Clinical Research Directory

AI-TOP Study Artificial Intelligence for Trigger Optimization.

To evaluate, in a randomized controlled trial, whether AI-guided monitoring and ovulation triggering leads to clinical outcomes comparable to those achieved through physician-led decision-making in patients undergoing ovarian stimulation for IVF.

AI-based Informational Assistant for Automated Point-of-care Documentation and Protocol Retrieval

Clinical rounds in the intensive care unit (ICU) involve substantial manual documentation. Retrieving the correct protocol text and structuring notes at the bedside is time-consuming and may contribute to variation in documentation quality. Modern artificial intelligence (AI) can help structure existing information and automate protocol look-ups within a restricted, manually selected document set. The tool evaluated in this study acts as an AI-based informational assistant for clinicians. It (1) pre-populates a standardized physical-exam and daily-rounds format, (2) prepares a concise ICU course/overview using predefined formatting, and (3) retrieves relevant passages from protocols to enable rapid consistency checks by the clinician. The AI-based informational assistant does not provide treatment recommendations or patient-specific advice; all outputs require clinician verification and clinical responsibility remains with the physician.

AIR Support: Artificially Intelligent Robot (AIR) Support for Pediatric Asthma Education

The purpose of this prevention study is to evaluate the design and usability of a newly developed asthma education protocol with the Human Support Robot (HSR) for children with asthma.

Adverse Outcome of Acute Pulmonary Embolism by Artificial Intelligence System Based on CT Pulmonary Angiography

The investigators aim to build a predictive tool for Adverse Outcome of Acute Pulmonary Embolism by Artificial Intelligence System Based on CT Pulmonary Angiography.

Artificial Intelligence to Predict Surgical Outcomes and Assess Pain Neuromodulation in Trigeminal Neuralgia Subjects

Trigeminal neuralgia (TN) is the most common cause of facial pain. Medical treatment is the first therapeutic choice whereas surgery, including Gamma Knife radiosurgery (GKRS), is indicated in case of pharmacological therapy failure. However, about 20% of subjects lack adequate pain relief after surgery. Virtual reality (VR) technology has been explored as a novel tool for reducing pain perception and might be the breakthrough in treatment-resistant cases. The investigators will conduct a prospective randomized comparative study to detect the effectiveness of GKRS aided by VR-training vs GKRS alone in TN patients. In addition, using MRI and artificial intelligence (AI), the investigators will identify pre-treatment abnormalities of central nervous system circuits associated with pain to predict response to treatment. The investigators expect that brain-based biomarkers, with clinical features, will provide key information in the personalization of treatment options and bring a huge impact in the management and understanding of pain in TN.

BaiXiaoAi AI Companion for Cancer Patient Follow-up

This is a prospective, single-center, exploratory study designed to evaluate the accuracy, user engagement, and user experience of the BaiXiaoAi Companion AI. Upon signing the informed consent form and enrollment, a dedicated "Doctor-Nurse-Patient-AI" WeChat group will be established for each participant. Within the group, the BaiXiaoAi AI will provide timely responses based on patient communications and proactively push information regarding disease management and patient education.

Sexual Health and Artificial Intelligence Literacy in Young Adults: Digital Age Perspective

Young people represent a substantial proportion of the global population, and the experiences gained during adolescence and early adulthood play a critical role in shaping lifelong health behaviors. Health literacy, defined as the ability to access, understand, and use health information, is particularly important during this developmental period. Sexual health is a key component of overall well-being and quality of life. Risky sexual behaviors among young adults may lead to serious outcomes such as unintended pregnancies, sexually transmitted infections, and sexual violence. Therefore, sexual health literacy is essential for promoting safe behaviors and protecting reproductive health. In recent years, digital environments have become major sources of health information for young people. With the rapid rise of generative artificial intelligence tools, individuals increasingly rely on AI-based systems for accessing and interpreting health-related content. This highlights the growing importance of artificial intelligence literacy, which refers to the ability to understand, critically evaluate, and effectively use AI technologies. However, the relationship between artificial intelligence literacy and sexual health literacy has not yet been directly examined. This study aims to investigate the association between these two literacy domains among young adults, contributing to a better understanding of sexual health information-seeking behaviors in the digital age.

