Background: This study analyzed the status of community-based practice in public healthcare institutions in Korean medical schools and identified related needs. Methods: We conducted an online cross-sectional survey in which 32 of 40 medical schools (80.0%) participated between March and April 2023. We developed questionnaire items aimed at ascertaining the status and perceptions of community-based practice and analyzed the response data using frequency analysis, the Mann-Whitney U test, and content analysis with word clouds. Results: Of the 32 medical schools analyzed, 23 (71.9%) offered practical courses, and of those, 12 (52.2%) implemented practice lessons within courses. Among 20 schools, 18 (90.0%) required students to complete practical courses and over 50% offered these courses in the third and fourth years of the clerkship phase. Perceptions of community practice showed no significant differences based on whether courses were offered. Many schools have proposed that practice should be a continuous curricular element from the premedical to medical years. The primary challenges facing community-based practice were identified as faculty development, collaboration, and compensation of practice institutions. The following words were extracted from the respondents’ comments: practice, community, institution, student, education, faculty, university, and public. Conclusion This study identified the limitations of community-based education provided by Korean medical schools and we report findings that highlight areas of improvement. Notable among these is the need to continuously incorporate community-based practice into medical education while engaging in discussions and conducting research toward developing a comprehensive and systematic curriculum.

Long coronavirus disease (COVID) is a condition in which coronavirus disease 2019 (COVID-19) symptoms persist for over 3 months, and currently poses a global public health challenge. Due to varying manifestations and lack of standardized definitions, diagnostic methods, and treatments, comprehensive clinical guidelines are required. This review article, summarizing research and expert consensus up to June 2023, provides recommendations for diagnosis and long-term management of long COVID symptoms. It emphasizes thorough patient evaluation, including medical history, physical examinations, and tests, and advocates vaccination and antiviral treatments to reduce risk. Guidelines for long COVID will be updated as new knowledge emerges.

Long coronavirus disease (COVID) is a condition in which coronavirus disease 2019 (COVID-19) symptoms persist for over 3 months, and currently poses a global public health challenge. Due to varying manifestations and lack of standardized definitions, diagnostic methods, and treatments, comprehensive clinical guidelines are required. This review article, summarizing research and expert consensus up to June 2023, provides recommendations for diagnosis and long-term management of long COVID symptoms. It emphasizes thorough patient evaluation, including medical history, physical examinations, and tests, and advocates vaccination and antiviral treatments to reduce risk. Guidelines for long COVID will be updated as new knowledge emerges.

Breast cancer is the most common cancer in women worldwide, and its early detection is critical for improving survival outcomes. As a diagnostic and screening tool, mammography can be less effective owing to the masking effect of fibroglandular tissue, but breast US has good sensitivity even in dense breasts. However, breast US is highly operator dependent, highlighting the need for artificial intelligence (AI)-driven solutions. Unlike other modalities, US is performed using a handheld device that produces a continuous real-time video stream, yielding 12000–48000 frames per examination. This can be significantly challenging for AI development and requires real-time AI inference capabilities. In this review, we classified AI solutions as computer-aided diagnosis and computer-aided detection to facilitate a functional understanding and review commercial software supported by clinical evidence.In addition, to bridge healthcare gaps and enhance patient outcomes in geographically under resourced areas, we propose a novel framework by reviewing the existing AI-based triage workflows including mobile ultrasound.

