Clinical Research Directory

AI-Generated Video Distraction Via Virtual Reality for Fear and Pain During Pediatric Circumcision

This randomized controlled trial evaluated the effect of artificial intelligence-generated video distraction delivered via virtual reality on procedural fear and pain in children undergoing circumcision. A total of 80 children aged 4-12 years were assigned to an intervention group (n=40) or a control group (n=40). Children in the intervention group received AI-generated video distraction via virtual reality, whereas children in the control group received routine care. Pain and emotional responses were assessed from the perspectives of the child, parent, and nurse. Physiological parameters were also measured before and after the procedure. The study aimed to determine whether AI-generated video distraction delivered via virtual reality reduces procedural fear and pain during pediatric circumcision.

Large Linguistic Model for Clinical Reaoning of Physical Therapy Students

Clinical reasoning is a fundamental skill for physical therapy students, enabling them to collect and interpret patient information to make accurate diagnoses and treatment decisions. Traditional training methods often limit students' exposure to a diverse range of clinical cases, which can restrict the development of these skills. The integration of Large Language Models (LLMs), such as ChatGPT, into physical therapy education offers a novel approach to enhance clinical reasoning by simulating interactive and realistic patient scenarios. This randomized controlled trial aims to evaluate the effectiveness of an LLM-based educational intervention in improving clinical reasoning skills in physical therapy students. The study will recruit a total of 200 third-year physiotherapy students from multiple university institutions. Participants will be randomly assigned to one of two groups: 1. Experimental Group - Students will receive LLM-based training, engaging with a conversational artificial intelligence model to solve clinical cases over an 4-week period. The model will provide real-time responses to their questions, allowing them to refine their diagnostic and treatment reasoning. 2. Control Group - Students will follow the standard curriculum, participating in conventional case-based learning and supervised clinical reasoning exercises without AI-based assistance. The primary outcome of the study is the improvement in clinical reasoning skills, assessed through standardized written case evaluations and structured practical examinations. Secondary outcomes include changes in digital competence, student engagement levels, overall satisfaction with the educational approach, and cost-effectiveness of the intervention. By assessing the impact of LLMs on clinical reasoning training, this study seeks to determine whether AI-driven educational tools can effectively complement traditional physiotherapy education and improve student preparedness for real-world clinical practice.

Development of a Multimodal AI System for GIST Management

Background: Gastrointestinal Stromal Tumors (GISTs) are the most common mesenchymal tumors of the gastrointestinal tract. Accurate pre-operative diagnosis, risk stratification, and genotyping are critical for determining the appropriate surgical approach and targeted therapy (such as Imatinib). However, current methods often rely on invasive postoperative pathology and expensive genetic testing. Study Objective: The purpose of this study is to develop and validate a multimodal Artificial Intelligence (AI) model that integrates clinical data, CT radiomics (imaging features), and pathomics (digital pathology features) to improve the precision of GIST management. Study Design: This is a prospective, observational study. The researchers will recruit patients with suspected gastric submucosal tumors who are scheduled for surgery or biopsy at The Fourth Hospital of Hebei Medical University. Core Tasks: The AI model will be trained to perform three specific tasks: Diagnosis: Distinguish GISTs from other non-GIST mesenchymal tumors (e.g., leiomyomas, schwannomas). Risk Assessment: Stratify GISTs into risk categories (e.g., Low vs. High risk) to predict malignant potential. Genotyping: Predict specific gene mutations (e.g., KIT or PDGFRA mutations) to guide immunotherapy or targeted therapy. Methodology: Patient data (CT scans, pathology slides, and clinical history) will be collected and analyzed by the AI system. The AI's predictions will be compared against the "Gold Standard" results derived from postoperative pathological examination and Next-Generation Sequencing (NGS). This study is non-interventional; the AI results will not affect the standard of care received by the patients.

Development and Demonstration of a Precision Prevention and Treatment System for Colorectal Tubular Adenomas Lesions

This study is a prospective,multi-center and observational clinical study.Investigators would like to innovatively construct a "trinity" database of colorectal tubular adenomas based on white light - magnifying chromo - pathological images.It simulates the decision - making logic of doctors, and based on the multimodal endoscopic LAFEQ method previously proposed, develop a multimodal deep - learning diagnostic model for colon adenomas and an interpretable risk prediction model for intestinal adenomas. While achieving high - precision auxiliary treatment decisions, clearly present the decision - making basis, and break through the limitation of poor interpretability of previous medical imaging AI models.

