Purpose: This developmental study explored the conceptual feasibility and applicability of a metaverse-based clinical assessment platform as a complementary tool to conventional objective structured clinical examinations in undergraduate medical education. Methods: A targeted literature review and expert consensus process were conducted to identify domains of clinical competence in which metaverse technologies could provide added value. Based on these findings, prototype virtual patient simulations were developed within a metaverse environment. Large language models (LLMs) were integrated to support dynamic, interactive history-taking simulations, and pilot modules for physical examination were also created. Results: Integration of LLMs into virtual patient scenarios enabled realistic, context-sensitive medical interviews, facilitating interactive dialogue between examinees and simulated patients. In contrast, physical examination modules faced technical limitations, particularly in replicating procedures requiring tactile or haptic feedback, such as palpation and percussion. Nevertheless, the metaverse environment enabled delivery of consistent and reproducible scenarios, supporting objective assessment of communication and diagnostic reasoning skills. Conclusion Metaverse-based simulations augmented by LLMs offer a promising approach to scalable and standardized clinical assessment, particularly within cognitive and interpersonal competency domains. Although current technological constraints limit the fidelity of physical examination simulations, rapid advancements in immersive and haptic technologies may help overcome these barriers in the near future. Further research is needed to evaluate the educational efficacy, validity, and feasibility of deploying such platforms in summative, high-stakes assessment contexts.

Purpose: Accurate anatomical knowledge and precise visualization are critical during liver surgery. We developed augmented reality (AR) software that overlays digital 3-dimensional (3D) models onto laparoscopic or robotic views, providing real-time visual aids for surgical navigation during 3D laparoscopic and robotic liver surgeries. This study assesses the accuracy of manual registration and the subjective perception of this AR software by the operator. Methods: Ten consecutive patients undergoing 3D laparoscopic or robotic liver surgery from December 2023 to February 2024 were selected for application of the AR software during surgery. Manual registration accuracy was quantified postregistration using the Dice similarity coefficient (DSC) to compare the stereoscopic and virtual liver images. A 6-question operator survey, using a 5-point Likert scale, was administered after each surgery to evaluate the software’s helpfulness in clinical settings. Results: Seven males and 3 females (mean age, 62.4 ± 6.4 years) underwent liver surgery (3D laparoscopic, 5; robotic, 5).Surgical procedures included 4 right hemihepatectomies, 1 extended left hemihepatectomy, 1 left lateral sectionectomy, and 4 segmentectomies. The mean tumor size was 4.4 ± 2.2 cm (range, 1.0–7.5 cm). The mean DSC was 0.912 ± 0.052 (range, 0.879–0.954). The operator rated registration alignment favorably before (mean score, 3.9 ± 1.1) and after mobilization (mean score, 4.1 ± 1.2). The software was reported as very helpful overall (mean score, 4.2 ± 0.8), and in locating blood vessels (4.2 ± 0.6) and tumors (4.3 ± 0.7). Conclusion Clinical application of the AR software during 3D laparoscopic and robotic liver surgery is feasible, with favorable registration accuracy and high operator perception of helpfulness.

Background/Aims: The Gout Impact Scale (GIS), a part of the Gout Assessment Questionnaire 2.0, is used to measure gout-specific health-related quality of life (HRQOL). Although several studies have been conducted on the factors affecting the HRQOL of patients with gout, few have focused on lifestyle factors. This study aimed to investigate the correlation between lifestyle habits and HRQOL using the GIS in patients with gout. Methods: We used data from the Urate-Lowering TheRApy in Gout (ULTRA) registry, a prospective cohort of Korean patients with gout treated at multiple centers nationwide. The patients were aged ≥18 years and met the 2015 American College of Rheumatology/European League Against Rheumatism gout classification criteria. They were asked to complete a GIS and questions regarding their lifestyle habits at enrollment. Results: The study included 232 patients. ‘Gout concern overall’ scores in the GIS were significantly lower in patients who exercised more frequently and consumed soft drinks and meat less, and ‘well-being during attack’ scores were significantly lower in patients who consumed vegetables and exercised more frequently. The frequency of vegetable consumption had a negative linear relationship with the ‘well-being during attack’ and ‘gout concern during attack’ scores (p = 0.01, p = 0.001, respectively). The frequency of exercise had a negative linear relationship with the ‘gout concern overall’ and ‘gout concern during attack’ scores (p = 0.04 and p = 0.002, respectively). Conclusions Patients with gout who frequently consumed vegetables and exercised regularly experienced less impact of gout, exhibiting a better GIS that represented HRQOL.

