• Korean Journal of Radiology
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• v.25 no.5
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• pp.414-425
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• 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.

• The Journal of Bigdata
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• v.8 no.1
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• pp.111-129
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• 2023

With the diffusion of digital innovation, the adoption of innovative medical technologies based on artificial intelligence is increasing in the medical field. This is driving the launch and adoption of AI-based SaMD(Software as a Medical Device), but there is a lack of research on the factors that influence the adoption of SaMD by medical institutions. The purpose of this study is to identify key factors that influence medical institutions' decisions to adopt AI-based SaMDs, and to analyze the weights and priorities of these factors. For this purpose, we conducted Delphi surveys based on the results of literature studies on technology acceptance models in healthcare industry, medical AI and SaMD, and developed a research model by combining HOTE(Human, Organization, Technology and Environment) framework and HABIO(Holistic Approach {Business, Information, Organizational}) framework. Based on the research model with 5 main criteria and 22 sub-criteria, we conducted an AHP(Analytical Hierarchy Process) analysis among the experts from domestic medical institutions and SaMD providers to empirically analyze SaMD adoption factors. The results of this study showed that the priority of the main criteria for determining the adoption of AI-based SaMD was in the order of technical factors, economic factors, human factors, organizational factors, and environmental factors. The priority of sub-criteria was in the order of reliability, cost reduction, medical staff's acceptance, safety, top management's support, security, and licensing & regulatory levels. Specifically, technical factors such as reliability, safety, and security were found to be the most important factors for SaMD adoption. In addition, the comparisons and analyses of the weights and priorities of each group showed that the weights and priorities of SaMD adoption factors varied by type of institution, type of medical institution, and type of job in the medical institution.

• Journal of Gastric Cancer
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• v.23 no.3
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• pp.400-409
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• 2023

Artificial intelligence (AI) has made significant progress in recent years, and many medical fields are attempting to introduce AI technology into clinical practice. Currently, much research is being conducted to evaluate that AI can be incorporated into surgical procedures to make them safer and more efficient, subsequently to obtain better outcomes for patients. In this paper, we review basic AI research regarding surgery and discuss the potential for implementing AI technology in gastric cancer surgery. At present, research and development is focused on AI technologies that assist the surgeon's understandings and judgment during surgery, such as anatomical navigation. AI systems are also being developed to recognize in which the surgical phase is ongoing. Such a surgical phase recognition systems is considered for effective storage of surgical videos and education, in the future, for use in systems to objectively evaluate the skill of surgeons. At this time, it is not considered practical to let AI make intraoperative decisions or move forceps automatically from an ethical standpoint, too. At present, AI research on surgery has various limitations, and it is desirable to develop practical systems that will truly benefit clinical practice in the future.

• Journal of Gastric Cancer
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• v.23 no.3
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• pp.410-427
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• 2023

Recent advances in artificial intelligence (AI) have provided novel tools for rapid and precise pathologic diagnosis. The introduction of digital pathology has enabled the acquisition of scanned slide images that are essential for the application of AI. The application of AI for improved pathologic diagnosis includes the error-free detection of potentially negligible lesions, such as a minute focus of metastatic tumor cells in lymph nodes, the accurate diagnosis of potentially controversial histologic findings, such as very well-differentiated carcinomas mimicking normal epithelial tissues, and the pathological subtyping of the cancers. Additionally, the utilization of AI algorithms enables the precise decision of the score of immunohistochemical markers for targeted therapies, such as human epidermal growth factor receptor 2 and programmed death-ligand 1. Studies have revealed that AI assistance can reduce the discordance of interpretation between pathologists and more accurately predict clinical outcomes. Several approaches have been employed to develop novel biomarkers from histologic images using AI. Moreover, AI-assisted analysis of the cancer microenvironment showed that the distribution of tumor-infiltrating lymphocytes was related to the response to the immune checkpoint inhibitor therapy, emphasizing its value as a biomarker. As numerous studies have demonstrated the significance of AI-assisted interpretation and biomarker development, the AI-based approach will advance diagnostic pathology.