Effects of Agent-assisted, LLM-assisted and Traditional Workflows on Diagnosis and Management Planning at Admission

The goal of this clinical trial is to evaluate whether AI-assisted workflows improve physicians' admission diagnosis and management planning performance on standardized simulated inpatient cases, among practicing internal medicine and surgery physicians across all seniority levels and across three tiers of the Chinese healthcare system. The main questions it aims to answer are: * Does the Agent-assisted workflow yield better structured admission diagnosis and management planning scores than standalone LLM assistance? * Does the Agent-assisted workflow outperform the traditional workflow without AI tools? Researchers will compare three parallel groups (traditional workflow group, LLM-assisted group, Agent-assisted group) to determine whether the Agent tool can improve diagnostic accuracy and efficiency. The goal of this clinical trial is to evaluate whether AI-assisted workflows improve physicians' admission diagnosis and management planning performance on standardized simulated inpatient cases, among practicing internal medicine and surgery physicians across all seniority levels and across three tiers of the Chinese healthcare system. The main questions it aims to answer are: * Does the Agent-assisted workflow yield better diagnosis and management planning scores than the LLM-assisted workflow? * Does the Agent-assisted workflow yield better diagnosis and management planning scores than the conventional workflow without AI assistance? Researchers will compare three parallel groups (traditional workflow group, LLM-assisted group, Agent-assisted group) to determine whether the agent tool can improve diagnostic and management planning performance and efficiency. Participants will: * Be recruited from 15 hospitals in China and participate remotely under video proctoring * Be randomly assigned to one of the three fixed workflows, with randomization stratified by hospital tier, specialty and seniority * Work through 6 anonymized simulated HIS admission cases and complete as many as they can. * Submit structured answers for each case covering principal diagnosis, secondary diagnoses, differential diagnoses, diagnostic justification, next diagnostic or therapeutic steps, consultation and referral decisions, and diagnostic confidence. * Have their operation logs and time consumption recorded automatically by the study platform. * Complete a post-session questionnaire after submitting all cases.

Impact of Artificial Intelligence on Trainee Polyp Miss Rates

Based on prior studies, trainee and practicing gastroenterologists miss pre-cancerous polyps (adenomas and serrated polyps) during colonoscopy. The use of computer-aided detection (CADe) systems, a form of artificial intelligence (AI) has been shown to help identify colorectal lesions for practicing gastroenterologists. However, less is known how AI impacts polyp detection for trainees. The investigators are conducting a tandem colonoscopy study wherein a portion of the colon is examined first by the trainee and then the attending physician. For each procedure, randomization will occur which will determine whether or not the trainee will utilize AI for their examination of the colon. At the end of the study, the investigators will determine whether AI helps trainees miss fewer polyps during colonoscopy. The investigators will also conduct interviews with trainees to understand how AI impacts colonoscopy training.

Trust in AI and Digitalization in Healthcare Across Generational Groups

This study aims to investigate differences in perception of barriers and facilitators of digitalization and Artificial Intelligence (AI) usage in healthcare across different generational groups (youth, working-age adults, and seniors). The results will help create practical recommendations for public health projects and consultants to support fair and inclusive use of new digital tools in healthcare. A cross-sectional online survey will be conducted among students at HAW, patients and employees in the rehabilitation center in Oldenburg, and seniors participating in the "Digital im Alter"(DIA) project.

Teaching Doctors in Training to Reason With Artificial Intelligence

The purpose of the TEACH-AI study is to assess whether a brief, structured workshop on artificial intelligence can improve the performance of medicine doctors in training (i.e. residents) in their diagnostic and management reasoning. In this multi-site randomized controlled trial, internal medicine and family medicine residents are assigned either to receive an in-person workshop on safe, effective LLM use before a standardized AI-assisted assessment, or to complete the same assessment before receiving the workshop. Residents will review clinical cases that are fully synthetic, no protected health information is used, using a password protected LLM interface.

