Clinical trials

The goal of this clinical trial is to learn how the use of a large language model (LLM) based tool affects outpatient clinical care in adult patients attending general hospital outpatient clinics. The main questions it aims to answer are: Does the use of an LLM-based tool affect the efficiency of outpatient visits? Does the use of an LLM-based tool affect the experience of doctors and patients during outpatient care? Researchers will compare outpatient visits supported by an LLM-based tool to standard outpatient visits without such a tool, to see whether and how the tool influences the care process and the experiences of doctors and patients. Participants will: Take part in outpatient visits that may or may not involve an LLM-based tool, depending on their assigned group Complete a short questionnaire about their visit experience after the consultation

This is a follow-up study of a previously completed randomized controlled trial (NCT06827873) that investigated the effects of oral supplements on influenza vaccine response in adults aged 60-70 years. The original study was completed in April 2025, with participants receiving either TUDCA (Tauro Ursodesoxy Cholic Acid) supplementation, fatty acid supplementation, or placebo during influenza vaccination. The primary objectives of this follow-up study are to: 1. Evaluate the durability of vaccine-induced antibody responses approximately 8 months post-vaccination 2. Assess the persistence of immune memory cells, particularly long-lived plasma cells and memory B cells 3. Compare long-term immune responses between the TUDCA supplementation group and placebo group This observational follow-up involves a single visit where participants will: 1. Provide one blood sample for antibody and immune cell analysis 2. No intervention or vaccination will be administered The study will specifically focus on B cell subsets through flow cytometry analysis, including total B cells, memory B cells, plasma cells, and long-lived plasma cells. This research aims to determine whether TUDCA supplementation can enhance the durability of vaccine-induced immunity in older adults.

Maternal and neonatal health remains one of the most pressing global health challenges, particularly in low- and middle-income countries (LMICs). Ethiopia continues to face a high burden, with maternal mortality estimated at 195 per 100,000 live births, neonatal mortality at 27 per 1,000 live births, and perinatal mortality rates ranging from 37‰ to 124‰ depending on the level of care. These outcomes remain substantially higher than the targets set under the United Nations Sustainable Development Goals (SDGs) for 2030. The World Health Organization (WHO) recommends that all pregnant women receive at least one ultrasound scan before 24 weeks of gestation, yet nearly two-thirds of women worldwide-especially in LMICs-lack access to this service. Barriers include high costs of ultrasound machines, limited technical expertise, and shortages of skilled sonographers in rural primary care. Artificial Intelligence-driven Point-of-Care Ultrasound (AI-POCUS) represents a promising innovation to expand prenatal imaging in resource-constrained settings by equipping frontline health workers with AI-supported diagnostic capabilities. This study, conducted under the Tsinghua University BRIGHT (Bringing Research to Impact for Global Health at Tsinghua) program, will evaluate the clinical effectiveness, feasibility, cost, and scalability of AI-POCUS in rural Ethiopia. A three-arm cluster randomized controlled trial will compare two AI-enabled ultrasound technologies-BabyChecker (Netherlands) and a China-developed AI-POCUS device-against standard antenatal care without ultrasound. Findings will generate robust clinical and policy-relevant evidence to guide the sustainable implementation of AI-enabled maternal health interventions in sub-Saharan Africa.

Easy-EyeFM is a code-free artificial intelligence platform designed for ophthalmologists to provide diagnostic and treatment recommendations and help doctors develop customized diagnostic models. This project aims to evaluate the usability and user-friendliness of the Easy-EyeFM platform for physicians. The study will gather feedback from medical professionals to evaluate the intuitiveness and effectiveness of the platform in supporting model customization and medical image analysis tasks. Subjects will participate in the trial of the Easy-EyeFM platform and the comparison with the existing commercial platform (AutoML) at the appointed time. The researchers will inform subjects of relevant precautions and assist them to read the user manual before the test. Subjects will complete model customization and picture diagnosis within the prescribed test time, and according to the test results, Score the human-computer interaction experience of the AI platform, fill in the questionnaire, and finally complete the evaluation content.

This study aims to explore the diagnostic and therapeutic performance of a multimodal foundation model-based clinical intelligent management system in simulated outpatient scenarios. Specifically, it will compare the system's AI agent with general practitioners from different linguistic and cultural backgrounds worldwide, as well as various dialect and minority language regions in China. The study will be conducted across multiple centers in diverse regions to comprehensively evaluate the system's adaptability. Using a prospective, single-blind design, each center will recruit primary care physicians. Standardized simulated patients across all centers will be trained based on the same set of standardized cases and then will consult with four real physicians and the AI agent, ensuring consistency. By comparing diagnostic and treatment quality, patient satisfaction, and clinical outcomes, this study aims to explore the system's effectiveness in enhancing primary care and patient management across different regions and linguistic environments.

