Rare diseases pose significant diagnostic and therapeutic challenges,carrying a high disease burden,their management critically reflects a nation's public health resilience.Currently,China faces key challenges such as scarce treatments,fragmented services,and low drug accessibility in rare disease care,which urgently require systemic solutions.Digital-intelligent technology as a key breakthrough are expected to resolve the challenges in this field.Although its application in the field of rare diseases is gradually expanding,there is a lack of systematic compilation of studies to elucidate how to precisely enhance the precision,synergy and sustainability of diagnosis and treatment.The key challenges in rare disease care concentrate in four areas:inefficiency in prenatal screening,uneven distribution of medical resources,low efficiency in social organization collaboration,and ineffective information dissemination.The"4C"strategy,based on digital-intelligent technology,can address these issues:①coordination,boost prenatal screening awareness and capacity via digital-intelligent platforms to strengthen prevention;②cooperation,deepen collaboration within specialist networks,empowering institutions to enhance diagnostic capacity;③co-creation,empower support organizations to optimize resources,efficiency;④cognition,minimize information dissipation through efficient platforms,improving patient and family quality of life.This establishes an integrated digital-intelligent rare disease model encompassing"screening-diagnosis-treatment-care".

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.

Rare diseases pose significant diagnostic and therapeutic challenges,carrying a high disease burden,their management critically reflects a nation's public health resilience.Currently,China faces key challenges such as scarce treatments,fragmented services,and low drug accessibility in rare disease care,which urgently require systemic solutions.Digital-intelligent technology as a key breakthrough are expected to resolve the challenges in this field.Although its application in the field of rare diseases is gradually expanding,there is a lack of systematic compilation of studies to elucidate how to precisely enhance the precision,synergy and sustainability of diagnosis and treatment.The key challenges in rare disease care concentrate in four areas:inefficiency in prenatal screening,uneven distribution of medical resources,low efficiency in social organization collaboration,and ineffective information dissemination.The"4C"strategy,based on digital-intelligent technology,can address these issues:①coordination,boost prenatal screening awareness and capacity via digital-intelligent platforms to strengthen prevention;②cooperation,deepen collaboration within specialist networks,empowering institutions to enhance diagnostic capacity;③co-creation,empower support organizations to optimize resources,efficiency;④cognition,minimize information dissipation through efficient platforms,improving patient and family quality of life.This establishes an integrated digital-intelligent rare disease model encompassing"screening-diagnosis-treatment-care".

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 evaluate the effectiveness and safety of sequential therapy with parathyroid hormone analogues and bisphosphonates for osteoporosis.METHODS PubMed,Embase,the Cochrane Library,Web of Science,China National Knowledge Infrastructure,VIP,Wanfang data,and SinoMed were searched in both English and Chinese databases from their inception to March 25,2024.Two researchers independently conducted literature searches,screening,and data extraction.Review Manager 5.4 software was used for the meta-analysis.Subgroup analyses and sensitivity analyses were performed based on different medication sequences in the treatment group to account for potential sources of heterogeneity.RESULTS A total of 7 randomized controlled trials involving 2 461 participants were included,with 1 215 in the treatment group and 1 246 in the control group.The meta-analysis results showed that the treatment group using sequential therapy with parathyroid hormone analogues and bisphosphonates had superior effects on improving bone mineral density at the lumbar spine[SMD=0.90,95％CI(0.44,1.35),P＜0.001],total hip[SMD=0.68,95％CI(0.14,1.21),P=0.01],and femoral neck[SMD=0.45,95％CI(0.04,0.86),P=0.03]compared to the control group.It also significantly outperformed the control group in reducing the incidence of fractures post-treatment[OR=0.72,95％CI(0.54,0.97),P=0.03].No significant difference was noted in the incidence of adverse reactions between the two groups[OR=1.21,95％CI(0.99,1.46),P=0.06].Subgroup analysis based on intervention measures in the treatment group showed that switching from bisphosphonates to parathyroid hormone analogues[SMD=0.56,95％CI(0.09,1.03),P=0.02]or switching from parathyroid hormone analogues to bisphosphonates[SMD=0.97,95％CI(0.49,1.46),P＜0.001]both significantly potentiated lumbar spine bone mineral density compared to the control group.Switching from bisphosphonates to parathyroid hormone analogues also significantly promoted total hip bone mineral density compared to the control group[SMD=0.66,95％CI(0.18,1.13),P=0.007].Sensitivity analysis indicated that the results of this study were robust.CONCLUSIONS Sequential therapy with parathyroid hormone analogues and bisphosphonates can be recommended as an effective treatment for patients with osteoporosis,with good safety profiles.The medication sequences should be individually adjusted based on the patient's particular situation and the different responses of various skeletal sites.