With the advancement of the "New Medical Science" reform, the "Medicine+X" model has emerged as a key direction for the future development of medical education. Multidisciplinary integration places higher demands on both educators and students. Emerging technologies, such as intelligent tutoring systems, adaptive learning platforms, intelligent campus management systems, and ChatGPT, have made personalized learning possible. Such approaches offer notable advantages, including improving learning efficiency, enhancing motivation, eliminating the spatiotemporal constraints of clinical education, and alleviating teachers′ workloads. Nevertheless, the application of artificial intelligence in personalized medical education still faces multiple challenges, such as issues of data quality and reliability, the need for faculty development, shifts in educational paradigms, and ethical considerations. This study explored the current status of artificial intelligence in personalized medical education and offered recommendations to promote its development, including strengthening the integration of technology and education, enhancing the digital literacy of educators, establishing ethical guidelines, and fostering multi-stakeholder collaboration.

In traditional teaching, medical students have limited opportunities to interact with patients, which constrains the development of their clinical skills. Virtual standardized patients offer a potential solution to this limitation. This article analyzes the advantages of virtual standardized patients and their application in clinical teaching.

Medical students often struggle to understand and master the relevant knowledge and skills in teaching, especially in surgical teaching. Emerging 3D printing technology can help students to understand and master surgical techniques. The problem-based learning (PBL) teaching method helps students to develop their independent thinking and teamwork skills. The combination of these methods has already achieved significant success. Therefore, this article discusses the application and combining 3D printing technology with the PBL teaching method in medical teaching, particularly in urological surgery education, and provides new ideas and references for future, more diverse, and high-tech medical education.

Objective:To investigate the predictive value of a machine learning model combining contrast-enhanced ultrasound（CEUS）parameters，radiomics features of ultrasound images，and clinical data for pathological grading in bladder urothelial carcinoma（BUC）.Methods:A retrospective analysis was conducted on 174 BUC patients from Inner Mongolia Autonomous Region People 's Hospital and the First Affiliated Hospital of Baotou Medical College from December 2017 to March 2024. One hundred and thirteen BUC patients from the former hospital were randomly divided into training group and internal test group in a ratio of 7 to 3，while 61 BUC patients from the latter hospital served as an external test group. The patients were stratified into low-grade bladder urothelial carcinoma（LGBUC）and high-grade bladder urothelial carcinoma（HGBUC）groups based on pathology. Two-dimensional grayscale ultrasound images were subjected to super-resolution（SR）reconstruction，followed by extraction and screening of radiomics features in comparison with CEUS video sequences. Selected features were input into a support vector machine（SVM）to build the radiomics model. CEUS parameters，conventional ultrasound metrics and clinical data with statistical significance between LGBUC and HGBUC groups were input into SVM to construct the clinical model. The radiomics and clinical model outputs were fused via multivariate Logistic regression to form a combined model. Model performances were evaluated using ROC curves，calibration curves，and clinical decision curves. Results:Seven radiomics features from SR images were used to build the radiomics model，while CEUS parameters（peak intensity and time-to-peak half），age，tumor-wall interface and tumor-wall angle formed the clinical model. The combined model integrated these outputs. All 3 models exhibited respective strengths，the combined model showed superior robustness. The AUCs of the combined model in the training，internal test and external test groups were 0.92，0.84 and 0.82，respectively.Conclusions:The combined model combining CEUS parameters，ultrasound radiomics features，and clinical data accurately predicts BUC pathological grade，providing a potential tool for clinical diagnosis and treatment.