Locally Optimised Contouring With AI Technology for Radiotherapy

LOCATOR is a multicentre phase II randomised clinical trial that is looking at the process of contouring in radiation treatment for breast cancer patients. This study looks at whether contouring aided by artificial intelligence (AI) is comparable in quality to that of contouring done completely manually by a radiation oncologist. We are also looking at whether AI assisted contouring saves radiation oncologists time when compared to fully manual contouring. LOCATOR uses the LOCATOR software which is an in-house software developed locally and trained on local data.

Diagnosis Evaluation Made by Artificial Inteligence in Response to a Request Made by General Praticioner on OMNIDOC to the Dermatologist of the CHU of Nice. A Comparison of This Response by the One Made by the Dermatologist.

The First part of the study will be the inclusion of our patients (those for whom a tele-expertise request has been made by their general practitioner on the OMNIDOC platform between the 1st of May 2024 and the 31st of December 2025. ) After that, the team will investigate the non opposition patient to the use of their personal information. Subsequently, the clinical study will focus on comparing the diagnosis made by chat gpt expert and a dermatologist from the University Hospital of Nice to a request of dermatology tele-expertise made by general praticioner on OMNIDOC

Optimization of Medical Time in the Emergency Department: Impact of an AI-Based System on Prescription Entry

Drug-related iatrogenesis is a major public health issue, accounting for a significant proportion of adverse events and hospitalizations in emergency departments. Optimizing prescription management in this context is critical to improve both patient safety and physician efficiency This study aims to evaluate the impact of the POSOS AI-driven device on the medical time required for prescription management in polymedicated patients admitted to emergency departments. The main objective is to establish whether the use of POSOS can reduce transcription time compared to standard electronic management.

Attitudes and Perceptions of Corresponding Authors From Top International Medical Journals Regarding the Use of Artificial Intelligence in the Scientific Process

"Artificial intelligence (AI), including large language models and conversational tools, is increasingly being used in medical research. These tools may assist researchers at different stages of the scientific process, such as generating research ideas, reviewing the literature, analyzing data, writing manuscripts, and preparing articles for publication. While interest in AI is growing rapidly, there is still limited information on how these tools are actually perceived and used by leading medical researchers. This study aims to better understand the attitudes, perceptions, and self-reported uses of artificial intelligence among corresponding authors who have published in six major international medical journals. These authors play a key role in shaping scientific standards and editorial practices, and their views are essential to understanding how AI may influence the future of medical research. Participants are invited to complete an anonymous online questionnaire that asks about their familiarity with AI tools, how and when they use or plan to use them in the research process, the potential benefits they perceive, and the concerns or limitations they identify. The survey also explores participants' expectations regarding transparency, ethical guidance, and journal policies related to the use of artificial intelligence in scientific work.The study is observational and does not involve any medical intervention or collection of personal or health-related data. Participation is voluntary, and responses are fully anonymous.

The Relationship Between Quality of Life, Anxiety Levels, and Attitudes Toward Artificial Intelligence Among Women Undergoing Infertility Treatment

Infertility affects approximately one in six individuals worldwide and is associated with significant psychological distress, particularly among women undergoing treatment. Increased anxiety levels are strongly linked to reduced quality of life during the infertility process. With the growing integration of artificial intelligence (AI) into healthcare, AI-based tools are increasingly used in infertility care to support decision-making and patient engagement. While many patients are familiar with AI technologies, individual attitudes toward AI may influence their acceptance and potential psychosocial benefits. This study aims to examine the relationship between attitudes toward artificial intelligence, anxiety levels, and quality of life among women undergoing infertility treatment.

