This comprehensive review systematically explores the multifaceted applications, inherent challenges, and promising future directions of artificial intelligence (AI) within the medical domain. It meticulously examines AI's specific contributions to basic medical research, disease prevention, intelligent diagnosis, treatment, rehabilitation, nursing, and health management. Furthermore, the review delves into AI's innovative practices and pivotal roles in clinical trials, hospital administration, medical education, as well as the realms of medical ethics and policy formulation. Notably, the review identifies several key challenges confronting AI in healthcare, encompassing issues such as inadequate algorithm transparency, data privacy concerns, absent regulatory standards, and incomplete risk assessment frameworks. Looking ahead, the future trajectory of AI in healthcare encompasses enhancing algorithm interpretability, propelling generative AI applications, establishing robust data-sharing mechanisms, refining regulatory policies and standards, nurturing interdisciplinary talent, fostering collaboration among industry, academia, and medical institutions, and advancing inclusive, personalized precision medicine. Emphasizing the synergy between AI and emerging technologies like 5G, big data, and cloud computing, this review anticipates a new era of intelligent collaboration and inclusive sharing in healthcare. Through a multidimensional analysis, it presents a holistic overview of AI's medical applications and development prospects, catering to researchers, practitioners, and policymakers in the healthcare sector. Ultimately, this review aims to catalyze the deep integration and innovative deployment of AI technology in healthcare, thereby driving the sustainable advancement of smart healthcare.

As the volume of medical research using large language models (LLM) surges, the need for standardized and transparent reporting standards becomes increasingly critical. In January 2025, Nature Medicine published statement titled by TRIPOD-LLM reporting guideline for studies using large language models. This represents the first comprehensive reporting framework specifically tailored for studies that develop prediction models based on LLM. It comprises a checklist with 19 main items (encompassing 50 sub-items), a flowchart, and an abstract checklist (containing 12 items). This article provides an interpretation of TRIPOD-LLM’s development methods, primary content, scope, and the specific details of its items. The goal is to help researchers, clinicians, editors, and healthcare decision-makers to deeply understand and correctly apply TRIPOD-LLM, thereby improving the quality and transparency of LLM medical research reporting and promoting the standardized and ethical integration of LLM into healthcare.

The clinical practice guidelines of traditional Chinese medicine （TCM） have problems such as limited clinical application and unclear implementation effects， which may be related to the lack of clinical practice evidence. To provide reliable and precise evidence for clinical practice， this article proposes a model of combining TCM guidelines with real-world study， which includes 4 steps. Firstly， during the implementation process of the guidelines， a high-quality research database is established. Secondly， the recommendations in the guidelines are evaluated based on the established database in multiple dimensions， including applicability， effectiveness， safety， and cost-effectiveness， and thus their effectiveness in practical applications can be determined. Thirdly， based on the established database， core prescriptions are identified， and the targeted populations and medication plans are determined. That is， the best treatment regimen is established based on the analysis of abundant clinical data regarding the effects of different medication frequencies， dosages， and duration on efficacy. Fourthly， the guidelines are updated according to the real-world evidence. The research based on this model can provide real-world evidence for ancient and empirical prescriptions， improving their application in clinical practice. Moreover， this model can reduce research costs and improve research efficiency. When applying this model， researchers need to pay attention to the quality of real-world evidence， ensuring that it can truly reflect the situation in clinical practice. In addition， importance should be attached to the clinical application of guideline recommendations， ensuring that doctors can conduct standardized diagnosis and treatment according to the guidelines. Finally， full-process participation of multidisciplinary experts is encouraged to ensure the comprehensiveness and scientificity of the study. In conclusion， the application of this model will contribute to the development of TCM guidelines responsive to the needs of clinical practice and achieve the goal of promoting the homogenization of TCM clinical diagnosis and treatment.