Background/Aims: We investigated how interactions between humans and computer-aided detection (CADe) systems are influenced by the user’s experience and polyp characteristics. Methods: We developed a CADe system using YOLOv4, trained on 16,996 polyp images from 1,914 patients and 1,800 synthesized sessile serrated lesion (SSL) images. The performance of polyp detection with CADe assistance was evaluated using a computerized test module. Eighteen participants were grouped by colonoscopy experience (nurses, fellows, and experts). The value added by CADe based on the histopathology and detection difficulty of polyps were analyzed. Results: The area under the curve for CADe was 0.87 (95% confidence interval [CI], 0.83 to 0.91). CADe assistance increased overall polyp detection accuracy from 69.7% to 77.7% (odds ratio [OR], 1.88; 95% CI, 1.69 to 2.09). However, accuracy decreased when CADe inaccurately detected a polyp (OR, 0.72; 95% CI, 0.58 to 0.87). The impact of CADe assistance was most and least prominent in the nurses (OR, 1.97; 95% CI, 1.71 to 2.27) and the experts (OR, 1.42; 95% CI, 1.15 to 1.74), respectively. Participants demonstrated better sensitivity with CADe assistance, achieving 81.7% for adenomas and 92.4% for easy-to-detect polyps, surpassing the standalone CADe performance of 79.7% and 89.8%, respectively. For SSLs and difficult-to-detect polyps, participants' sensitivities with CADe assistance (66.5% and 71.5%, respectively) were below those of standalone CADe (81.1% and 74.4%). Compared to the other two groups (56.1% and 61.7%), the expert group showed sensitivity closest to that of standalone CADe in detecting SSLs (79.7% vs 81.1%, respectively). Conclusions CADe assistance boosts polyp detection significantly, but its effectiveness depends on the user’s experience, particularly for challenging lesions.

Objectives: The purpose of this study was to identify any difference in user experience between tablet- and augmented reality (AR) glasses-based tele-exercise programs in elderly women. Methods: Participants in the AR group (n = 14) connected Nreal glasses with smartphones to display a pre-recorded exercise program, while each member of the tablet group (n = 13) participated in the same exercise program using an all-in-one personal computer. The program included sitting or standing on a chair, bare-handed calisthenics, and muscle strengthening using an elastic band. The exercise movements were presented first for the upper and then the lower extremities, and the total exercise time was 40 minutes (5 minutes of warm-up exercises, 30 minutes of main exercises, and 5 minutes of cool-down exercises). To evaluate the user experience, a questionnaire consisting of a 7-point Likert scale was used as a measurement tool. In addition, the Wilcoxon rank-sum test was used to assess differences between the two groups. Results: Of the six user experience scales, attractiveness (p = 0.114), stimulation (p = 0.534), and novelty (p = 0.916) did not differ significantly between the groups. However, efficiency (p = 0.006), perspicuity (p = 0.008), and dependability (p = 0.049) did vary significantly between groups. Conclusions When developing an AR glasses-based exercise program for the elderly, the efficiency, clarity, and stability of the program must be considered to meet the participants’ needs.

Objectives: As the Fourth Industrial Revolution advances, there is a growing interest in digital technology. In particular, the use of digital therapeutics (DTx) in healthcare is anticipated to reduce medical expenses. However, analytical research on DTx is still insufficient to fuel momentum for future DTx development. The purpose of this article is to analyze representative cases of different types of DTx from around the world and to propose a classification system. Methods: In this exploratory study examining DTx interaction types and representative cases, we conducted a literature review and selected seven interaction types that were utilized in a large number of cases. Then, we evaluated the specific characteristics of each DTx mechanism by reviewing the relevant literature, analyzing their indications and treatment components. A representative case for each mechanism was provided. Results: Cognitive behavioral therapy, distraction therapy, graded exposure therapy, reminiscence therapy, art therapy, therapeutic exercise, and gamification are the seven categories of DTx interaction types. Illustrative examples of each variety are provided. Conclusions Efforts from both the government and private sector are crucial for success, as standardization can decrease both the expense and the time required for government-led DTx development. The private sector should partner with medical facilities to stimulate potential demand, carry out clinical research, and produce scholarly evidence.

Objectives: Smart hospitals involve the application of recent information and communications technology (ICT) innovations to medical services; however, the concept of a smart hospital has not been rigorously defined. In this study, we aimed to derive the definition and service types of smart hospitals and investigate cases of each type. Methods: A literature review was conducted regarding the background and technical characteristics of smart hospitals. On this basis, we conducted a focus group interview with experts in hospital information systems, and ultimately derived eight smart hospital service types. Results: Smart hospital services can be classified into the following types: services based on location recognition and tracking technology that measures and monitors the location information of an object based on short-range communication technology; high-speed communication network-based services based on new wireless communication technology; Internet of Things-based services that connect objects embedded with sensors and communication functions to the internet; mobile health services such as mobile phones, tablets, and wearables; artificial intelligence-based services for the diagnosis and prediction of diseases; robot services provided on behalf of humans in various medical fields; extended reality services that apply hyper-realistic immersive technology to medical practice; and telehealth using ICT. Conclusions Smart hospitals can influence health and medical policies and create new medical value by defining and quantitatively measuring detailed indicators based on data collected from existing hospitals. Simultaneously, appropriate government incentives, consolidated interdisciplinary research, and active participation by industry are required to foster and facilitate smart hospitals.