Gastric cancer is one of the most common cancers in both Korea and worldwide. Since 2004, the Korean Practice Guidelines for Gastric Cancer have been regularly updated, with the 4th edition published in 2022. The 4th edition was the result of a collaborative work by an interdisciplinary team, including experts in gastric surgery, gastroenterology, endoscopy, medical oncology, abdominal radiology, pathology, nuclear medicine, radiation oncology, and guideline development methodology. The current guideline is the 5th version, an updated version of the 4th edition. In this guideline, 6 key questions (KQs) were updated or proposed after a collaborative review by the working group, and 7 statements were developed, or revised, or discussed based on a systematic review using the MEDLINE, Embase, Cochrane Library, and KoreaMed database. Over the past 2 years, there have been significant changes in systemic treatment, leading to major updates and revisions focused on this area.Additionally, minor modifications have been made in other sections, incorporating recent research findings. The level of evidence and grading of recommendations were categorized according to the Grading of Recommendations, Assessment, Development and Evaluation system. Key factors for recommendation included the level of evidence, benefit, harm, and clinical applicability. The working group reviewed and discussed the recommendations to reach a consensus. The structure of this guideline remains similar to the 2022 version.Earlier sections cover general considerations, such as screening, diagnosis, and staging of endoscopy, pathology, radiology, and nuclear medicine. In the latter sections, statements are provided for each KQ based on clinical evidence, with flowcharts supporting these statements through meta-analysis and references. This multidisciplinary, evidence-based gastric cancer guideline aims to support clinicians in providing optimal care for gastric cancer patients.

Objective: To prospectively evaluate the effect of accelerated deep learning-based reconstruction (Accel-DL) on improving brain magnetic resonance imaging (MRI) quality and reducing scan time compared to that in conventional MRI. Materials and Methods: This study included 150 participants (51 male; mean age 57.3 ± 16.2 years). Each group of 50 participants was scanned using one of three 3T scanners from three different vendors. Conventional and Accel-DL MRI images were obtained from each participant and compared using 2D T1- and T2-weighted and 3D gradient-echo sequences. Accel-DL acquisition was achieved using optimized scan parameters to reduce the scan time, with the acquired images reconstructed using U-Net-based software to transform low-quality, undersampled k-space data into high-quality images. The scan times of Accel-DL and conventional MRI methods were compared. Four neuroradiologists assessed the overall image quality, structural delineation, and artifacts using Likert scale (5- and 3-point scales). Inter-reader agreement was assessed using Fleiss’ kappa coefficient. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated, and volumetric quantification of regional structures and white matter hyperintensities (WMHs) was performed. Results: Accel-DL showed a mean scan time reduction of 39.4% (range, 24.2%–51.3%). Accel-DL improved overall image quality (3.78 ± 0.71 vs. 3.36 ± 0.61, P < 0.001), structure delineation (2.47 ± 0.61 vs. 2.35 ± 0.62, P < 0.001), and artifacts (3.73 ± 0.72 vs. 3.71 ± 0.69, P = 0.016). Inter-reader agreement was fair to substantial (κ = 0.34–0.50). SNR and CNR increased in Accel-DL (82.0 ± 23.1 vs. 31.4 ± 10.8, P = 0.02; 12.4 ± 4.1 vs. 4.4 ± 11.2, P = 0.02). Bland-Altman plots revealed no significant differences in the volumetric measurements of 98.2% of the relevant regions, except in the deep gray matter, including the thalamus. Five of the six lesion categories showed no significant differences in WMH segmentation, except for leukocortical lesions (r = 0.64 ± 0.29). Conclusion Accel-DL substantially reduced the scan time and improved the quality of brain MRI in both spin-echo and gradientecho sequences without compromising volumetry, including lesion quantification.

Background: The aim of this study is to evaluate the prevalence and associated psychosocial factors of behavioral addictions among school adolescents living in the Gwangju and Jeonnam regions in Korea. Methods: A self-reported survey was conducted from December 4, 2023, to January 31, 2024, including 855 middle and high school students residing in the Gwangju-Jeonnam regions. Aside from the information on demographic characteristics, data on depression, anxiety, Internet gaming addiction, gambling problems, and resilience was obtained. Results: The prevalence of Internet gaming addiction among adolescents was 5.4%, while the prevalence of gambling problems was 3.3%. The male adolescents had a significantly higher risk of behavioral addiction compared with the female adolescents. The logistic regression analysis revealed that male and depression were significant risk factors for Internet gaming addiction. For gambling problems, male was identified as a significant risk factor. Conclusion The findings of this study suggested that the prevalence of behavioral addiction among school adolescents has been relatively higher than that of previous studies, emphasizing the need for community-based prevention and intervention strategies tailored to the sex difference and psychological factors associated with adolescent behavioral addictions.