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: During transoral endoscopic thyroidectomy, preserving the recurrent laryngeal nerve (RLN) is a major challenge because visualization of this nerve is often obstructed by the thyroid itself, increasing the risk of serious complications.This study explores the application of an augmented reality (AR) system to facilitate easier identification of the RLN during transoral endoscopic thyroidectomy. Methods: Three patients scheduled for transoral endoscopic thyroidectomy were enrolled in this proof-of-concept study. Preoperative computed tomography scans were used to create an AR model that included the thyroid, trachea, veins, arteries, and RLN. The model was overlaid onto real-time endoscopic camera images during live surgeries.Manual registration of the AR model was performed using a customized controller. The model was aligned with surgical landmarks such as the trachea and common carotid artery. Manual registration accuracy was assessed using the Dice similarity coefficient (DSC) to evaluate the alignment between the real RLN and the RLN of the AR model. Results: The 3 patients included were female (mean age, 33.3 ± 15.7 years), and the mean tumor size was 1.0 ± 0.3 cm. All patients underwent transoral endoscopic thyroidectomy of the right lobe. Final histopathological diagnoses comprised 2 papillary thyroid carcinomas and one follicular adenoma. The manual registration accuracy was 0.60, 0.70, and 0.57 for patients 1, 2, and 3, respectively, with a mean value of 0.6 ± 0.1. Conclusion The application of an AR system during transoral endoscopic thyroidectomy proved feasible and demonstrated potential for improving the localization of anatomical structures, particularly the RLN, as indicated by a moderate DSC.

Purpose: During transoral endoscopic thyroidectomy, preserving the recurrent laryngeal nerve (RLN) is a major challenge because visualization of this nerve is often obstructed by the thyroid itself, increasing the risk of serious complications.This study explores the application of an augmented reality (AR) system to facilitate easier identification of the RLN during transoral endoscopic thyroidectomy. Methods: Three patients scheduled for transoral endoscopic thyroidectomy were enrolled in this proof-of-concept study. Preoperative computed tomography scans were used to create an AR model that included the thyroid, trachea, veins, arteries, and RLN. The model was overlaid onto real-time endoscopic camera images during live surgeries.Manual registration of the AR model was performed using a customized controller. The model was aligned with surgical landmarks such as the trachea and common carotid artery. Manual registration accuracy was assessed using the Dice similarity coefficient (DSC) to evaluate the alignment between the real RLN and the RLN of the AR model. Results: The 3 patients included were female (mean age, 33.3 ± 15.7 years), and the mean tumor size was 1.0 ± 0.3 cm. All patients underwent transoral endoscopic thyroidectomy of the right lobe. Final histopathological diagnoses comprised 2 papillary thyroid carcinomas and one follicular adenoma. The manual registration accuracy was 0.60, 0.70, and 0.57 for patients 1, 2, and 3, respectively, with a mean value of 0.6 ± 0.1. Conclusion The application of an AR system during transoral endoscopic thyroidectomy proved feasible and demonstrated potential for improving the localization of anatomical structures, particularly the RLN, as indicated by a moderate DSC.

Purpose: During transoral endoscopic thyroidectomy, preserving the recurrent laryngeal nerve (RLN) is a major challenge because visualization of this nerve is often obstructed by the thyroid itself, increasing the risk of serious complications.This study explores the application of an augmented reality (AR) system to facilitate easier identification of the RLN during transoral endoscopic thyroidectomy. Methods: Three patients scheduled for transoral endoscopic thyroidectomy were enrolled in this proof-of-concept study. Preoperative computed tomography scans were used to create an AR model that included the thyroid, trachea, veins, arteries, and RLN. The model was overlaid onto real-time endoscopic camera images during live surgeries.Manual registration of the AR model was performed using a customized controller. The model was aligned with surgical landmarks such as the trachea and common carotid artery. Manual registration accuracy was assessed using the Dice similarity coefficient (DSC) to evaluate the alignment between the real RLN and the RLN of the AR model. Results: The 3 patients included were female (mean age, 33.3 ± 15.7 years), and the mean tumor size was 1.0 ± 0.3 cm. All patients underwent transoral endoscopic thyroidectomy of the right lobe. Final histopathological diagnoses comprised 2 papillary thyroid carcinomas and one follicular adenoma. The manual registration accuracy was 0.60, 0.70, and 0.57 for patients 1, 2, and 3, respectively, with a mean value of 0.6 ± 0.1. Conclusion The application of an AR system during transoral endoscopic thyroidectomy proved feasible and demonstrated potential for improving the localization of anatomical structures, particularly the RLN, as indicated by a moderate DSC.

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: 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: The objective of this study was to investigate the effects of a digital therapeutic exercise platform for pre-frail or frail elderly individuals using augmented reality (AR) technology accessed through glasses. A tablet-based exercise program was utilized for the control group, and a non-inferiority assessment was employed. Methods: The participants included older adult women aged 65 years and older residing in Incheon, South Korea. A digital therapeutic exercise program involving AR glasses or tablet-based exercise was administered twice a week for 12 weeks, with gradually increasing exercise duration. Statistical analysis was conducted using the t-test and Wilcoxon rank sum test for non-inferiority assessment. Results: In theprimary efficacy assessment, regarding the change in lower limb strength, a non-inferior result was observed for the intervention group (mean change, 5.46) relative to the control group (mean change, 4.83), with a mean difference of 0.63 between groups (95% confidence interval, –2.33 to 3.58). Changes in body composition and physical fitness-related variables differed non-significantly between the groups. However, the intervention group demonstrated a significantly greater increase in cardiorespiratory endurance (p < 0.005) and a significantly larger decrease in the frailty index (p < 0.001). Conclusions An AR-based digital therapeutic program significantly and positively contributed to the improvement of cardiovascular endurance and the reduction of indicators of aging among older adults. These findings underscore the value of digital therapeutics in mitigating the effects of aging.

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.