The global increase in obesity highlights the need for accurate tools to assess body composition and monitor treatment efficacy.Traditional metrics, including body mass index and waist circumference, offer limited precision for fat quantification. Imagingbased techniques capable of visualizing internal structures are increasingly being recognized for their ability to provide comprehensive fat assessment. This review outlines the principles, strengths, and limitations of key modalities, including dualenergy X-ray absorptiometry (DXA), CT, MRI, and bioelectrical impedance analysis (BIA). DXA is cost-effective and accessible for population-level screening, whereas CT and MRI offer higher precision, particularly for visceral fat assessment. BIA is a practical, low-cost alternative, but it is limited by variability and lack of standardization. Regulatory agencies, including the US Food and Drug Administration and the Korean Ministry of Food and Drug Safety, are increasingly supporting imaging-based endpoints in clinical trials of anti-obesity drugs because these methods capture fat mass reduction beyond total weight loss. Recent phase III trials of semaglutide and tirzepatide have underscored the utility of DXA and CT in quantifying fat loss and preserving lean mass. Selecting appropriate imaging modalities based on technical capabilities and regulatory considerations can improve the evaluation of obesity treatments and strengthen the design of anti-obesity drug trials.

Objective: To evaluate the accuracy and reasoning capabilities of large multimodal language models compared with those of neuroradiology subspecialty-trained radiologists in neuroradiology case interpretation. Materials and Methods: This experimental study used custom-made 401 radiologic quizzes derived from articles published in RadioGraphics covering neuroradiology and head and neck topics (October 2020 to February 2024). We prompted the GPT-4 Turbo with Vision (GPT-4V), GPT-4 Omni, Gemini Flash, and Claude models to provide the top three differential diagnoses with a rationale and describe examination characteristics such as imaging modality, sequence, use of contrast, image plane, and body part. The temperature was adjusted to 0 and 1 (T1). Two neuroradiologists answered the same questions.The accuracies of the large language models (LLMs) and the neuroradiologists were compared using generalized estimating equations. Three neuroradiologists assessed the rationale provided by the LLMs for their differential diagnoses using four-point scales, separately for specific lesion locations and imaging findings, and evaluated the presence of hallucinations and the overall acceptability of the responses. Results: Top-3 accuracy (i.e., correct answers present among top-3 differential diagnoses) of LLMs ranged from 29.9% (120 of 401) to 49.4% (198 of 401, obtained with GPT-4V in the T1 setting), while radiologists achieved 80.3% (322 of 401) and 68.3% (274 of 401), respectively (P < 0.001). Regarding the rationale for differential diagnoses, GPT-4V (T1) accurately identified both the specific lesion location and imaging findings in 30.7% (123 of 401) and 12.9% (16 of 124) of cases without textual clinical history. Hallucinations occurred in 4.5% (18 of 401), and only 29.4% (118 of 401) of the LLM-generated analyses were deemed acceptable. GPT-4V (T1) demonstrated high accuracy in identifying the imaging modality (97.4% [800 of 821]) and scanned body parts (92.2% [756 of 820]). Conclusion LLMs remarkably underperformed compared with neuroradiologists and showed unsatisfactory reasoning for their differential diagnoses, with performance declining further in cases without textual input of clinical history. These findings highlight the limitations of current multimodal LLMs in neuroradiological interpretation and their reliance on text input.

Background: and Purpose Criteria for amyloid-related imaging abnormalities in anti-amyloid therapy are based on T2* gradient-recalled echo (GRE), but susceptibility-weighted imaging (SWI) is widely used, creating uncertainty. This study quantitatively compared the detectability of SWI and GRE for cerebral microbleeds and established evidence supporting distinct microbleed criteria for each. Methods: A systematic review and meta-analysis were conducted following PRISMA guidelines. PubMed and Embase were searched for studies directly comparing SWI and GRE up to August 8, 2024. Study quality was assessed with QUADAS-2. The pooled proportion of microbleed detection and detection ratio were calculated. Subgroup analyses were performed based on magnetic field strength (1.5 T vs. 3 T) and SWI slice thickness (<2 mm vs. ≥2 mm), equipment vendor, and study quality. Results: Thirteen studies were included. SWI detected cerebral microbleeds approximately 1.6times more effectively than GRE. At 3.0 T and 1.5 T, SWI exhibited 1.7-fold and 1.5-fold greater detectability, respectively. SWI with thinner slices (<2 mm) showed a 1.9-fold improvement, while thicker slices (≥2 mm) showed a 1.3-fold improvement. Subgroup analyses revealed no significant differences between vendors (0.61 vs. 0.60, p=0.89), or by study quality (0.61 vs. 0.59,p=0.89). Conclusions SWI detects cerebral microbleeds about 1.6 times more effectively than GRE, highlighting important differences between the two techniques. Cautious exploration of adjusted thresholds may be needed, and prospective validation in therapy-specific cohorts will be essential before clinical application.

