Large language models (LLMs) offer a modern approach to help inherit traditional Chinese medicine (TCM). This article discussed the progress of LLM applications in TCM and proposed future development directions by reviewing the existing research. We have found that LLMs and related technologies have excellent applications and performance in the management of TCM knowledge and data. They are often applied in information extraction, knowledge graph construction, and data standardization processing. However, data quality and security issues need to be given more attention. In clinical diagnosis and treatment, LLMs can imitate the thinking of TCM by disassembling and reconstructing its diagnostic process and can achieve functions such as prescription recommendation and question and answer (Q&A). However, this approach involves LLMs making inferences and predictions based on existing corpora and thus may not flexibly handle complex environments and tasks. Moreover, the current evaluation criteria for TCM LLMs can be summarized into 3 categories: general evaluation metrics, technical framework evaluation, and evaluation criteria for the characteristics of TCM (such as consistency rates of prescriptions and diagnostic suggestions). However, the lack of a unified and standardized evaluation system hinders the clinical application of TCM LLMs. The future progress of TCM LLMs should focus on the 3 aforementioned critical aspects to achieve technological breakthroughs. In addition, we are promoting the research on vertical TCM LLMs and application terminals. We believe this will bring new ideas to the research on TCM LLMs.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

[Objective] To compare the clinical efficacy of super pulse thulium laser enucleation of the prostate (SPThuLEP) with "open tunnel" and transurethral holmium laser enucleation of the prostate (HoLEP) in the treatment of benign prostatic hyperplasia (BPH), in order to provide reference for the treatment options of BPH. [Methods] The clinical data of 112 BPH patients treated in our hospital during Jan.2023 and Jul.2023 were retrospectively analyzed, including 65 treated with SPThuLEP with "open tunnel" and 57 with HoLEP.The operation time, postoperative hemoglobin decrease, postoperative bladder irrigation, catheter indwelling time, hospitalization time and complications were compared between the two groups.The changes of maximum urine flow rate (Qmax), international prostate symptom score (IPSS), quality of life score (QoL), postvoid residual (PVR) and prostate-specific antigen (PSA) were compared between the two groups before operation and one month after operation. [Results] All operations were successful without conversion to open or transurethral plasmakinetic resection.The postoperative decrease of hemoglobin in SPThuLEP group was lower than that in HoLEP group [(13.12±6.72) g/L vs. (21.02±6.51) g/L], with statistical difference (P<0.05). There were no significant differences in the operation time [(63.35±15.73) min vs.(61.02±17.55) min], postoperative bladder irrigation time [(1.07±0.45) d vs. (1.06±0.36) d], catheter indwelling time [(2.98±0.56) d vs. (3.01±0.63) d] and hospitalization time [(3.63±0.61) d vs.(3.79±0.76) d] between the two groups (P>0.05). No blood transfusion, secondary bleeding or unplanned hospitalization occurred, and there were no serious complications such as transurethral electroresection syndrome (TURS), urethral stricture and urinary incontinence.One month after operation, the Qmax, IPSS, QoL, PVR and PSA of the two groups were significantly improved compared with those before operation (P<0.05), but with no statistical difference between the two groups (P>0.05). [Conclusion] SPThuLEP with "open tunnel" has comparable efficacy as HoLEP in the treatment of BPH.With advantages of small amount of bleeding and high safety, this minimally invasive technique can be widely popularized in clinical practice.