As a supplement to randomized controlled trials， observational studies can provide evidence for the effectiveness of traditional Chinese medicine （TCM） treatment measures. They can also study influencing factors of diseases， etiology， and prognosis. However， there is a confounding effect due to the lack of randomization， which seriously affects the causal inference between the study factors and the outcome， resulting in confounding bias. Therefore， identifying and controlling confounding factors are key issues to be addressed in TCM observational studies. According to the causal network and the characteristics of TCM theory， confounding factors can be categorized into measured and unmeasured confounding factors. In addition， attention must be paid to identifying confounding factors and intermediate variables， as well as the interaction between confounding factors and study factors. For methods of controlling confounding factors， measured confounding factors can be controlled by stratification， multifactor analysis， propensity scores， and disease risk scores. Unmeasured and unknown confounding factors can be corrected using instrumental variable methods， difference-in-difference methods， and correction for underlying event rate ratios. Correcting and controlling confounding factors can ensure a balance between groups， and confounding bias can be reduced. In addition， methods such as sensitivity analysis and determination of interactions make the control of confounding factors more comprehensive. Due to the unique characteristics of TCM， observational studies of TCM face unique challenges in identifying and controlling confounding factors， including the ever-changing TCM treatment measures received by patients， the often-overlooked confounding effects in the four diagnostic information of TCM， and the lack of objective criteria for TCM evidence-based diagnosis. Some scholars have already conducted innovative explorations to address these issues， providing a methodological basis for conducting higher-quality TCM observational studies， so as to obtain more rigorous real-world evidence of TCM and gradually develop quality evaluation criteria for OS that are consistent with the characteristics of TCM.

The classification of traditional Chinese medicine （TCM） syndromes is one of the core technical elements in the industry standard of Specification of Diagnosis and Therapeutic Effect Evaluation of Diseases and Syndromes in TCM. In the past，when clinical standards for TCM were formulated，the determination of TCM syndrome classification relied heavily on textbooks and expert experience，lacking systematic research. This approach thus failed to reflect the advancement and scientificity of the standards，thereby affecting their implementation and application. This article reviewed the presentation forms and technical methods of TCM syndrome classification，including the two-tier syndrome classification model with primary and secondary symptoms，as well as the application of modern literature research，ancient literature research，Delphi method，in-depth expert interviews，consensus conferences，and real-world research. When syndrome classification standards are developed，it is necessary to build upon modern literature research，adopt a mixed approach combining qualitative research and quantitative analysis results，and reach expert consensus through consensus conferences. Through systematic research，the scientificity，applicability，and coordination of TCM syndrome classification standards can be enhanced，providing guidance for the standardization of TCM.

Objective To understand the epidemiological characteristics of Chlamydia trachomatis infection in Hubei province, and to provide scientific basis for prevention and control. Methods The data of Chlamydia trachomatis infection cases reported through the China Information System for Disease Control and Prevention from 2008 to 2022 were collected for epidemiological statistical analysis. Results The incidence of Chlamydia trachomatis infection in Hubei Province showed an increasing trend from 2008 to 2022, with an average annual reported incidence of 2.26/100 000. The top three reported incidence areas were Shiyan (6.04/100 000), Yichang (5.62/100 000) and Shennongjia (3.47/ 100 000). The reported incidence in southeast area was significantly higher than that in other areas (χ²=2869.603 , P < 0.001). The high incidence age group was 20-39 years old, accounting for more than 70%. The reported incidence in females was higher than that in males (χ²=1429.27 , P < 0.001). Housework and unemployment were the most common professions (43.54%). The case reporting institutions were mainly comprehensive hospitals (87.29%). Conclusion To effectively control the infection and transmission of Chlamydia trachomatis, it is necessary to strengthen the health popularization of STD knowledge, intervention and early active screening of high-risk groups.

Postoperative pain seriously affects the recovery process of patients, resulting in prolonged hospital stay and increased care costs. Appropriate application of patient-controlled analgesia devices can effectively relieve perioperative acute pain. In 1994 patient-controlled analgesia began to be used in Peking Union Medical College Hospital, and the Acute Pain Service Working Group was established in 2004. With the cooperation of anesthesiologists and specialist nurses, the group jointly has implemented the whole process and standardized management based on patient-controlled analgesia, and constantly improved and innovated working methods, laying a solid foundation for the development of postoperative pain management. This paper systematically reviews and summarizes the work from the aspects of clinical focus, nursing management experience, promotion and dissemination of pain treatment concepts, and development of acute pain service model under the new situation, with the hope of providing valuable reference for comprehensively strengthening pain management in the process of diagnosis and treatment, and enhancing patients' satisfaction with perioperative analgesia services.