This study aims to evaluate the effectiveness of a clinical intelligent management system, based on a multimodal foundational model, in improving the quality of primary care at a community health service center. Using a prospective, randomized controlled, single-blind study design, the trial will include primary care physicians and adult patients seeking care at the Tiantongyuanbei Community Health Service Center and Shanghai Health and Medical Center. Physicians in the intervention group will utilize the intelligent management system with AR glasses to aid in diagnosis and treatment, while patients will engage with the system's patient interface to access health education, follow-up reminders, and AI-powered home consultation features. In contrast, physicians in the control group will provide routine diagnoses and treatments, with their patients receiving standard health education and follow-up care. The trial was designed with the assistance of a digital twin-based clinical research system (termed X Town). By comprehensively evaluating diagnostic quality, physicians' clinical abilities, patient-reported outcomes, and system appraisals, this study will assess the effectiveness of the clinical intelligent management system in enhancing the quality of primary care and patient management.

This study examines how artificial intelligence systems interact with medical professionals. Researchers will compare physician responses to different AI-generated outputs in simulative medical scenarios.

This study will evaluate the effects of the AI system developed by our team on biomedical research.

The goal of this cluster randomized controlled trial is to study the effectiveness of the Nutrition Package (Yingyangbao) on improving malnutrition in children and adolescents aged 7-15 in Myanmar. The main questions it aims to answer are: * What is the effectiveness of Nutrition Package intervention on the growth, development, and nutritional health of children and adolescents aged 7-15 in Myanmar? * How safe and feasible is the Nutrition package? Researchers will compare the Nutrition Package plus probiotics, the Nutrition Package alone, and no intervention to see if the Nutrition Package improves malnutrition and other health outcomes of children and adolescents aged 7-15 in Myanmar. Participants will receive Nutrition Package products for six months and have their height, weight, mid-upper arm circumference, and hemoglobin levels measured and compared at baseline and end.

Dear Squash Enthusiast, Hello! First of all, thank you for participating in this survey! This questionnaire is part of a joint study conducted by the Sports and Brain Science Research Team at Tsinghua University and the Squash program students from Nanjing Sport Institute. The study focuses on promoting squash, understanding the exercise experiences of squash participants, and examining the effects of squash on sleep and mental health. Completing the questionnaire will take approximately 5 minutes. This research strictly follows the guidelines of the Tsinghua University Ethics Review Committee. All tests are harmless to the body, and there are no right or wrong answers to the questions in the survey. Please respond according to your personal situation. Your personal information will only be used for statistical purposes, and we will ensure that your information is kept strictly confidential.

The aim of this clinical trial is to explore the efficacy of fatty acid and bile acid based supplements on enhancing influenza vaccine immune response in adults aged 60-70 years. The objectives of this study are: 1. To explore the efficacy of fatty acid and bile acid based supplements on enhancing flu vaccine immune response. 2. To evaluate the safety of fatty acid and bile acid use in elders. 3. To explore the potential role of microbiota in regulating immune response. This study will conduct a randomized clinical trial to compare the efficacy of fatty acid / bile acid (Tauro Ursodesoxy Cholic Acid, TUDCA)supplements on enhancing vaccine immune response. The antibody's titer and safety indicators after influenza vaccination will be evaluated. Study process are: 1. Participants will be required to intake the assigned supplement or placebo daily for 25 days; 2. Receive a influenza vaccine on day 4; 3. Provide blood samples three times and stool samples twice at base line and endpoint respectively; 4. The antibody's titer and safety indicators will be analyzed and compared among groups. This study aims to establish a theoretical foundation for utilizing nutritional strategies to enhance vaccine-induced immune responses and to provide a scientific framework for developing oral vaccine boosters.

With the rise of generative artificial intelligence and large language models, medical education is undergoing a significant transformation. Numerous studies have highlighted the limitations of traditional educational knowledge acquisition and the potential impact of artificial intelligence on medical education, resident training, and continuing education for clinical practitioners. However, there is a lack of real-world experiments on the effectiveness of AI-integrated education. Artificial intelligence can provide extensive educational resources and tools that are not limited by geographical location or language, thereby lowering the barrier to accessing high-quality medical education and promoting educational equity. Nevertheless, the performance of AI models trained by different medical institutions or healthcare systems may vary. To offer a more universal, accessible, high-quality, and interconnected educational journey. We have developed a virtual ophthalmology teacher, which developed based on foundational model and large language models. This model, named EyeTeacher provide comprehensive theoretical knowledge and clinical skills enhancement for untrained medical students. To verify the effectiveness of our EyeTeacher across different national ophthalmology education systems and languages, we plan to implement a randomized controlled trial. This trial will assess the clinical capabilities of all participants and explore the advantages and disadvantages of the system compared to traditional teaching methods.

To determine whether an integrated retinal AI decision support can improve predictive accuracy of coronary heart disease (CHD), the investigators are conducting a randomized controlled study of AI guided prediction of CHD compared to clinical prediction by physicians (e.g., usingPCEs), both using clinical intuition as baseline.