With the widespread adoption of low-dose CT screening and the extensive application of high-resolution CT, the detection rate of sub-centimeter lung nodules has significantly increased. How to scientifically manage these nodules while avoiding overtreatment and diagnostic delays has become an important clinical issue. Among them, lung nodules with a consolidation tumor ratio less than 0.25, dominated by ground-glass shadows, are particularly worthy of attention. The therapeutic challenge for this group is how to achieve precise and complete resection of nodules during surgery while maximizing the preservation of the patient's lung function. The "watershed topography map" is a new technology based on big data and artificial intelligence algorithms. This method uses Dicom data from conventional dose CT scans, combined with microscopic (22-24 levels) capillary network anatomical watershed features, to generate high-precision simulated natural segmentation planes of lung sub-segments through specific textures and forms. This technology forms fluorescent watershed boundaries on the lung surface, which highly fit the actual lung anatomical structure. By analyzing the adjacent relationship between the nodule and the watershed boundary, real-time, visually accurate positioning of the nodule can be achieved. This innovative technology provides a new solution for the intraoperative positioning and resection of lung nodules. This consensus was led by four major domestic societies, jointly with expert teams in related fields, oriented to clinical practical needs, referring to domestic and foreign guidelines and consensus, and finally formed after multiple rounds of consultation, discussion, and voting. The main content covers the theoretical basis of the "watershed topography map" technology, indications, operation procedures, surgical planning details, and postoperative evaluation standards, aiming to provide scientific guidance and exploration directions for clinical peers who are currently or plan to carry out lung nodule resection using the fluorescent microscope watershed analysis method.

Osteoarthritis （OA） and stroke are common clinical diseases that reduce patients' quality of life and place a burden on families and society. Ruyi Zhenbaowan， a classic prescription in Tibetan medicine， have the functions of clearing heat， awakening the brain and opening orifices， relaxing tendons and promoting meridian circulation， and eliminating yellow water. Clinically， they are used to treat osteoarthritis， post-stroke sequelae， neuropathic pain， and other related conditions. Modern pharmacological studies have demonstrated their anti-inflammatory， analgesic， and nerve-repairing effects. However， current research remains insufficient regarding the appropriate indications， timing， and efficacy of this medicine in treating relevant diseases. To enhance clinicians' understanding of this medicine and promote its standardized and rational clinical use， a panel of national experts， including clinical specialists， Tibetan medicine practitioners， pharmacologists， and methodologists， formulated this consensus based on clinical experience and evidence-based practice. The Cochrane systematic review framework， the Grading of Recommendations Assessment， Development and Evaluation （GRADE） system， and the nominal group method were employed to generate seven graded recommendations and 19 consensus-based suggestions. These recommendations clearly define the key points in the clinical application of Ruyi Zhenbaowan， including therapeutic indications， dosage and administration， treatment duration， and medication safety. The consensus specifically addresses the clinical efficacy， appropriate timing of administration， dosage strategies， treatment cycles， and combination medication strategies for treating osteoarthritis and stroke and provides an overview of safety considerations. The aim is to provide standardized guidance for hospitals and healthcare institutions nationwide to ensure the rational application of Ruyi Zhenbaowan in the treatment of osteoarthritis and stroke， reduce medication-related risks， and further leverage its clinical advantages. This consensus has been approved and issued by the China Association of Chinese Medicine， with the standard number GS/CACM 369-2024.

This narrative review examines the challenges, strategies, and future directions in the development of young teachers within the pathophysiology departments of Chinese medical colleges. A thorough review of 49 studies published between 2013 and 2024 was carried out using PubMed, Web of Science, and various Chinese databases. The primary challenges identified include teaching innovation (cited in 84.2% of the studies), research pressure (91.2%), disciplinary characteristics (87.7%), and career development (80.7%). Medical schools have responded by enhancing training systems (94.7%), innovating teaching methods (93.0%), and bolstering research support (96.5%). Looking ahead, trends are shifting toward the application of new technologies, interdisciplinary integration, and international collaboration. The focus on cultivating young teachers is increasingly geared towards personalization and diversification, which are essential for advancing education in pathophysiology. High-quality young teachers are pivotal in raising teaching standards, fostering research innovation, and facilitating interdisciplinary exchanges. Based on these insights, we recommend several practical measures to enhance the quality of pathophysiology education in China. These include establishing comprehensive training programs that integrate teaching innovation and research skills; developing structured mentorship systems with clear pathways for career advancement; creating platforms that support technology-enhanced teaching and international collaboration; and implementing systematic evaluation mechanisms to assess teaching effectiveness. These targeted interventions will require a coordinated effort from department heads, educational institutions, and policymakers to ensure a sustained improvement in the quality of pathophysiology education.