Objective To observe the value of gastric filling ultrasound(GFUS)for diagnosing dysphagia after surgical operation of hiatal hernia(HH).Methods Totally 71 HH patients who underwent laparoscopic HH repair and fundoplication surgery were retrospectively enrolled and divided into dysphagia group(n=23)and non dysphagia group(n=48)according to postoperative Saeed scores.GFUS parameters,including inner diameter of esophageal hiatus lumen(IDE),wall thickness of abdominal esophageal(WTE)and inner diameter of the esophageal lumen at the fold(FIDE)were compared between groups,and their value of diagnosing postoperative dysphagia were analyzed.Results In dysphagia group,IDE and FIDE were lower,while WTE was higher than those in non dysphagia group(all P＜0.05).Low IDE and high WTE were both independent risk factors of postoperative dysphagia in HH patients.The sensitivity,specificity and area under the curve(AUC)of IDE for diagnosing dysphagia after surgical operation of HH was 82.64％,66.69％and 0.773,of WTE was 82.59％,68.73％and 0.793,of their combination was 88.89％,77.59％and 0.843,respectively.The AUC of the combination of IDE and WTE was higher than that of IDE and WTE alone(Z=1.328,1.364,P=0.044,0.043).Conclusion Combination of GFUS parameters IDE and WTE was valuable for diagnosing dysphagia after surgical operation of HH.

Objective To form the expert consensus on the limb management of patients with transvenous temporary cardiac pacing,standardize the limb management of patients with transvenous temporary cardiac pacing,and reduce complications related to the limb.Methods Using evidence-based methods,the evidence in this field was searched,evaluated and summarized,and relevant recommendations and research conclusions were extracted and classified by the level of evidence quality,and then the first draft of the consensus was formed.From December 2023 to January 2024,through 2 rounds of expert consultation and 4 rounds of expert meetings,the content was adjusted and the consensus was reached.Results Totally 16 experts participated in the consultation.The positive coefficient is 100%;the authoritative coefficient is 0.847 and 0.836;the average value of each index is more than＞3.8;the coefficient of variation is less than 0.21.The Kendall's harmony coefficient of the 2 rounds of expert consultation is 0.372 and 0.314,respectively,which were statistically significant.The consensus covers the preoperative,intraoperative and postoperative on limb management of patients with transvenous temporary cardiac pacing.Totally 11 themes were involved,including the preoperative preparation,position and catheter fixation in operation,position and catheter fixation in postoperative,activity,turn and transfer,duty shift on limb,nursing care after withdrawal of the catheter,prevention of deep vein thrombosis of the operative limb and prevent infection.Conclusion The consensus is highly scientific,and it is helpful to standardize the limb management of patients with transvenous temporary cardiac pacing.

This study aimed to explore key quality control factors that affected the prognosis of intensive care unit (ICU) patients in Chinese mainland over six years (2015-2020). The data for this study were from 31 provincial and municipal hospitals (3425 hospital ICUs) and included 2 110 685 ICU patients, for a total of 27 607 376 ICU hospitalization days. We found that 15 initially established quality control indicators were good predictors of patient prognosis, including percentage of ICU patients out of all inpatients (%), percentage of ICU bed occupancy of total inpatient bed occupancy (%), percentage of all ICU inpatients with an APACHE II score ⩾15 (%), three-hour (surviving sepsis campaign) SSC bundle compliance (%), six-hour SSC bundle compliance (%), rate of microbe detection before antibiotics (%), percentage of drug deep venous thrombosis (DVT) prophylaxis (%), percentage of unplanned endotracheal extubations (%), percentage of patients reintubated within 48 hours (%), unplanned transfers to the ICU (%), 48-h ICU readmission rate (%), ventilator associated pneumonia (VAP) (per 1000 ventilator days), catheter related blood stream infection (CRBSI) (per 1000 catheter days), catheter-associated urinary tract infections (CAUTI) (per 1000 catheter days), in-hospital mortality (%). When exploratory factor analysis was applied, the 15 indicators were divided into 6 core elements that varied in weight regarding quality evaluation: nosocomial infection management (21.35%), compliance with the Surviving Sepsis Campaign guidelines (17.97%), ICU resources (17.46%), airway management (15.53%), prevention of deep-vein thrombosis (14.07%), and severity of patient condition (13.61%). Based on the different weights of the core elements associated with the 15 indicators, we developed an integrated quality scoring system defined as F score=21.35%xnosocomial infection management + 17.97%xcompliance with SSC guidelines + 17.46%×ICU resources + 15.53%×airway management + 14.07%×DVT prevention + 13.61%×severity of patient condition. This evidence-based quality scoring system will help in assessing the key elements of quality management and establish a foundation for further optimization of the quality control indicator system.
Humans ; China/epidemiology* ; Cross Infection/epidemiology* ; Intensive Care Units/statistics & numerical data* ; Quality Control ; Quality Indicators, Health Care/statistics & numerical data* ; Sepsis/therapy* ; East Asian People/statistics & numerical data*