Background: Large language models (LLMs) are increasingly integrated into medical education, with transformative potential for learning and assessment. However, their performance across diverse medical exams globally has remained underexplored. Objective: This study aims to introduce MedExamLLM, a comprehensive platform designed to systematically evaluate the performance of LLMs on medical exams worldwide. Specifically, the platform seeks to (1) compile and curate performance data for diverse LLMs on worldwide medical exams; (2) analyze trends and disparities in LLM capabilities across geographic regions, languages, and contexts; and (3) provide a resource for researchers, educators, and developers to explore and advance the integration of artificial intelligence in medical education. Methods: A systematic search was conducted on April 25, 2024, in the PubMed database to identify relevant publications. Inclusion criteria encompassed peer-reviewed, English-language, original research articles that evaluated at least one LLM on medical exams. Exclusion criteria included review articles, non-English publications, preprints, and studies without relevant data on LLM performance. The screening process for candidate publications was independently conducted by 2 researchers to ensure accuracy and reliability. Data, including exam information, data process information, model performance, data availability, and references, were manually curated, standardized, and organized. These curated data were integrated into the MedExamLLM platform, enabling its functionality to visualize and analyze LLM performance across geographic, linguistic, and exam characteristics. The web platform was developed with a focus on accessibility, interactivity, and scalability to support continuous data updates and user engagement. Results: A total of 193 articles were included for final analysis. MedExamLLM comprised information for 16 LLMs on 198 medical exams conducted in 28 countries across 15 languages from the year 2009 to the year 2023. The United States accounted for the highest number of medical exams and related publications, with English being the dominant language used in these exams. The Generative Pretrained Transformer (GPT) series models, especially GPT-4, demonstrated superior performance, achieving pass rates significantly higher than other LLMs. The analysis revealed significant variability in the capabilities of LLMs across different geographic and linguistic contexts. Conclusions: MedExamLLM is an open-source, freely accessible, and publicly available online platform providing comprehensive performance evaluation information and evidence knowledge about LLMs on medical exams around the world. The MedExamLLM platform serves as a valuable resource for educators, researchers, and developers in the fields of clinical medicine and artificial intelligence. By synthesizing evidence on LLM capabilities, the platform provides valuable insights to support the integration of artificial intelligence into medical education. Limitations include potential biases in the data source and the exclusion of non-English literature. Future research should address these gaps and explore methods to enhance LLM performance in diverse contexts.

Emerging data have highlighted the coexistence of multiple sclerosis (MS) and Alzheimer’s disease (AD), both of which are common central nervous system degenerative diseases with a heavy burden on patients, their families, and society. However, it is unclear how MS progresses under an AD pathological background. We aimed to address the question of how MS progresses under an AD pathological background. We induced the experimental autoimmune encephalomyelitis (EAE) model of MS in two types of AD mouse models, Tg6799 and APP/PS1 mice. We found that, compared with wild-type mice, the clinical symptoms of EAE were significantly ameliorated in APP/PS1 mice but not in Tg6799 mice. Moreover, a much lower level of serum Aβ was observed in Tg6799 mice. EAE clinical symptoms in Tg6799 and C57BL/6J mice were ameliorated by intraperitoneal injection of Aβ42. Peripheral administration of Aβ42 peptides was able to inhibit Th17 development in vivo, which is likely to occur through the inhibition of IL-6 production in dendritic cells. Our findings revealed that AD and EAE could coexist in the same mouse, and Aβ residing in peripheral circulation likely plays an anti-inflammatory role in preventing EAE progression. These findings reveal the potential benefit of Aβ, one of the supervillains of AD, at least in certain contexts.

Objectives: The selection of appropriate outcome measurement instruments (OMIs) in neurodegenerative disease (NDD) researches remains complex and often inconsistent. This study aims to consolidate knowledge on OMIs applied in NDD over the last two decades and to develop outcome measurement instruments in neurodegenerative diseases (NDDOMI), a web-based knowledge platform for OMIs selection. Methods: We collected clinical trials from the past two decades across six prevalent NDDs from ClinicalTrials.gov. OMIs were manually extracted, categorized, and normalized by experts. We analyzed OMI utilization patterns, cross-disease applicability, and temporal trends. Additionally, we developed NDDOMI, a knowledge resource platform, using Django, ECharts, and JavaScript. The usability of NDDOMI was assessed using the System Usability Scale and Net Promoter Score. Results: The NDDOMI platform provides an interface covering 4619 clinical trials and 2494 OMIs. On average, 4.4 instruments were utilized per trial. Fifteen instruments are shared across six NDDs. 76.75% of the trials employed more than one instruments, often in combination. A positive correlation was observed between the number of trials and the diversity of OMIs employed. The top 10 frequently employed instruments for each disease were identified, and their utilization has increased over time. The usability assessment demonstrated excellent user satisfaction with an average SUS score of 88.19. Conclusions: This study introduces a comprehensive analysis of OMI usage trends in NDD research and introduces NDDOMI platform for OMI navigation and selection. NDDOMI serves as a valuable resource for optimizing clinical research design and standardizing outcome measurement in NDD management. The NDDOMI platform is available at http://bioinf.org.cn:3134/index/ .

