This review summarizes the establishment of the 2025 Dietary Reference Intakes for Koreans (KDRIs), the third national standard for nutrient reference values in Korea. The 2025KDRIs build on lessons from revisions in 2010, 2015, and 2020, and chart a path forward by addressing 4 priorities: upgrading the scientific evidence base through systematic evaluation, strengthening intake monitoring using national survey data, advancing international harmonization, and responding to changes in the nutrition and health environment, including those associated with the coronavirus disease 2019 (COVID-19) pandemic. The scientific basis of the KDRIs was advanced by systematically evaluating the findings across exposure indicators, health assessment indicators, and the health outcomes, and reorganizing the indicators to estimate the nutrient requirements. Adequate Intake was set using explicit criteria when an Estimated Average Requirement could not be derived, data gaps, uncertain outcomes, and limited representativeness were documented. Key inputs, including coefficients of variation and uncertainty factors, as well as life stage estimation procedures, were re-evaluated in alignment with current evidence and international standards. The 2025 KDRIs incorporate intake evidence from the Korea National Health and Nutrition Examination Survey to inform policy and practice and support intake monitoring. For international harmonization, the NUQUEST-based literature framework was updated, and recent DRIs from other countries were compared. The shifts in anthropometric characteristics and dietary intake patterns observed during the COVID-19 pandemic were considered to reflect a changing context. The review identified remaining challenges for future revisions, including validating Koreanspecific indicators, developing evidence for infants and older adults, stronger translation of reference values into policy and practice, prioritizing of nutrients for future review within the 5-year revision cycle. Together, these advances will position the 2025 KDRIs as a science-based national reference integrating policy, practice, and evidence to support implementation aimed at improving nutritional status and healthy life expectancy in Korea.

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.

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.

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.

Symmetric drug-related intertriginous and flexural exanthema (SDRIFE) is a rare drug-induced skin reaction characterized by distinctive rashes. It presents as sharply demarcated erythema in “V” shape on the flexural areas such as the buttocks and the groin. Additionally, it can affect other flexural regions such as the axillae, popliteal fossae, and antecubital fossae. SDRIFE typically occurs within a few days following systemic drug exposure, without prior cutaneous sensitization. It is generally associated with a favorable prognosis with no systemic involvement. Consequently, treatment usually involves discontinuation of the offending drug and symptomatic management with antihistamines, with systemic corticosteroids rarely necessary. Herein, we report a case of severe SDRIFE that developed six weeks after denosumab administration and required long-term systemic corticosteroids.

Symmetric drug-related intertriginous and flexural exanthema (SDRIFE) is a rare drug-induced skin reaction characterized by distinctive rashes. It presents as sharply demarcated erythema in “V” shape on the flexural areas such as the buttocks and the groin. Additionally, it can affect other flexural regions such as the axillae, popliteal fossae, and antecubital fossae. SDRIFE typically occurs within a few days following systemic drug exposure, without prior cutaneous sensitization. It is generally associated with a favorable prognosis with no systemic involvement. Consequently, treatment usually involves discontinuation of the offending drug and symptomatic management with antihistamines, with systemic corticosteroids rarely necessary. Herein, we report a case of severe SDRIFE that developed six weeks after denosumab administration and required long-term systemic corticosteroids.

Symmetric drug-related intertriginous and flexural exanthema (SDRIFE) is a rare drug-induced skin reaction characterized by distinctive rashes. It presents as sharply demarcated erythema in “V” shape on the flexural areas such as the buttocks and the groin. Additionally, it can affect other flexural regions such as the axillae, popliteal fossae, and antecubital fossae. SDRIFE typically occurs within a few days following systemic drug exposure, without prior cutaneous sensitization. It is generally associated with a favorable prognosis with no systemic involvement. Consequently, treatment usually involves discontinuation of the offending drug and symptomatic management with antihistamines, with systemic corticosteroids rarely necessary. Herein, we report a case of severe SDRIFE that developed six weeks after denosumab administration and required long-term systemic corticosteroids.

Symmetric drug-related intertriginous and flexural exanthema (SDRIFE) is a rare drug-induced skin reaction characterized by distinctive rashes. It presents as sharply demarcated erythema in “V” shape on the flexural areas such as the buttocks and the groin. Additionally, it can affect other flexural regions such as the axillae, popliteal fossae, and antecubital fossae. SDRIFE typically occurs within a few days following systemic drug exposure, without prior cutaneous sensitization. It is generally associated with a favorable prognosis with no systemic involvement. Consequently, treatment usually involves discontinuation of the offending drug and symptomatic management with antihistamines, with systemic corticosteroids rarely necessary. Herein, we report a case of severe SDRIFE that developed six weeks after denosumab administration and required long-term systemic corticosteroids.

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.