Clinical Research Directory

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.

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.

Study on Female Patients' Mammographic Texture Features

Mammography is the most common method for breast imaging, and it provides information for model building and analysis. Radiomics applied to mammography has the potential to revolutionize clinical decision-making by providing valuable insights into risk assessment and disease detection. Despite this, the influence of imaging parameters and clinical and biological factors on radiological texture features remains poorly understood. There is a pressing need to overcome the obstacle of system-inherent effects on mammographic images to facilitate the translation of radiological texture features into routine clinical practice by enabling reliable and robust AI-based or AI-aided decision-making. Furthermore, understanding the relationship between imaging parameters, textural features, and clinical and biological information supports the clinical use of AI. The objective of this study is to evaluate AI methods for clinical practice and to study how it relates to clinical factors and biological features.

Artificially Intelligent Robot Control

The purpose of the study is to compare the impact of standard asthma education with the standard + Artificially Intelligent Robot (AIR) Control intervention.

Diagnostic Accuracy of a Deep Learning-Based Software for Automated Multiparametric Echocardiographic Measurements

Transthoracic echocardiography is an essential imaging modality for the diagnosis and follow-up of cardiovascular diseases. Comprehensive echocardiographic assessment requires multiple quantitative measurements of cardiac structure and function, which are time-consuming and highly dependent on operator expertise. US2.AI (Us2.v1) is an artificial intelligence (deep learning)-based software designed to automatically analyze standard two-dimensional and Doppler echocardiographic DICOM video clips acquired from different ultrasound vendors. The software provides automated measurements of cardiac morphology and function, including chamber dimensions and volumes, left and right ventricular systolic and diastolic function, myocardial strain, and Doppler-derived parameters, generating a comprehensive echocardiographic report based on current international guideline recommendations. In addition, the software may assist in identifying echocardiographic features suggestive of several cardiovascular conditions, including heart failure, pulmonary hypertension, hypertrophic cardiomyopathy, cardiac amyloidosis, valvular heart disease, and ischemic cardiomyopathy.

Artificial Intelligence-enabled ECG Detection of Congenital Heart Disease in Children: a Novel Diagnostic Tool

Congenital heart disease (CHD) is the most common congenital disease in children. The early detection, diagnosis and treatment of CHD in children is of great significance to improve the prognosis and reduce the mortality of children, but the current screening methods have limitations. Electrocardiogram (ECG), as an economical and rapid means of heart disease detection, has a very important value in the auxiliary diagnosis of CHD.Big data and deep learning technologies in artificial intelligence (AI) have shown great potential in the medical field. The advent of the big data era provides rich data resources for the in-depth study of CHD ECG signals in children. The development of deep learning technology, especially the breakthrough in the field of image recognition, provides a strong technical support for the intelligent analysis of electrocardiogram. The particularity of children electrocardiogram requires the development of a special algorithm model. At present, the research on the application of deep learning models to identify children's electrocardiograms is limited, and the training and verification from large data sets are lacking. Based on the Chinese Congenital Heart Disease Collaborative Research Network, this project aims to integrate data and deep learning technology to develop a set of intelligent electrocardiogram assisted diagnosis system (CHD-ECG AI system) suitable for children with CHD, so as to improve the early detection rate of CHD and improve the efficiency of congenital heart disease screening.

Artificial Intelligence-Assisted Advanced Analysis of Knee Imaging and Outcome Prediction

This study aims to develop and validate an artificial intelligence (AI)-assisted platform for musculoskeletal knee ultrasonography and to establish an interpretable prediction model for clinical outcomes following ultrasound-guided injection therapies in patients with degenerative knee disorders. The project seeks to improve the standardization, reproducibility, and clinical utility of knee ultrasound by reducing operator dependency and providing quantitative image analysis and outcome prediction. The study will be conducted in three phases. First, an AI foundation model for knee ultrasonography will be developed using standardized image acquisition protocols to enable automated localization, segmentation, and quantitative assessment of major anatomical structures, including tendons, ligaments, cartilage, fat pads, and peripheral nerves. Second, supervised machine learning models will be trained to classify normal and pathological ultrasound findings, including common degenerative and inflammatory abnormalities affecting the knee. Third, retrospective and prospective clinical data from approximately 150 patients receiving ultrasound-guided injection therapies will be integrated to develop and validate a predictive model for treatment outcomes using imaging biomarkers and clinical variables. Treatment response will be evaluated using validated patient-reported outcome measures, and explainable AI methods will be applied to improve model interpretability. The anticipated outcome of this study is the development of a comprehensive AI-assisted knee ultrasound platform that supports standardized image interpretation, quantitative assessment of musculoskeletal pathology, and personalized prediction of treatment response to ultrasound-guided injection therapies in degenerative knee disorders.