Real-time Feedback of Red-out Within Colonoscopy Intubation

This study will employ a prospective, multicenter, controlled design. It will be conducted across multiple centers, with participated centers randomly assigned to one of four groups: Group A, Group B, Group C, and Group D. The research will primarily focus on the AI-based analysis of colonoscopic images to calculate the following metrics: caecal intubation time, red-out percentage, and the AI-based red-out avoiding score. Based on the study's implementation protocol, a decision will be made regarding whether to provide real-time feedback. Additionally, the presence of any complications will be assessed both during and after the colonoscopy procedure.

AI-Assisted 2D Fetal Brain Ultrasound for Intracranial Anomaly Detection

Timely detection of fetal brain anomalies is critical for improving prenatal counseling and postnatal neurological outcomes. Ultrasonography is the most commonly used and effective imaging method for evaluating fetal structures; however, diagnostic accuracy can be affected by operator experience, fetal position, and image quality, leading to variability in interpretation. Artificial intelligence (AI)-based image analysis offers a new opportunity to standardize diagnostic assessment and reduce subjectivity in ultrasound interpretation. This study aims to evaluate the diagnostic accuracy and clinical applicability of an AI-assisted model (Alyssia) designed to analyze archived 2D fetal brain ultrasound images. The model will be trained and validated to distinguish between normal and abnormal intracranial findings, focusing particularly on the lateral ventricles and other relevant brain regions. The research employs an observational, retrospective design using anonymized ultrasound data obtained during routine prenatal examinations between 18 and 24 weeks of gestation. Expert clinicians will review and label all eligible images to establish ground truth classifications for model training and validation. A deep learning-based algorithm will be developed to automatically classify these images, and its performance will be evaluated using accuracy, sensitivity, specificity, precision, and F1-score metrics. Misclassified cases will be qualitatively analyzed to determine contributing factors such as image quality, anatomical variability, and gestational differences. By comparing AI model outputs with expert-labeled references, the study will assess the model's ability to enhance diagnostic standardization and reduce inter-observer variability. The findings are expected to provide valuable insights into the integration of AI-based decision support systems in prenatal neurosonography. Ultimately, this research aims to support earlier and more reliable detection of fetal brain anomalies, contributing to improved prenatal care and healthier outcomes for mothers and infants.

Artificial Intelligence in Assessing Gastric Intestinal Metaplasia Via the EGGIM Score

The endoscopic grading system (EGGIM) has been widely used to assess the extent of gastric intestinal metaplasia during endoscopy. Investigators developed an artificial intelligence (AI) system to automatically evaluate the extent of gastric intestinal metaplasia (GIM) and calculate the EGGIM scores in endoscopy examination. This study is a prospective, multi-center study aimed at exploring the performance and reliability of AI-EGGIM scoring. This is a prospective study designed to validate the AI-EGGIM system in a larger cohort. The study protocol was developed based on preliminary experience from a prior investigation (NCT05464108).

Miss Rate of Gastric Neoplasms Under Computer-aided Endoscopy

This prospective randomized trial compares AI-assisted upper gastrointestinal endoscopy with high definition upper gastrointestinal endoscopy in term of missed rate of gastric neoplasm. The investigators hypothesize the miss rate of high definition upper gastrointestinal endoscopy is higher than AI-assisted upper gastrointestinal endoscopy.

Glioma Adaptive Radiotherapy With Development of an Artificial Intelligence Workflow

Gliomas are common primary brain tumors in adults. Gliomas can be classified into different types based on tumor grade, histopathological features, and molecular characteristics. The common types of diffuse gliomas include glioblastoma, astrocytoma, and oligodendroglioma. The standard treatment for diffuse gliomas includes surgery followed by radiation and chemotherapy. As per standard institutional practice, a uniform dose of radiation is delivered to the disease area and MRI is done before and after the treatment. In this study, MRI and PET scan will be done before starting the treatment and standard dose of radiation will be delivered. The interval imaging will be done twice during the course of treatment with MRI and PET, followed by dose modifications. The CT, MRI, and PET will be combined. Based on PET imaging, specific dose will be altered and delivered to specific areas. Dose modification will be done with the help of artificial intelligence. Participant's assessment will be done at regular intervals. Modifications in radiation plans are done based on the changes in disease seen in scans is likely to improve the accuracy of RT treatments. Dose modifications based on imaging to resistant areas will help achieve better tumor control, reduce treatment-related toxicities, precise delivery of the RT and adjusting doses to the organs at risk (OAR) and changes in disease leading to better treatment compliance. Creating an artificial intelligence framework in radiation oncology promises to improve quality of workflow, treatment planning and RT delivery. The aim of the study is to develop an artificial intelligence workflow for treatment of glioma with adaptive radiotherapy. This study will be conducted in Tata Memorial Centre on a population of 60 patients for a duration of 2 years. The total study duration is 4 years.