Effect of AI-Supported Case Analysis on Nursing Students

The aim of this study is to determine the effect of AI-supported oncology case analysis on nursing students' knowledge, level of learning satisfaction, and clinical decision-making skills. This study is planned to be conducted using a single-blind randomized controlled trial design for the quantitative research component and an interview design for the qualitative research component. The students will be divided into two groups: an intervention group (artificial intelligence) and a control group (traditional instruction).

LLM-Generated Medical Summaries During Hospitalization to Improve Patient Activation in High-Risk Pregnancies

The goal of this clinical trial is to learn if AI-generated medical summaries help hospitalized pregnant participants better understand and actively participate in their own care. The main questions it aims to answer are: Do AI-generated summaries increase participant activation (knowledge, skills, and confidence in managing their health)? How accurate are the AI-generated summaries, as assessed by physicians? Do AI-generated summaries affect depressive symptoms during hospitalization? Researchers will compare participants who receive periodic AI-generated summaries of their medical care with participants who receive standard care to determine whether the summaries improve patient activation during hospitalization. Participants will: Be randomly assigned to receive AI-generated medical summaries or standard care. Complete the Patient Activation Measure (PAM-13) at baseline before the intervention and again after the intervention. Complete the Edinburgh Postnatal Depression Scale (EPDS) at baseline and again after the intervention. Receive up to 3 summaries during their hospitalization (intervention group only).

Identification of Depressive and Anxiety Symptoms Among a Sample of Emergency Department Patients Using Artificial Intelligence (AI) Technology

Behavioral health problems, such as depression and anxiety, are common yet often are not identified by emergency department doctors and nurses. These mental health conditions can be due to medical issues or can worsen medical problems. One way investigators hope to do a better job of learning about mental health is by training Artificial Intelligence (AI) software to detect anxiety and depression by analyzing facial expression and tone of voice. Participants are invited to participate in a study which may help improve emergency department care. An audio and video recording of the participant's responses to some simple, non-psychological questions will be analyzed by a computer to determine whether investigators can assess mood and anxiety by analyzing speech and visual patterns. The audio and video will not be listened to nor watched by study personnel, only analyzed by a computer. The investigator's hope is that it will help others in the future by aiding in the assessment of psychological state. This study is being conducted at CMC ED only.

AI-Assisted Smart Interactive Healthcare Robot

To address workforce shortages and increasing workloads in nursing, technological solutions and AI-powered robots for ward navigation have been introduced. However, limitations remain in their application to clinical care. This study aims to develop and test a programming framework for an AI-assisted nursing care robot ("E-Nursing Assistant") to reduce nurses' workload and improve the efficiency and quality of care.

EUS-Based Artificial Intelligence to Predict Outcomes in Pancreatic Cancer

The EUS-AI-R study is an observational, single-center, two-phase (retrospective-prospective) study designed to develop and validate artificial intelligence (AI) models for predicting chemotherapy response and oncological outcomes in patients with pancreatic ductal adenocarcinoma (PDAC). Patients who underwent endoscopic ultrasound (EUS) with tissue acquisition (EUS-FNA/FNB) for suspected pancreatic lesions at IRCCS San Raffaele Hospital between January 1st, 2019 and January 2026 will be retrospectively included. All patients have a histologically confirmed diagnosis of PDAC and a minimum follow-up of six months. These data derive from an IRB-approved institutional study (BIOPANCREAS; NCT06552078). Retrospective multimodal data, including EUS imaging (B-mode, elastography, contrast-enhanced EUS), clinical and laboratory variables, CT/MRI imaging, digital pathology, and molecular data when available, will be used to develop and internally validate multiple AI models. In the prospective phase, the best-performing AI model will be applied to an independent cohort of patients undergoing EUS at the same institution to evaluate feasibility, calibration, and real-world performance. No additional procedures beyond standard clinical practice will be performed.