Purpose: Bladder capacity is an important parameter in the diagnosis of lower urinary tract dysfunction. We aimed to determine whether the maximum bladder capacity (MCC) measured during a urodynamic study was affected by involuntary detrusor contraction (IDC) in patients with Lower Urinary Tract Symptoms (LUTS)/Benign Prostatic Hyperplasia (BPH). Methods: Between March 2020 and April 2021, we obtained maximum voided volume (MVV) from a 3-day frequency-volume chart, MCC during filling cystometry, and maximum anesthetic bladder capacity (MABC) during holmium laser enucleation of the prostate under spinal or general anesthesia in 139 men with LUTS/BPH aged >50 years. Patients were divided according to the presence of IDC during filling cystometry. We assumed that the MABC is close to the true value of the MCC, as it is measured under the condition of minimizing neural influence over the bladder. Results: There was no difference in demographic and clinical characteristics between the non-IDC (n=20) and IDC groups (n=119) (mean age, 71.5±7.4) (P>0.05). The non-IDC group had greater bladder volume to feel the first sensation, first desire, and strong desire than the IDC group (P<0.001). In all patients, MABC and MVV were correlated (r=0.41, P<0.001); however, there was no correlation between MCC and MABC (r=0.19, P=0.02). There was no significant difference in MABC between the non-IDC and IDC groups (P=0.19), but MVV and MCC were significantly greater in the non-IDC group (P<0.001). There was no significant difference between MABC and MVV (MABC-MVV, P=0.54; MVV/MABC, P=0.07), but there was a significant difference between MABC and MCC between the non-IDC and IDC groups (MABC-MCC, P<0.001; MCC/MABC, P<0.001). Conclusions Maximum bladder capacity from a urodynamic study does not represent true bladder capacity because of involuntary contractions.

Objectives: The purpose of this study was to explore new ways of creating value in the medical field and to derive recommendations for the role of medical institutions and the government. Methods: In this paper, based on expert discussion, we classified Internet of Things (IoT) technologies into four categories according to the type of information they collect (location, environmental parameters, energy consumption, and biometrics), and investigated examples of application. Results: Biometric IoT diagnoses diseases accurately and offers appropriate and effective treatment. Environmental parameter measurement plays an important role in accurately identifying and controlling environmental factors that could be harmful to patients. The use of energy measurement and location tracking technology enabled optimal allocation of limited hospital resources and increased the efficiency of energy consumption. The resulting economic value has returned to patients, improving hospitals’ cost-effectiveness. Conclusions Introducing IoT-based technology to clinical sites, including medical institutions, will enhance the quality of medical services, increase patient safety, improve management efficiency, and promote patient-centered medical services. Moreover, the IoT is expected to play an active role in the five major tasks of facility hygiene in medical fields, which are all required to deal with the COVID-19 pandemic: social distancing, contact tracking, bed occupancy control, and air quality management. Ultimately, the IoT is expected to serve as a key element for hospitals to perform their original functions more effectively. Continuing investments, deregulation policies, information protection, and IT standardization activities should be carried out more actively for the IoT to fulfill its expectations.

Objectives: This paper proposes a method for computer-assisted diagnosis of coronavirus disease 2019 (COVID-19) through chest X-ray imaging using a deep learning model without writing a single line of code using the Konstanz Information Miner (KNIME) analytics platform. Methods: We obtained 155 samples of posteroanterior chest X-ray images from COVID-19 open dataset repositories to develop a classification model using a simple convolutional neural network (CNN). All of the images contained diagnostic information for COVID-19 and other diseases. The model would classify whether a patient was infected with COVID-19 or not. Eighty percent of the images were used for model training, and the rest were used for testing. The graphic user interface-based programming in the KNIME enabled class label annotation, data preprocessing, CNN model training and testing, performance evaluation, and so on. Results: 1,000 epochs training were performed to test the simple CNN model. The lower and upper bounds of positive predictive value (precision), sensitivity (recall), specificity, and f-measure are 92.3% and 94.4%. Both bounds of the model’s accuracies were equal to 93.5% and 96.6% of the area under the receiver operating characteristic curve for the test set. Conclusions In this study, a researcher who does not have basic knowledge of python programming successfully performed deep learning analysis of chest x-ray image dataset using the KNIME independently. The KNIME will reduce the time spent and lower the threshold for deep learning research applied to healthcare.