the medical image analysis field was analyzed through a literature review. Literature searches were conducted on PubMed, ResearchGate, Google and Cochrane Review using the key word. Through literature search, 114 abstracts were searched, and 98 abstracts were reviewed, excluding 16 duplicates. In the reviewed literature, AI is applied in classification, localization, disease detection, disease segmentation, and fit degree of registration images. In machine learning (ML), prior feature extraction and inputting the extracted feature values into the neural network have disappeared. Instead, it appears that the neural network is changing to a deep learning (DL) method with multiple hidden layers. The reason is thought to be that feature extraction is processed in the DL process due to the increase in the amount of memory of the computer, the improvement of the calculation speed, and the construction of big data. In order to apply the analysis of medical images using AI to medical care, the role of physicians is important. Physicians must be able to interpret and analyze the predictions of AI algorithms. Additional medical education and professional development for existing physicians is needed to understand AI. Also, it seems that a revised curriculum for learners in medical school is needed.

• Archives of Craniofacial Surgery
• /
• v.22 no.5
• /
• pp.223-231
• /
• 2021

The field of artificial intelligence (AI) is rapidly advancing, and AI models are increasingly applied in the medical field, especially in medical imaging, pathology, natural language processing, and biosignal analysis. On the basis of these advances, telemedicine, which allows people to receive medical services outside of hospitals or clinics, is also developing in many countries. The mechanisms of deep learning used in medical AI include convolutional neural networks, residual neural networks, and generative adversarial networks. Herein, we investigate the possibility of using these AI methods in the field of craniofacial surgery, with potential applications including craniofacial trauma, congenital anomalies, and cosmetic surgery.

• Journal of Digestive Cancer Research
• /
• v.12 no.1
• /
• pp.38-43
• /
• 2024

Artificial Intelligence (AI) is the intelligence of machines or software, in contrast to human intelligence. Generative AI technologies, such as ChatGPT, have emerged as valuable research tools that facilitate brainstorming ideas for research, analyzing data, and writing papers. However, their application has raised concerns regarding authorship, copyright, and ethical considerations. Many organizations of medical journal editors, including the International Committee of Medical Journal Editors and the World Association of Medical Editors, do not recognize AI technology as an author. Instead, they recommend that researchers explicitly acknowledge the use of AI tools in their research methods or acknowledgments. Similarly, international journals do not recognize AI tools as authors and insist that human authors should be accountable for the research findings. Therefore, when integrating AI-generated content into papers, it should be disclosed under the responsibility of human authors, and the details of the AI tools employed should be specified to ensure transparency and reliability.

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

• Proceedings of the Korean Society of Applied Pharmacology
• /
• 1995.10a
• /
• pp.107-113
• /
• 1995

Basement membrane laminin is a multidomain glycoprotein that interacts with itself, heparin and cells. The distal long arm plays major cell and heparin interactive roles. The long arm consists of three subunits (A, B1, B2) joined in a coiled-coil rod attached to a terminal A chain globule (G). The globule is in turn subdivided into five subdomains (Gl-5). In order to analyze the functions of this region, recombinant G domains (rG, rAiG, rG5, rGΔ2980-3028) were expressed in Sf9 insect cells using a baculovirus expression vector. A hybrid molecule (B-rAiG), consisting of recombinant A chain(rAiG) and the authentic B chains (E8-B)was assembled in vitro. The intercalation of rAiG into E8-B chains suppressed a heparin binding activity identified in subdomain Gl-2. By the peptide napping and ligand blotting, the relative affinity of each subeomain to heparin was assigned as Gl> G2= G4> G5> G3, such that G1 bound strongly and G3 not at all. The active heparin binding site of G domain in intact laminin appears to be located in G4 and proximal G5. Cell binding was examined using fibrosarcoma Cells. Cells adhered to E8, B-rAiG, rAiG and rG, did not bind on denatured substrates, poorly bound to the mixture of E8-B and rG. Anti-${\alpha}$6 and anti-${\beta}$1 integrin subunit separately blocked cell adhesion on E8 and B-rAiG, but not on rAiG. Heparin inhibited cell adhesion on rAiG, partially on B-rAiG, and not on E8. In conclusion, 1) There are active and cryptic cell and heparin binding activities in G domain. 2) Triple-helix assembly inactivates cell and heparin binding activities and restores u6131 dependent cell binding activities.

• Journal of Information Technology Services
• /
• v.18 no.2
• /
• 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.

• The Korean Society of Law and Medicine
• /
• v.24 no.4
• /
• pp.67-101
• /
• 2023

The utilization of generative AI in the medical field is also being rapidly researched. Access to vast data sets reduces the time and energy spent in selecting information. However, as the effort put into content creation decreases, there is a greater likelihood of associated issues arising. For example, with generative AI, users must discern the accuracy of results themselves, as these AIs learn from data within a set period and generate outcomes. While the answers may appear plausible, their sources are often unclear, making it challenging to determine their veracity. Additionally, the possibility of presenting results from a biased or distorted perspective cannot be discounted at present on ethical grounds. Despite these concerns, the field of generative AI is continually advancing, with an increasing number of users leveraging it in various sectors, including biomedical and life sciences. This raises important legal considerations regarding who bears responsibility and to what extent for any damages caused by these high-performance AI algorithms. A general overview of issues with generative AI includes those discussed above, but another perspective arises from its fundamental nature as a large-scale language model ('LLM') AI. There is a civil law concern regarding "the memorization of training data within artificial neural networks and its subsequent reproduction". Medical data, by nature, often reflects personal characteristics of patients, potentially leading to issues such as the regeneration of personal information. The extensive application of generative AI in scenarios beyond traditional AI brings forth the possibility of legal challenges that cannot be ignored. Upon examining the technical characteristics of generative AI and focusing on legal issues, especially concerning the protection of personal information, it's evident that current laws regarding personal information protection, particularly in the context of health and medical data utilization, are inadequate. These laws provide processes for anonymizing and de-identification, specific personal information but fall short when generative AI is applied as software in medical devices. To address the functionalities of generative AI in clinical software, a reevaluation and adjustment of existing laws for the protection of personal information are imperative.

• Korean Journal of Radiology
• /
• v.24 no.11
• /
• pp.1061-1080
• /
• 2023

Artificial intelligence (AI) in radiology is a rapidly developing field with several prospective clinical studies demonstrating its benefits in clinical practice. In 2022, the Korean Society of Radiology held a forum to discuss the challenges and drawbacks in AI development and implementation. Various barriers hinder the successful application and widespread adoption of AI in radiology, such as limited annotated data, data privacy and security, data heterogeneity, imbalanced data, model interpretability, overfitting, and integration with clinical workflows. In this review, some of the various possible solutions to these challenges are presented and discussed; these include training with longitudinal and multimodal datasets, dense training with multitask learning and multimodal learning, self-supervised contrastive learning, various image modifications and syntheses using generative models, explainable AI, causal learning, federated learning with large data models, and digital twins.