Multimodal Radiology Report to Improve Patient-centered Radiology

The goal of this study is to learn if AI-generated video explanations help people better understand their radiology reports. The main question it aims to answer is: Do AI-generated videos help participants understand their medical imaging results better than written reports alone? Participants will send their own radiology images and written reports to the research team; receive a personalized AI-generated video that explains their results in easy-to-understand language; watch their video explanation (about 1-5 minutes long); and complete 15-minute online survey about how well the video helped them understand their results.

AI Toothbrush and Visual Pedagogy to Improve Oral Hygiene in Children With Autism Spectrum Disorder

This randomized clinical trial evaluates the effectiveness of an AI-enabled electric toothbrush and visual pedagogy materials in improving oral hygiene among children with autism spectrum disorder (ASD). The study compares plaque control, gingival health, and adherence between children using a manual toothbrush with visual pedagogy support and those using an AI-enabled electric toothbrush with app-based monitoring.

Augmented Endobronchial Ultrasound (EBUS-TBNA) With Artificial Intelligence

To evaluate the usefulness of Deep neural network (DNN) in the evaluation of mediastinal and hilar lymph nodes with Endobronchial ultrasound (EBUS). The study will explore the feasibility of DNN to identify lymph nodes and blood vessel examined with EBUS.

Assessment of Liver Diseases Using a Deep-Learning Approach Based on Ultrasound RF-Data

The goal of this clinical trial is to test the performance of neuronal networks trained on ultrasonic raw Data (=radiofrequency data) for the assessment of liver diseases in patients undergoing a clinical ultrasound examination. The general feasibility is currently evaluated in a retrospective cohort. The main questions the study aims to answer are: * Can a neuronal network trained on RF Data perform equally good as elastography in the assessment of diffuse liver diseases? * Can a neuronal network trained on RF Data perform better than a neuronal network trained on b-mode images in the assessment of diffuse liver diseases? * Can a neuronal network trained on RF Data distinguish focal pathologies in the liver from healthy tissue? To answer these questions participants with a clinically indicated fibroscan will undergo: * a clinical elastography in Case ob suspected diffuse liver disease * a reliable ground truth (if normal ultrasound is not sufficient e.g. contrast enhanced ultrasound, biopsy, MRI or CT) in case of focal liver diseases, depending on the standard routine of the participating center * a clinical ultrasound examination during which b-mode images and the corresponding RF-Data sets are captured

Artificial Intelligence for Pathology Diagnosis and Prognosis Prediction of Lung Nodule Using Smartphone Photos

The current study aims to develop and validate a deep learning signature for diagnosing pathology and predicting prognosis of lung nodule using smartphone photos of resected tumor specimens.

A Real-world Study of the Efficacy and Safety of ICIs as First-line Therapy for Advanced Malignancies

In this study, we collected the data of immunohistochemistry, gene detection, image, OS, PFS, Orr, and so on. Secondly, the database of immunotherapy for malignant tumor was established, and the predictive model was constructed to verify and establish the rationality and validity of the biomarkers and predictive system of immunotherapy

Assisting Pulmonary Disease Diagnosis With Ophthalmic Artificial Intelligence Technology

This study intends to collect ophthalmologic examination results, pulmonary examination results and related indexes from patients with pulmonary disease and control populations, and combine big data analysis and artificial intelligence technology to explore whether new methods can be provided for early screening strategies for pulmonary disease with the aid of ophthalmologic examination, and thus assist in identifying the types of pulmonary disease and determining disease prognosis.