Objective:To investigate the completion rate of tumor evaluation at initial assessment and after neoadjuvant therapy for mid and low rectal cancer patients in the national multicenter real-world database.Methods:The prospective real-world study was conducted. The clinicopathological data of 1 074 patients who underwent surgical treatment for mid and low rectal cancer in 47 national medical institutions, including Peking Union Medical College Hospital et al, from May 12,2023 to May 11,2024 were collected. Observation indicators: (1) clinical characteristics of patients with mid and low rectal cancer; (2) initial colonoscopy and pathologic evaluation of tumors in patients with mid and low rectal cancer; (3) initial imaging evaluation of patients with mid and low rectal cancer; (4) imaging evaluation after neoadjuvant therapy for patients with mid and low rectal cancer. Measurement data with normal distribution were represented as Mean± SD, and measurement data with skewed distribution were represented as M( Q1, Q3). Count data were described as absoluter numbers and/or percentages. Results:(1) Clinical characteristics of patients with mid and low rectal cancer. Of the 1 074 patients, there were 713 males and 361 females, aged 63(56,70)years. The body mass index of 1 074 patients was 24(21,26)kg/m 2.For American Society of Anesthesiologists classification, there were 147 cases of stage Ⅰ, 641 cases of stage Ⅱ, 157 cases of stage Ⅲ, 2 cases of stage Ⅳ, and there were 127 cases missing data. (2) Initial colonoscopy and pathologic evaluation of tumors in patients with mid and low rectal cancer. Of the 1 074 patients, there were 787 cases (73.28%) undergoing complete colonoscopy, and there were only 197 cases (18.34%) undergoing immunohistochemical evaluation of all four mismatch repair proteins. (3) Initial imaging evaluation of patients with mid and low rectal cancer. Of the 1 074 patients, there were 842(78.40%) patients completing magnetic resonance imaging (MRI) or ultrasound evaluation, and there were 914(85.10%) patients completing chest, abdomen, and pelvis enhanced computed tomography (CT) evaluation. In the 149 patients completing rectal ultrasound evaluation, there were 122 cases (81.88%) comple-ting T staging evaluation, and there were 81 cases (54.36%) completing N staging evaluation. In the 808 patients completing rectal MRI evaluation, there were 708 cases (87.62%) completing T staging evaluation, and there were 590 cases (73.02%) completing N staging evaluation. (4) Imaging evalua-tion after neoadjuvant therapy for patients with mid and low rectal cancer. Of the 388 patients with neoadjuvant therapy, there were 332 patients (85.57%) completing MRI or ultrasound evaluation, and there were 327 patients (84.28%) completing chest, abdomen, and pelvis enhanced CT evalua-tion. In the 70 patients completing rectal ultrasound evaluation, there were 65 cases (92.86%) com-pleting T staging evaluation, and there were 49 cases (70.00%) completing N staging evaluation. In the 327 patients completing rectal MRI evaluation, there were 246 cases (75.23%) completing T staging, and there were 228 cases (69.72%) completing N staging evaluation. Conclusion:The com-pletion rate of tumor imaging evaluation at initial assessment and after neoadjuvant therapy for mid and low rectal cancer patients on a national scale is relatively good.

With the advancement of the "New Medical Science" reform, the "Medicine+X" model has emerged as a key direction for the future development of medical education. Multidisciplinary integration places higher demands on both educators and students. Emerging technologies, such as intelligent tutoring systems, adaptive learning platforms, intelligent campus management systems, and ChatGPT, have made personalized learning possible. Such approaches offer notable advantages, including improving learning efficiency, enhancing motivation, eliminating the spatiotemporal constraints of clinical education, and alleviating teachers′ workloads. Nevertheless, the application of artificial intelligence in personalized medical education still faces multiple challenges, such as issues of data quality and reliability, the need for faculty development, shifts in educational paradigms, and ethical considerations. This study explored the current status of artificial intelligence in personalized medical education and offered recommendations to promote its development, including strengthening the integration of technology and education, enhancing the digital literacy of educators, establishing ethical guidelines, and fostering multi-stakeholder collaboration.

In traditional teaching, medical students have limited opportunities to interact with patients, which constrains the development of their clinical skills. Virtual standardized patients offer a potential solution to this limitation. This article analyzes the advantages of virtual standardized patients and their application in clinical teaching.

Medical students often struggle to understand and master the relevant knowledge and skills in teaching, especially in surgical teaching. Emerging 3D printing technology can help students to understand and master surgical techniques. The problem-based learning (PBL) teaching method helps students to develop their independent thinking and teamwork skills. The combination of these methods has already achieved significant success. Therefore, this article discusses the application and combining 3D printing technology with the PBL teaching method in medical teaching, particularly in urological surgery education, and provides new ideas and references for future, more diverse, and high-tech medical education.