This narrative review examines the challenges, strategies, and future directions in the development of young teachers within the pathophysiology departments of Chinese medical colleges. A thorough review of 49 studies published between 2013 and 2024 was carried out using PubMed, Web of Science, and various Chinese databases. The primary challenges identified include teaching innovation (cited in 84.2% of the studies), research pressure (91.2%), disciplinary characteristics (87.7%), and career development (80.7%). Medical schools have responded by enhancing training systems (94.7%), innovating teaching methods (93.0%), and bolstering research support (96.5%). Looking ahead, trends are shifting toward the application of new technologies, interdisciplinary integration, and international collaboration. The focus on cultivating young teachers is increasingly geared towards personalization and diversification, which are essential for advancing education in pathophysiology. High-quality young teachers are pivotal in raising teaching standards, fostering research innovation, and facilitating interdisciplinary exchanges. Based on these insights, we recommend several practical measures to enhance the quality of pathophysiology education in China. These include establishing comprehensive training programs that integrate teaching innovation and research skills; developing structured mentorship systems with clear pathways for career advancement; creating platforms that support technology-enhanced teaching and international collaboration; and implementing systematic evaluation mechanisms to assess teaching effectiveness. These targeted interventions will require a coordinated effort from department heads, educational institutions, and policymakers to ensure a sustained improvement in the quality of pathophysiology education.

This narrative review examines the challenges, strategies, and future directions in the development of young teachers within the pathophysiology departments of Chinese medical colleges. A thorough review of 49 studies published between 2013 and 2024 was carried out using PubMed, Web of Science, and various Chinese databases. The primary challenges identified include teaching innovation (cited in 84.2% of the studies), research pressure (91.2%), disciplinary characteristics (87.7%), and career development (80.7%). Medical schools have responded by enhancing training systems (94.7%), innovating teaching methods (93.0%), and bolstering research support (96.5%). Looking ahead, trends are shifting toward the application of new technologies, interdisciplinary integration, and international collaboration. The focus on cultivating young teachers is increasingly geared towards personalization and diversification, which are essential for advancing education in pathophysiology. High-quality young teachers are pivotal in raising teaching standards, fostering research innovation, and facilitating interdisciplinary exchanges. Based on these insights, we recommend several practical measures to enhance the quality of pathophysiology education in China. These include establishing comprehensive training programs that integrate teaching innovation and research skills; developing structured mentorship systems with clear pathways for career advancement; creating platforms that support technology-enhanced teaching and international collaboration; and implementing systematic evaluation mechanisms to assess teaching effectiveness. These targeted interventions will require a coordinated effort from department heads, educational institutions, and policymakers to ensure a sustained improvement in the quality of pathophysiology education.

This narrative review examines the challenges, strategies, and future directions in the development of young teachers within the pathophysiology departments of Chinese medical colleges. A thorough review of 49 studies published between 2013 and 2024 was carried out using PubMed, Web of Science, and various Chinese databases. The primary challenges identified include teaching innovation (cited in 84.2% of the studies), research pressure (91.2%), disciplinary characteristics (87.7%), and career development (80.7%). Medical schools have responded by enhancing training systems (94.7%), innovating teaching methods (93.0%), and bolstering research support (96.5%). Looking ahead, trends are shifting toward the application of new technologies, interdisciplinary integration, and international collaboration. The focus on cultivating young teachers is increasingly geared towards personalization and diversification, which are essential for advancing education in pathophysiology. High-quality young teachers are pivotal in raising teaching standards, fostering research innovation, and facilitating interdisciplinary exchanges. Based on these insights, we recommend several practical measures to enhance the quality of pathophysiology education in China. These include establishing comprehensive training programs that integrate teaching innovation and research skills; developing structured mentorship systems with clear pathways for career advancement; creating platforms that support technology-enhanced teaching and international collaboration; and implementing systematic evaluation mechanisms to assess teaching effectiveness. These targeted interventions will require a coordinated effort from department heads, educational institutions, and policymakers to ensure a sustained improvement in the quality of pathophysiology education.