BACKGROUND Large language models (LLMs) are increasingly integrated into medical education, with transformative potential for learning and assessment. However, their performance across diverse medical exams globally has remained underexplored. OBJECTIVE This study aims to introduce MedExamLLM, a comprehensive platform designed to systematically evaluate the performance of LLMs on medical exams worldwide. Specifically, the platform seeks to (1) compile and curate performance data for diverse LLMs on worldwide medical exams; (2) analyze trends and disparities in LLM capabilities across geographic regions, languages, and contexts; and (3) provide a resource for researchers, educators, and developers to explore and advance the integration of artificial intelligence in medical education. METHODS A systematic search was conducted on April 25, 2024, in the PubMed database to identify relevant publications. Inclusion criteria encompassed peer-reviewed, English-language, original research articles that evaluated at least one LLM on medical exams. Exclusion criteria included review articles, non-English publications, preprints, and studies without relevant data on LLM performance. The screening process for candidate publications was independently conducted by 2 researchers to ensure accuracy and reliability. Data, including exam information, data process information, model performance, data availability, and references, were manually curated, standardized, and organized. These curated data were integrated into the MedExamLLM platform, enabling its functionality to visualize and analyze LLM performance across geographic, linguistic, and exam characteristics. The web platform was developed with a focus on accessibility, interactivity, and scalability to support continuous data updates and user engagement. RESULTS A total of 193 articles were included for final analysis. MedExamLLM comprised information for 16 LLMs on 198 medical exams conducted in 28 countries across 15 languages from the year 2009 to the year 2023. The United States accounted for the highest number of medical exams and related publications, with English being the dominant language used in these exams. The Generative Pretrained Transformer (GPT) series models, especially GPT-4, demonstrated superior performance, achieving pass rates significantly higher than other LLMs. The analysis revealed significant variability in the capabilities of LLMs across different geographic and linguistic contexts. CONCLUSIONS MedExamLLM is an open-source, freely accessible, and publicly available online platform providing comprehensive performance evaluation information and evidence knowledge about LLMs on medical exams around the world. The MedExamLLM platform serves as a valuable resource for educators, researchers, and developers in the fields of clinical medicine and artificial intelligence. By synthesizing evidence on LLM capabilities, the platform provides valuable insights to support the integration of artificial intelligence into medical education. Limitations include potential biases in the data source and the exclusion of non-English literature. Future research should address these gaps and explore methods to enhance LLM performance in diverse contexts.

BACKGROUND Most large language models (LLMs) have achieved passing scores on medical licensing examinations. However, most evaluations focus on single-question accuracy, overlooking performance on multistep patient management scenarios, such as making a diagnosis followed by a treatment plan. It is unclear if LLMs can maintain high performance on complete clinical cases. OBJECTIVE This study aimed to evaluate the performance of LLMs on multistep clinical cases and to investigate the impact of model size scaling and case complexity on performance stability. METHODS We curated a dataset of 189 unique clinical cases comprising 473 individual questions from the Chinese National Medical Licensing Examination. The number of questions related to each case ranged from 2 to 4. A physician with more than 8 years of clinical experience annotated the cases, confirming that 82.5% (156/189) contained sequentially dependent questions. We tested 4 representative LLMs: DeepSeek-R1, GPT-4o, Gemini-2.5-Flash, and Qwen2.5. We measured 2 metrics: the question pass rate (QPR) and the case pass rate (CPR), where a case was considered correct only if the model answered all its questions correctly. The consistency gap was calculated as the difference between QPR and CPR. Additionally, we tested Qwen2.5 at different parameter sizes (3B, 7B, 14B, and 32B) to examine the effect of model size. We also calculated the expected CPR and used the McNemar test with Bonferroni correction to compare model performance. RESULTS All LLMs achieved a QPR exceeding 83%. However, the CPR was lower for all models. DeepSeek-R1 achieved the highest QPR at 89.9% (425/473), with a CPR of 79.9% (151/189), corresponding to the smallest consistency gap at 10%. In contrast, GPT-4o exhibited a QPR of 83.5% (395/473) and a CPR of 65.6% (124/189), resulting in the largest consistency gap at 17.9%. DeepSeek-R1 and Qwen2.5-32B significantly outperformed GPT-4o in both QPR and CPR (P<.05). Observed and expected CPR values were closely aligned across models. Increasing the model size from 3B to 32B reduced the consistency gap by approximately 50%. As the number of questions per case increased, the consistency gap tended to increase across all evaluated LLMs. CONCLUSIONS Current LLMs exhibit high accuracy in answering individual questions, but their ability to correctly answer all questions within a complete clinical case is substantially lower. This study design evaluated independent question answering, with responses aggregated at the case level. The performance depends heavily on model scale and case complexity. LLMs should be used as support tools rather than independent decision-makers in medical education and clinical practice.