Lecanemab and donanemab have received full U.S. Food and Drug Administration (FDA) approval, and subsequently, lecanemab has been approved by the Korean FDA and it has recently entered commercial use in Korea. This has increased interest in anti-amyloid immunotherapy for Alzheimer’s disease. Anti-amyloid immunotherapy has shown potential to modify the progression of the disease by specifically binding to amyloid β, a key pathological product in Alzheimer’s disease, and eliminating accumulated amyloid plaques in the brain. However, this treatment can be accompanied by a side-effect, amyloid-related imaging abnormalities (ARIA), which requires periodic monitoring by MRI. It is crucial to detect ARIA and accurately assess the severity by radiology. The role of the radiologist is important in this context, requiring proficiency in basic knowledge of ARIA, and in diagnosing/evaluating ARIA. This review aims to comprehensively cover aspects of ARIA, including its definition, pathophysiology, incidence, risk factors, assessment of severity by radiology, differential diagnosis, and management.

The introduction of anti-amyloid therapies for Alzheimer’s disease (AD), such as lecanemab (Lequembi®), which was recently approved in Korea, necessitates careful monitoring for amyloid-related imaging abnormalities (ARIA) using brain MRI. To optimize ARIA monitoring in Korean clinical settings, the Korean Society of Neuroradiology (KSNR) and the Age and Neurodegeneration Imaging (ANDI) Study Group proposed MRI protocol recommendations on essential MR sequences, MRI acquisition parameters, timing and condition of MRI examinations, and essential details to provide a scientific basis for maximizing the safety and efficacy of AD treatment. A customized, standardized MRI protocol focusing on Korea’s healthcare environment can improve ARIA management and ensure patient safety through early detection of potential anti-amyloid therapy side effects, thereby enhancing treatment quality.

Objective: To evaluate the adherence of large language model (LLM)-based healthcare research to the Minimum Reporting Items for Clear Evaluation of Accuracy Reports of Large Language Models in Healthcare (MI-CLEAR-LLM) checklist, a framework designed to enhance the transparency and reproducibility of studies on the accuracy of LLMs for medical applications. Materials and Methods: A systematic PubMed search was conducted to identify articles on LLM performance published in high-ranking clinical medicine journals (the top 10% in each of the 59 specialties according to the 2023 Journal Impact Factor) from November 30, 2022, through June 25, 2024. Data on the six MI-CLEAR-LLM checklist items: 1) identification and specification of the LLM used, 2) stochasticity handling, 3) prompt wording and syntax, 4) prompt structuring, 5) prompt testing and optimization, and 6) independence of the test data—were independently extracted by two reviewers, and adherence was calculated for each item. Results: Of 159 studies, 100% (159/159) reported the name of the LLM, 96.9% (154/159) reported the version, and 91.8% (146/159) reported the manufacturer. However, only 54.1% (86/159) reported the training data cutoff date, 6.3% (10/159) documented access to web-based information, and 50.9% (81/159) provided the date of the query attempts. Clear documentation regarding stochasticity management was provided in 15.1% (24/159) of the studies. Regarding prompt details, 49.1% (78/159) provided exact prompt wording and syntax but only 34.0% (54/159) documented prompt-structuring practices. While 46.5% (74/159) of the studies detailed prompt testing, only 15.7% (25/159) explained the rationale for specific word choices. Test data independence was reported for only 13.2% (21/159) of the studies, and 56.6% (43/76) provided URLs for internet-sourced test data. Conclusion Although basic LLM identification details were relatively well reported, other key aspects, including stochasticity, prompts, and test data, were frequently underreported. Enhancing adherence to the MI-CLEAR-LLM checklist will allow LLM research to achieve greater transparency and will foster more credible and reliable future studies.