Objective To analyze the correlation between tongue image features and the risk levels of disease in patients with idiopathic membranous nephropathy(IMN).Methods Based on IMN clinical research electronic data acquisition system,a cross-sectional study method was used to analyze the clinical diagnosis and treatment data of 135 IMN patients from Guangdong Provincial Hospital of Chinese Medicine.The patients were grouped according to the risk levels of disease,and then the correlation between the risk levels of disease and tongue image features was analyzed.During the description of tongue image features,TB is for tongue body,TC is for tongue coating,L is for luminance,a is for red-green axis,G is for the value of green,B is for the value of blue,and AUT is for the value of autocorrelation.Results The comparison of tongue image feature indicators of patients with different risk levels of IMN showed that:(1)the higher the level of disease risk of IMN patients,the greater the values of TB-L,TB-G and TB-B(P<0.05 or P<0.01).The values of tongue image indicator TB-a and TC-a of the patients with different risk levels of IMN were shown in decreasing sequence:low-risk group>high-risk group>middle-risk group>extremely-high-risk group(P<0.05).(2)Linear regression analysis showed that TB-L,TB-G,and TB-B were significantly increased in the high-risk group compared with those in the middle-and low-risk groups(P<0.05 or P<0.01),whereas there were no significant differences between the middle-risk group and low-risk group(P>0.05).(3)The results of correlation analysis showed that there was a positive correlation among most of the tongue image feature indicators(including TB-L,TB-G,TB-B,TB-AUT,TC-L,TC-G,and TC-B,etc.)and the risk level of disease,while TB-a was negatively correlated with the risk level of disease,and the differences were all statistically significant(P<0.05 or P<0.01).(4)All patients were treated with Chinese medicine and/or Chinese patent medicine,and 46.7%of patients were given hormones and immunosuppressants,and there was no statistically significant difference in the the use of hormones and immunosuppressants among various groups(P=0.637).Conclusion There is a correlation between the tongue image features of IMN patients and the risk level of disease,and the results will provide an objective reference for the assessment of illness state and traditional Chinese medicine(TCM)syndrome differentiation of IMN patients.With reference to the changes in the tongue image features,the illness state can be precisely identified,which is more accurate than the inspection of four diagnostic methods of TCM.

Objective To explore the relationship between dynamic contrast-enhanced magnetic resonance imaging(DCE-MRI)and clinical pathological features of invasive breast cancer and lymphovascular invasion(LVI).Methods Imaging and clinical pathological data were retrospectively collected from 508 patients with invasive breast cancer who underwent breast DCE-MRI at the First Medical Center of Chinese PLA General Hospital from January 2019 to August 2021.Patients were divided into the LVI-positive(LVI+)group(n=79)and LVI-negative(LVI-)group(n=429)based on postoperative pathological results.Univariate and multivariate logistic regression analyses were used to identify risk factors for LVI.Results Compared with LVI-group,LVI+group had a higher proportion of patients aged<45 years(44.3%vs.27.0%,P=0.002),non-mass-like enhancement(NME)(31.7%vs.17.7%,P=0.004),Ki-67 expression rate(40.0%vs.30.0%,P<0.001),high Ki-67 expression(94.9%vs.78.1%,P=0.001),Luminal B subtype(76.0%vs.60.1%,P=0.008),and positive axillary lymph nodes rate(72.2%vs.31.5%,P<0.001),while the proportion of Luminal A subtype was lower(2.5%vs.21.5%,P<0.001).Univariate and multivariate logistic regression analyses showed that age≥45 years(OR=0.468,95%CI 0.280-0.783,P=0.004)was an independent protective factor for LVI,while NME(OR=1.987,95%CI 1.126-3.444,P=0.016)was an independent risk factor.Compared with Luminal A subtype,patients with Luminal B subtype(OR=10.482,95%CI 3.164-64.923,P=0.001),HER-2 overexpression subtype(OR=11.571,95%CI 2.755-79.341,P=0.003)and triple-negative subtypes(OR=8.433,95%CI 1.985-57.908,P=0.009)had a higher risk of LVI.Conclusions Age≥45 years is an independent protective factor for LVI,while NME is an independent risk factor.Among molecular subtypes,patients with Luminal B,HER-2 overexpression and triple-negative subtypes have a higher risk of LVI compared with the Luminal A subtype.