• Journal of the Korean Society of Radiology
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• v.83 no.3
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• pp.473-485
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• 2022

The incidence of neurodegenerative diseases in the older population has increased in recent years. A considerable number of studies have been performed to characterize these diseases. Imaging analysis is an important biomarker for the diagnosis of neurodegenerative disease. Objective and reliable assessment and precise detection are important for the early diagnosis of neurodegenerative diseases. Artificial intelligence (AI) using brain MRI applied to the study of neurodegenerative diseases could promote early diagnosis and optimal decisions for treatment plans. MRI-based AI software have been developed and studied worldwide. Representatively, there are MRI-based volumetry and segmentation software. In this review, we present the development process of brain volumetry analysis software in neurodegenerative diseases, currently used and developed AI software for neurodegenerative disease in the Republic of Korea, probable uses of AI in the future, and AI software limitations.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.15
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• pp.6359-6364
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• 2015

Background: Preclinical studies have shown that the combination of an aromatase inhibitor (AI) and capecitabine in estrogen receptor (ER)- positive cell lines enhance antitumor efficacy. This retrospective analysis of a group of patients with metastatic breast cancer (MBC) evaluated the efficacy and safety of combined AI with capecitabine. Materials and Methods: Patients with hormone receptor-positive metastatic breast cancer treated between 1st January 2005 and 31st December 2010 with a combination of capecitabine and AI were evaluated and outcomes were compared with those of women treated with capecitabine in conventional dose or AI as a monotherapy. Results: Of 72 patients evaluated, 31 received the combination treatment, 22 AI and 19 capecitabine. The combination was used in 20 patients as first-line and 11 as second-line treatment. Mean age was 46.2 years with a range of 28-72 years. At the time of progression, 97% had a performance status of <2 and 55% had visceral disease. No significant difference was observed between the three groups according to clinical and pathological features. Mean follow up was 38 months with a range of 16-66 months. The median PFS of first-line treatment was significantly better for the combination (PFS 21 months vs 8.0 months for capecitabine and 15.0 months for AI). For second-line treatment, the PFS was longer in the combination compared with capecitabine and Al groups (18 months vs. 5.0 months vs. 11.0 months, respectively). Median 2 year and 5 year survival did not show any significant differences among combination and monotherapy groups. The most common adverse events for the combination group were grade 1 and 2 hand-for syndrome (69%), grade 1 fatigue (64%) and grade 1 diarrhoea (29%). Three grade 3 hand-foot syndrome events were reported. Conclusions: Combination treatment with capecitabine and AI used as a first line or second line treatment was safe with much lowered toxicity. Prospective randomized clinical trials should evaluate the use of combination therapy in advanced breast cancer to confirm these findings.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.8
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• pp.5009-5022
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• 2014

The purpose of this study was to obtain the serum lipid levels according to the lifestyles, and examine the influence of lifestyles on the serum lipid levels among adults who examined the health checkup in an university hospital. The subjects for this study were 4,112 adults who underwent medical examinations at the health center of a university hospital in Daejeon city from Jan 2012 to Dec 2013. The lifestyles and serum lipid levels of study subjects were obtained from self-recorded questionnaires and medical examination charts of the hospital. As a result, the mean values of the serum lipid levels (TC, HDL-C. LDL-C and TG) and atherogenic index (AI) of the study subjects showed a significantly difference according to the lifestyle, such as age, alcohol consumption, smoking, regular exercise, overeating and meat consumption in both sexes. The TC, HDL-C. LDL-C, TG and AI showed a positive correlation with age, AUDIT score, but the HPI score showed a negative correlation in both sexes. In the age-adjusted odds ratio, the risk ratio of an abnormality of TC, HDL-C. LDL-C, TG and AI increased significantly because there was an increase in the group of everyday overeating and meat consumption, smoking group, no exercise group, and low HPI group than their respective counterparts in both sexes. The above results suggested that the serum lipid levels of the subjects was closely related to increasing age, and lifestyles, such as alcohol consumption, smoking, regular exercise, overeating, and meat consumption.