Federated Learning for Point-of-Care Cardiac Ultrasound

This prospective, multicenter study will evaluate a federated machine-learning system designed to analyze focused cardiac point-of-care ultrasound examinations. Federated learning allows participating clinical sites to contribute to model development while keeping raw ultrasound images and directly identifiable patient information within each site's controlled computing environment. Encrypted model updates, rather than patient images, will be transmitted for secure aggregation. The prospective validation cohort will include approximately 3,000 adults undergoing clinically indicated focused cardiac ultrasound. Model performance will be compared with an expert interpretation of a comprehensive transthoracic echocardiogram performed within 24 hours. The primary objective is to determine how accurately the model identifies reduced left ventricular systolic function, defined as a left ventricular ejection fraction below 40%. During the initial validation period, the investigational software will operate in silent mode. Its results will not be displayed to treating clinicians and will not be used to diagnose participants, select treatment, or replace standard clinical interpretation. The study will also evaluate image-quality classification, cardiac-view recognition, performance across clinical sites and ultrasound systems, model calibration, processing time, cybersecurity, privacy resilience, and performance across demographic and clinical subgroups. Long-term monitoring will assess whether model performance changes as clinical populations, ultrasound equipment, acquisition practices, and software environments evolve during the 2026-2037 study period.

The Effect of AI Training on Arthroscopy Outcomes

This study is planned to determine the effects of AI-assisted regional training on surgical anxiety, fear, postoperative pain, and comfort levels in patients undergoing arthroscopy. It will be designed as a prospective, observational, and intervention-based clinical trial. Necessary ethical and institutional permissions will be obtained before the start of the research. Ethical Approvals and Permissions were obtained from the Niğde Ömer Halisdemir University Ethics Committee, Niğde Provincial Health Directorate, and Ömer Halisdemir Training and Research Hospital. The participants are adult patients undergoing arthroscopy surgery. Informed consent will be obtained for participation in the study. Intervention: AI-assisted regional training will be applied before arthroscopy. Data Collection Tools: Pre-Assessment Form, Personal Information Form, Surgical Fear Scale, Surgical Anxiety Scale for Adult Patients, Postpartum Comfort Scale, and Perianesthesia Comfort Scale will be used. Data Analysis: Data will be evaluated using the IBM SPSS Statistics 24 software package. Appropriate statistical analysis methods will be used.

AI Ambient Scribe in Psychiatry

The goal of this clinical trial is to learn if an artificial intelligence (AI) tool that listens to appointments and drafts clinical notes can help clinicians in psychiatric care. Researchers also want to know if the tool changes how patients experience their care. The study includes mental health care providers (physicians, psychologists/psychotherapists, nurses, and social workers) at Psychiatrie St. Gallen and the adult patients they see in their appointments. The main questions it aims to answer are: * Does the AI tool lower the time clinicians spend writing notes? * Are notes written with the AI tool as good as or better than notes written without it? * Do patients notice a difference in their relationship with their clinician when the tool is used? Researchers will compare clinicians who use the AI tool to clinicians who keep writing notes their usual way, before and during the intervention period, to see if writing time, note quality, and the patient-rated therapeutic relationship differ between the two groups. Clinicians will: Join one of two groups. One group starts using the AI tool on August 14, 2026. The other group keeps writing notes the usual way. Both groups will be observed until the end of November 2026, with note quality evaluation completed in December 2026. Clinicians who cannot contribute to all measurements may, with their consent, remain in the study for automated measurement of documentation time only. Patients will: * Be invited to fill out a short, anonymous survey about their experience with their clinician. * Be asked to agree, in writing, that researchers may review the report from their initial assessment appointment.

Video/Image Library of Endoscopy Procedures for the Development of AI-empowered Endoscopy Quality Reporting and Educational Modules

The goal of this observational study is to establish a video/image library dataset of complete endoscopy or partial colonoscopy procedures for patients with rectal cancer or inflammatory bowel disease (IBD). With this video/image library, the aims are: * to develop and validate novel AI-empowered solutions to automatically detect and report endoscopy quality metrics * to develop automated endoscopy reporting solutions, auditing, and educational tools for residents and fellows to enhance their endoscopy skills. The hypothesis is that a heterogeneous video/image library will provide: * comprehensive and robust source material to develop AI models * real-time quality feedback at the end of an endoscopy procedure.

Artificial Intelligence in Perioperative Nursing

This mixed-methods study aims to assess current perspectives, attitudes, and preparedness of perioperative nurses regarding the integration of artificial intelligence (AI) in clinical practice. The study targets nurses working in surgical wards and operating rooms to explore AI utilization, perceived usability, professional impact, and readiness for future implementation. Quantitative and qualitative data will be collected concurrently and integrated to generate comprehensive insights into AI adoption and future directions in perioperative nursing.

