Clinical Research Directory

Multi-Theory Model-Based AI Agent Intervention for Smoking Cessation in Early-Stage Cancer Patients

The goal of this clinical trial is to evaluate the effectiveness of a Multi-Theory Model (MTM)-based AI agent intervention for smoking cessation in early-stage cancer patients (clinical stage cTNM 0\~II) who currently smoke. The main questions it aims to answer are: Does the AI agent intervention improve the biochemically verified 7-day point prevalence abstinence rate at the 6-month follow-up compared to control groups? Is the AI agent intervention feasible and acceptable for early-stage cancer patients? Researchers will compare the AI agent intervention group to an professional counseling group and a routine health education groupto see if the AI agent yields higher smoking cessation rates and better maintenance of abstinence. Participants will: Be randomly assigned to one of three groups to receive either AI agent support via WeChat, professional counseling via Phone, or routine health education. Interact with the AI agent (if in the intervention group) which provides personalized guidance, emotional support, and resource matching based on the Multi-Theory Model constructs (e.g., participatory dialogue, emotional transformation). Complete questionnaires regarding smoking behavior, nicotine dependence, self-efficacy, and quality of life at baseline and follow-ups (1 week, 1 month, 3 months, and 6 months). Provide exhaled carbon monoxide (CO) and saliva cotinine samples for biochemical verification if they report successful smoking cessation.

cRCT of AI Agent in Clinital Resoning Training

Traditional medical education has long emphasized one-way transmission of theoretical knowledge, which presents limitations in the systematic cultivation of clinical reasoning skills among medical students. Miller's pyramid of clinical competence emphasizes the gradual transformation from theoretical knowledge to clinical practice ability. Case-based learning (CBL), as a teaching method centered on real or simulated clinical cases, is a key strategy to address the above limitations. Artificial intelligence (AI)-assisted clinical reasoning training tools can overcome time and space constraints, and offer students repeatable, adaptive, and real-time feedback case training, thereby reinforcing the sustained role of CBL in clinical reasoning development. Currently, it still lacks high-quality evidence from randomized controlled trials on the impact of AI agents on medical students' clinical reasoning skills. This study plans to evaluate the impact of an AI clinical reasoning training agent on students' clinical reasoning training outcomes and CBL learning experience. Primary Objective: To evaluate the impact of the AI agent on student learning outcomes (course examination scores and clinical reasoning test scores). Secondary Objective: To investigate students' AI acceptance (perceived usefulness, perceived ease of use, satisfaction, and intention to use). This study adopts a two-arm parallel cluster randomized controlled trial design. The trial is designed and reported in accordance with the CONSORT statement. The study population will recruit Class of 2021 medical students (8-year program) from Peking Union Medical College and Class of 2020 medical students (8-year program) from Tsinghua University School of Medicine. Both cohorts are officially enrolled in the "Comprehensive Clinical Course" for the 2025-2026 academic year, have consistent foundational knowledge in basic medicine and diagnostics, and are in the phase of clinical medicine theory learning, not yet having entered clinical practice. Using PASS 2025 software, the sample size per arm for the cRCT is 39, with number of clusters per arm K=N/M =13, Considering a 10% attrition or exclusion rate, the target recruitment is 88 participants. Considering potential heterogeneity in baseline between students from the two schools, and possible contamination due to discussions among dormitory mates during the intervention, this study will adopt stratified cluster randomization, first stratifying by school, then using dormitory as the smallest randomization unit. Dormitories will be sorted by the random number, with the first half allocated to the intervention group and the second half to the control group. Participants' group assignment will be revealed via unique student ID only after baseline data collection and informed consent are completed. This study will select five topics from the "Comprehensive Clinical Course": "Infectious Diarrhea," "Viral Hepatitis," "Bloodstream Infection," "Infective Endocarditis," and "Central Nervous System Infection". Standardized cases will be provided by the teaching faculty, with two cases per topic, totaling 10 cases. These cases will be used to train AI agent. After class, the AI agent training tasks will be sent to the intervention group, and study materials will be distributed to the control group. Course examination scores and clinical reasoning test scores are the primary outcomes. AI technology acceptance including perceived usefulness, perceived ease of use, satisfaction, and intention to use are the secondary outcomes. This study has been approved by the Research Ethics Committee of Peking Union Medical College Hospital (Approval No.: I-26PJ0851).

A Multimodal AI Agent for Ophthalmic Clinical Decision Support

This study is a multicenter randomized controlled trial evaluating the effectiveness and safety of EyeAgent, a multimodal artificial intelligence (AI) agent designed to assist ophthalmologists in clinical decision-making. Participants will be recruited from ophthalmology clinics and hospitals in Hong Kong and mainland China. The AI agent acts as a digital co-pilot, analyzing patient images and clinical history to provide diagnostic and management recommendations. The trial aims to determine whether the use of the AI agent improves diagnostic accuracy, treatment decision-making performance, report generation, workflow efficiency, and user satisfaction compared to standard clinical practice.

AI-Agent for Automated Diagnosis and Predicting Using EHR and Multimodal Data

The goal of this clinical study is to evaluate the effectiveness of an AI agent in diagnosing and predicting diseases using electronic health records (EHR) and multimodal imaging data. The AI agent leverages advanced machine learning algorithms to process and analyze diverse health data sources, aiming to assist healthcare providers in making more accurate diagnoses and predictions.