The realm of clinical medicine stands on the brink of a revolutionary breakthrough as large language models (LLMs) emerge as formidable allies, propelled by the prowess of deep learning and a wealth of clinical data. Yet, amidst the disquieting specter of misdiagnoses haunting the halls of medical treatment, LLMs offer a glimmer of hope, poised to reshape the landscape. However, their mettle and medical acumen, particularly in the crucible of real-world professional scenarios replete with intricate logical interconnections, re-main shrouded in uncertainty. To illuminate this uncharted territory, we present an audacious quantitative evaluation method, harnessing the ingenuity of tho-racic surgery questions as the litmus test for LLMs’ medical prowess. These clinical questions covering various diseases were collected, and a test format consisting of multi-choice questions and case analysis was designed based on the Chinese National Senior Health Professional Technical Qualification Examination. Five LLMs of different scales and sources were utilized to answer these questions, and evaluation and feedback were provided by professional thoracic surgeons. Among these models, GPT-4 demonstrated the highest performance with a score of 48.67 out of 100, achieving accuracies of 0.62, 0.27, and 0.63 in single-choice, multi-choice, and case-analysis questions, respectively. However, further improvement is still necessary to meet the passing threshold of the examination. Additionally, this paper analyzes the performance, advantages, disadvantages, and risks of LLMs, and proposes suggestions for improvement, providing valuable insights into the capabilities and limitations of LLMs in the specialized medical domain.

Background: A plethora of monkeypox papers have been published; however, pinpointing key and pivotal studies can be challenging amongst the ever-expanding literature. Bibliometric analyses are helpful in identifying the most influential articles and their impact pertinent to this field, which has helped mould the recognition and management of monkeypox. Methods: The Web of Science Core Collection (WoSCC) was searched on 27 October 2022. The top 100 most-cited articles on monkeypox were identified and evaluated by author, country, institution, type of articles, theme, journal of publication, keywords, and citations. Results: The top 100 most-cited studies were published between 1997 and 2022, and the 5-year period with the largest number of articles was 2007–2011. The median number of citations among the top 100 most-cited articles was 78.23. Of the top 100 most-cited studies, 91 were original articles, and nine were reviews, the median of annual citations was significantly higher in the review group than the original group, 7.86 (4.20–13.80) versus 4.50 (3.07–7.59; P=0.023). The 100 articles were classified into different research theme, with the top three being Immunology (31%), Infectious Diseases (30%), and Virology (26%), respectively. The keywords with the highest co-occurrence frequency were “monkeypox”, “smallpox,” and “smallpox virus.” The largest number of articles in the top 100 were published in Emerging Infectious Diseases (n=13), followed by Journal of Virology (n=11), Journal of Infectious Diseases (n=5), and PLoS One (n=5). The authors identified 711 different authors from 195 institutions and 28 countries in the top 100 most-cited articles, with the majority based in the USA. Conclusion: The top 100 most-cited studies provide an important insight into the historical developments of monkeypox. The authors should strengthen the recognition and management of monkeypox worldwide and strengthen research cooperation among scholars in order to better respond to the ongoing or future outbreak.

Introduction: Lung cancer remains the leading cause of cancer-related mortality. Although bronchoscopy allows direct visualization and tissue sampling, detecting subtle lesions is still challenging owing to limited resolution, variable imaging conditions, and the complex structure of the airway. Most existing approaches treat lesion segmentation and cancer diagnosis as separate tasks, which can reduce diagnostic coherence and limit clinical applicability. Method: We propose a novel Multi-Adapter-based Segment Any Bronchoscope Model (MASA), an end-to-end framework with an encoder that fuses spatial, frequency, and positional information and a dual decoder that performs simultaneous lesion segmentation and lung cancer diagnosis. MASA was trained/evaluated on the public BM-BronchoLC dataset. Results: On BM-BronchoLC, MASA improved lesion segmentation over the strongest baseline (ESFPNet), raising mean Dice coefficient (mDice) by +3.01% and mean Intersection-over-Union (mIoU) by +1.24%. For diagnosis, MASA increased Macro-F1 by +8.1 points and area under the precision–recall curve (AUPRC) by +14.1%. Conclusion: MASA provides a unified and interpretable pipeline for automated bronchoscopic image analysis, generating pixel-level lesion maps alongside case-level diagnostic predictions. The framework shows strong promise for improving early lung cancer detection and enhancing the efficiency of bronchoscopic workflows in clinical practice.

Background: Specialized thoracic-surgery questions require the integration of multi-factor clinical relationships within text, yet general-purpose large language models (LLMs) may underperform on such exam-style benchmarks. Methods: We constructed a DK-LLM agent by embedding curated medical textbook knowledge into a LangChain-based framework to support domain-specific reasoning. The model was tested on a 56-item thoracic-surgery examination question set in a restricted text-only setting without internet browsing or external tools and was compared with generic LLM baselines, three thoracic surgeons, and three non-expert engineers. Examination score and error patterns were assessed. Results: The knowledge-augmented DK-LLM configuration showed an 11.8-point examination-score advantage over the version without the local knowledge base. Commercial LLM-based agents outperformed the open-source baselines and non-expert participants on this question set, whereas experienced thoracic surgeons achieved the highest scores overall; in the ablation analysis, removing the local knowledge base reduced the examination score by 11.8 percentage points. Conclusion: Embedding domain-specific knowledge into LLMs may improve performance on specialized exam-style thoracic-surgery questions on this text-only benchmark. However, the present 56-item evaluation does not establish clinical equivalence, diagnostic accuracy in practice, multimodal competence, or readiness for real-world clinical decision support.