Prospective User Study and Multicenter Validation of Multimodal Medical Imaging Large Models

Following model development and locking, the fixed model is evaluated in prospectively collected CT cohorts from two centers. The study is observational and does not affect clinical care. A subset of cases is used in a randomized crossover reader study.

Postpartum Education Via Artificial Intelligence for Recovery and Loneliness: A Randomized Controlled Trial

The goal of this clinical trial is to learn whether a postpartum chatbot powered by generative artificial intelligence (genAI) can help new mothers get better pelvic floor health information and feel less lonely after childbirth. The main questions this study aims to answer are: * Does using the chatbot improve postpartum pelvic floor health knowledge? * Does using the chatbot help reduce feelings of loneliness during the postpartum period? * Does using the chatbot impact pelvic floor symptoms? Researchers will compare standard postpartum care to standard care plus the chatbot. Participants will: Be assigned by chance (like flipping a coin) to standard postpartum care with or without access to the chatbot. If in the chatbot group, participants will receive education and support via the chatbot over a 4-week period. Both groups will complete questionnaires to measure their pelvic floor knowledge, pelvic floor symptoms, feelings of loneliness, depression, infant bonding, perceived social support, adverse childhood experiences, and peri-traumatic distress. The chatbot was created by urogynecology experts in collaboration with UC San Diego computer science and biomedical informatics researchers. The chatbot is designed to give new mothers personalized, evidence-based information and support in real time.

Artificial Intelligence Supported Case Analysis and Nursing Students

In this study, it was aimed to evaluate the effect of artificial intelligence-supported case analysis method on nursing students' knowledge, case management performances and nursing diagnosis determination skills.This study was conducted in a single-blind randomised controlled trial design. Students were randomly assigned to the traditional teaching group, the case analysis group and the artificial intelligence group.

AI-Supported Case Analysis Among Nursing Students

The aim of this study is to determine the effect of AI-supported internal medicine nursing case analysis on students' case management performance, learning outcomes, learning experience, clinical self-efficacy, and cognitive load levels. This study will be conducted using a single-blind randomized controlled trial design for the quantitative research and an individual interview design for the qualitative research. Students will be randomly assigned to either the intervention (artificial intelligence) or control (case analysis) group.

Development of a Prediction Score for the Occurrence of Death or Lung Transplantation in Patients With Emphysema Secondary to Alpha-1-anti-tripsin Deficiency

Emphysema linked to alpha-1-antitrypsin deficiency (DAAT): towards a better prediction of risks Emphysema caused by alpha-1-antitrypsin deficiency (DAAT) is a rare genetic disorder that can lead to serious complications, such as the need for a lung transplant or death, affecting up to 15% of patients. The only specific treatment available is a weekly infusion of alpha-1-antitrypsin (IV-AAT), an expensive and burdensome therapy. Currently, there is no reliable model to predict the course of the disease in these patients. Our study, conducted in several French hospitals, aims to develop a prediction tool combining clinical, biological, functional data and advanced medical image analysis (lung CT). This model will make it possible to identify the most at-risk patients, in order to better adapt their care, anticipate transplant needs and avoid unnecessary treatments for low-risk patients. Ultimately, this approach could also improve access to care for patients who need it most, while optimizing health system resources.

Development and Validation of an AI Foundation Model for Frozen-Section Pathology

This multicenter observational study aims to develop and validate an artificial intelligence foundation model for frozen-section pathology. The study includes a retrospective phase and a prospective validation phase. Retrospective frozen-section pathology data will be used for model development, internal validation, and external validation. A prospective multicenter cohort of patients undergoing intraoperative frozen-section examination will then be enrolled to evaluate the model in a real-world clinical setting. The model will analyze digitized frozen-section whole-slide images and will be evaluated for prespecified frozen-section pathology diagnostic tasks across multiple organ systems. Its performance will be assessed using pathological reference standards. The primary outcome is the area under the receiver operating characteristic curve. Secondary outcomes include accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. This study is observational and will not require research-mandated changes to routine clinical care.