• Journal of Physiology & Pathology in Korean Medicine
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• v.35 no.1
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• pp.22-27
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• 2021

This study conducted a questionnaire for students of Pusan National University Graduate School of Korean Medicine who practiced using the Oriental Medicine Diagnosis System (ODS). From the questionnaire, this study investigated current state of application and perception of AI in Korean Medicine and explored the direction of ODS improvement and utilization. The survey questions consisted of six questions examining the satisfaction of the diagnostic expert system, five questions evaluating the availability of the diagnostic expert system, and six questions to predict the impact of AI on the Korean medicine community. The survey analysis showed high satisfaction with practice using ODS. On the other hand, the possibility of using ODS, especially in clinical use, was evaluated as relatively low compared to the satisfaction of the practice. Therefore, the overall impact of AI on the Korean medical community is not expected to be large. Although there are difficulties in standardization of clinical data due to the academic characteristics of Korean medicine, it is necessary to continue attempts to apply AI. By actively introducing educational tools using the latest AI techniques to the diagnosis experience and doctor-patient role in a practice, students will be able to increase their satisfaction with their practice and respond appropriately to the state-of-the-art medical environment.

• Tuberculosis and Respiratory Diseases
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• v.84 no.2
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• pp.115-124
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• 2021

Background: This study aimed to determine the parameters for worsening oxygenation in non-severe coronavirus disease 2019 (COVID-19) pneumonia. Methods: This retrospective cohort study included cases of confirmed COVID-19 pneumonia in a public hospital in South Korea. The worsening oxygenation group was defined as that with SpO₂ ≤94% or received oxygen or mechanical ventilation (MV) throughout the clinical course versus the non-worsening oxygenation group that did not experience any respiratory event. Parameters were compared, and the extent of viral pneumonia from an initial chest computed tomography (CT) was calculated using artificial intelligence (AI) and measured visually by a radiologist. Results: We included 136 patients, with 32 (23.5%) patients in the worsening oxygenation group; of whom, two needed MV and one died. Initial vital signs and duration of symptoms showed no difference between the two groups; however, univariate logistic regression analysis revealed that a variety of parameters on admission were associated with an increased risk of a desaturation event. A subset of patients was studied to eliminate potential bias, that ferritin ≥280 ㎍/L (p=0.029), lactate dehydrogenase ≥240 U/L (p=0.029), pneumonia volume (p=0.021), and extent (p=0.030) by AI, and visual severity scores (p=0.042) were the predictive parameters for worsening oxygenation in a sex-, age-, and comorbid illness-matched case-control study using propensity score (n=52). Conclusion: Our study suggests that initial CT evaluated by AI or visual severity scoring as well as serum markers of inflammation on admission are significantly associated with worsening oxygenation in this COVID-19 pneumonia cohort.

• Korean Journal of Adult Nursing
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• v.22 no.1
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• pp.60-69
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• 2010

Purpose: The aim of this study was to investigate the level of resilience and related factors in patients with Parkinson's disease (PD) in Korea. Methods: Data were obtained from 148 patients using the Resilience Scale (RS), Beck's Depression Inventory (BDI), and Spielberger's Anxiety Inventory (AI). Results: The mean scores of the RS, BDI, and AI were $127.7{\pm}21.6$, $12.9{\pm}9.3$, and $41.9{\pm}11.1$, respectively. The RS score was strongly correlated with the BDI score (r=-.531, p<.001) and the AI (r=-.572, p<.001). The resilience was significantly revealed by household income (F=4.002, p=.009) and presence of a hobby (t=-3.300, p=.001). In addition, resilience was significantly correlated with age of disease onset (r=.164, p=.046), years of living with PD (r=-.262, p=.001), and the length of treatment with levodopa (r=-.283, p<.001). From the stepwise multiple regression analysis, the most important factors related to the RS score were the AI score, household income, and length of treatment with levodopa. Conclusion: Understanding these factors is essential for developing effective interventions to improve resilience in patients with PD.