Recent systematic reviews have raised concerns about the quality of reporting in studies evaluating the accuracy of large language models (LLMs) in medical applications. Incomplete and inconsistent reporting hampers the ability of reviewers and readers to assess study methodology, interpret results, and evaluate reproducibility. To address this issue, the MInimum reporting items for CLear Evaluation of Accuracy Reports of Large Language Models in healthcare (MI-CLEAR-LLM) checklist was developed. This article presents an extensively updated version. While the original version focused on proprietary LLMs accessed via web-based chatbot interfaces, the updated checklist incorporates considerations relevant to application programming interfaces and self-managed models, typically based on open-source LLMs. As before, the revised MI-CLEAR-LLM focuses on reporting practices specific to LLM accuracy evaluations: specifically, the reporting of how LLMs are specified, accessed, adapted, and applied in testing, with special attention to methodological factors that influence outputs. The checklist includes essential items across categories such as model identification, access mode, input data type, adaptation strategy, prompt optimization, prompt execution, stochasticity management, and test data independence. This article also presents reporting examples from the literature. Adoption of the updated MI-CLEAR-LLM can help ensure transparency in reporting and enable more accurate and meaningful evaluation of studies.

Breast lesions that do not meet the criteria for a mass on ultrasound (US), analogous to asymmetry on mammography or nonmass enhancement on MRI, are frequently encountered during diagnostic or screening US. These lesions, referred to as nonmass lesions (NMLs), are discrete areas of altered echotexture compared to surrounding breast tissue, lacking the threedimensionality or conspicuity of a mass. Their subtle nature makes it difficult-particularly for less experienced US operators-to distinguish between benign and malignant NMLs. With increasing clinical recognition, the upcoming sixth edition of the American College of Radiology Breast Imaging Reporting and Data System may include NMLs as a distinct diagnostic category. This article illustrates the sonographic features of NMLs and their pathologic correlations, providing extensive representative examples across benign NMLs, benign NMLs with upgrade potential, and malignant NMLs. In addition, it offers a practical and structured guide for a diagnostic approach to aid clinical management.

To evaluate the current status of cardiac magnetic resonance imaging (CMR) practice across Asian regions, guiding future clinical advancements and academic collaboration in CMR.

The trends in chest computed tomography (CT) utilization among patients with pneumonia and its association with pneumonia incidence and mortality remain unclear. This study aimed to investigate these trends and their associations in older adults.

Body-composition analysis (BCA) is gaining increasing clinical importance, because abnormalities in muscle and fat distribution are closely associated with patient outcomes for various diseases. Although several methods for assessing body composition are available, including bioelectrical impedance analysis, dual-energy X-ray absorptiometry, and magnetic resonance imaging, computed tomography (CT) has emerged as the most widely used imaging modality owing to its accuracy, accessibility, and artificial intelligence-driven automated analytical capabilities. CT-based BCA enables the precise quantification of skeletal muscle and adipose tissues, but its measurements can be influenced by various technical factors, such as the contrast phase, tube current and voltage, slice thickness, reconstruction algorithm, and scanner type. These parameters particularly affect attenuation-based metrics such as muscle density. Recent technological advancements, such as iterative reconstruction, dual-energy CT, and photon-counting CT, have resulted in new capabilities but may further introduce variability. This review summarizes the effects of CT parameters on BCA results and underscores the need for awareness and consistency when performing CT-based BCA. A better understanding of these factors may improve measurement reproducibility and support broader clinical and research applications.

To assess percutaneous radiofrequency ablation (RFA) techniques, radiation doses, and treatment outcomes for recurrent tumors of a single pediatric kidney.

Generative artificial intelligence (AI) systems can be used to draft automated chest X-ray (CXR) reports. Although promising in terms of efficiency and workforce shortages, their accuracy, reliability, and clinical utility remain uncertain. This article presents the Korean Society of Thoracic Radiology (KSTR) position statement on AI-assisted CXR report drafting, derived from a Delphi survey of experts who used the software on a modest case set.

Image-based dosimetry in transarterial radioembolization (TARE) has been proposed for the prediction of contralateral lobe hypertrophy as well as tumor response. This study aimed to evaluate the predictive value of post-treatment ⁹⁰Y PET/CT voxel-based dosimetry for post-TARE liver hypertrophy in treatment-naïve patients with hepatocellular carcinoma (HCC).

This study aimed to investigate the feasibility of pretreatment ⁶⁸Ga-labeled prostate-specific membrane antigen-11 ([⁶⁸Ga]-PSMA-11) PET/CT for predicting treatment response in patients with metastatic renal cell carcinoma (mRCC) undergoing first-line therapy with tyrosine kinase inhibitors (TKIs) in combination with immune checkpoint inhibitors (ICIs).

The integration of artificial intelligence (AI) into radiology has the potential to enhance diagnostic accuracy, streamline workflows, and improve patient outcomes. However, successful real-world adoption hinges on robust systems for ongoing monitoring to maintain safety, efficacy, and compliance with regulatory standards. This article delves into the critical need for such monitoring in radiology, examining current regulatory frameworks and proposing actionable strategies for overseeing technical performance, algorithm reliability, and human-AI interactions. Key topics include methods for aligning imaging studies with appropriate AI tools, addressing challenges related to data transmission and processing delays, and evaluating approaches to algorithm performance monitoring, ranging from vendor-based and specialized systems to in-house solutions. The potential of using large language models to help algorithm monitoring is also highlighted as a promising avenue. Additionally, the article explores human-AI interaction challenges, such as automation bias (the tendency of users to overly trust automated decisions), misuse, and underuse, offering strategies to mitigate these risks through structured protocols and ongoing education. By aligning regulatory requirements with practical implementation strategies, comprehensive AI monitoring can optimize diagnostic decision-making while ensuring patient safety.

Contrast-enhanced mammography (CEM) has increasingly been established as a valuable tool in breast imaging that enhances lesion detection and characterization by combining functional and anatomical information. This review highlights the recent key advances in CEM technology, explores its expanding clinical applications, and discusses the common interpretation pitfalls and current limitations. Instead of offering a comprehensive overview, this review focuses on providing a case-based perspective on emerging applications and how CEM can be efficiently incorporated into clinical practice. Through illustrative case examples, we offer practical insights into optimizing breast imaging strategies and demonstrate how CEM can effectively complement other imaging modalities in both routine practice and complex diagnostic scenarios.

Dynamic contrast-enhanced (DCE) MRI is an advanced imaging technique that involves intravenous administration of a contrast agent followed by serial imaging to characterize temporal enhancement patterns. This technique provides essential information on tissue vascularity, perfusion, and capillary permeability, which are essential for characterizing soft tissue lesions. DCE-MRI plays a valuable role in differentiating benign from malignant lesions, distinguishing neoplastic from non-neoplastic conditions, evaluating histological grades, and monitoring post-treatment changes by enabling both qualitative and quantitative assessments of tissue enhancement dynamics. This review provides a comprehensive overview of the technical principles of DCE-MRI, summarizes current analytical approaches, and discusses its clinical applications in the evaluation of soft tissue tumors and tumor-like lesions.

To evaluate the diagnostic potential of viscosity (Vi) imaging and shear wave elastography (SWE) of the tibial nerve in diabetic peripheral neuropathy (DPN).