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.

AI Readiness in Orthotics-Prosthetics

The aim of this study is to analyze, using a mixed-methods approach, the attitudes, perceptions, levels of technology acceptance, and competencies in the use of generative artificial intelligence among healthcare professionals working in the field of orthotics and prosthetics. The study will reveal how the technological transformation in orthotics and prosthetics is perceived by healthcare professionals, and will also identify the professional requirements, barriers, and opportunities for integrating artificial intelligence technologies into practice. In this way, it aims to provide a scientific reference for decision-makers to support the updating of professional education programs in orthotics and prosthetics, the development of institutional policies, and the wider adoption of AI-supported clinical applications.

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.

The Impact of AI-Assisted Nursing Care Intervention Within the Enhanced Recovery Framework on Outcomes of Colorectal Surgery Patients

Colorectal cancer is one of the most common cancers worldwide, affecting a large number of people each year (Bray et al., 2022). Surgical intervention remains the gold standard in treatment. However, advances in surgical techniques and increased effectiveness of neoadjuvant therapies have brought sphincter-preserving surgeries to the forefront, reducing the need for stoma creation compared to the past (Jo \& Wilson, 2025; Wang et al., 2025). Even without stoma creation, these patients face complex care needs in the post-discharge period, including changes in bowel habits, nutritional management, and adaptation to physical activity (Wang et al., 2025). Difficult-to-manage complications carry a high risk of readmission to the hospital. Patients receive limited support during the transition from the hospital to home and at home (Storm et al., 2024). Patients and their families are often left alone to manage home care until routine follow-up appointments. Patients, especially those poorly prepared for discharge, may not know how to perform care practices at home or what to watch out for in case of complications. Situations that are well managed in the hospital can spiral out of control upon inadequate follow-up after the patient returns home, leading to unplanned readmissions. Insufficient postoperative patient follow-up can cause anxiety in patients, leading to readmissions due to the inability to manage the home care process effectively (Storm et al., 2024). Although accelerated recovery after surgery (ERAS) is known to shorten hospital stays (Gustafsson et al., 2025; Gustafsson et al., 2019), studies show varying results regarding readmissions, re-operations, developing complications, and survival (Coleman et al., 2006; Takchi et al., 2020; Lee et al., 2022). These variable results highlight the need for a structured discharge process and home care management for patients who undergo ERAS and are discharged home earlier. In the study by Takchi et al. (2020), a scheduled phone call was proposed as the final step in advanced recovery recommendations and presented as a pilot study. The study reported that each patient contacted reported at least one symptom and personal care need (Takchi et al., 2020). The scheduled phone calls proposed by Takchi et al. (2020) are an important monitoring mechanism in the recovery process; however, they are insufficient. Supporting this monitoring process with a structured discharge management and AI-powered digital video accessible to the patient at any time, extends the continuity of care to a digital dimension. It is reported that AI-powered multimedia tools, whose use is increasing with the transformation in health technologies today, reduce cognitive load by concretizing complex surgical processes with audiovisual materials and improve patients' self-care skills regardless of their health literacy level (Mendoza-Pinto et al., 2025). "Content prepared with generative artificial intelligence algorithms, in particular, increases the retention of information and the patient's digital health literacy compared to traditional educational materials (Zaretsky et al., 2024). This study aims to both structure the discharge and post-discharge follow-up process, which is included in ERAS protocols to a limited extent, and to increase the patient's readiness for discharge, improve patient outcomes, and facilitate home care management using AI-assisted educational videos. Thus, the study significantly points to a fourth step, which is included in ERAS guidelines in the pre-operative, intra-operative, and post-operative phases and is felt to be missing: the discharge and home follow-up process.

AI-Based Education and Menstrual Health Behaviors in Adolescents

This study is designed as a randomized controlled trial aiming to compare the effectiveness of different educational approaches in improving dysmenorrhea self-care and genital hygiene behaviors among adolescent girls. Participants will be allocated into three groups: an artificial intelligence-supported mobile education group, a face-to-face education group, and a brochure-based control group. The intervention process will be conducted using a pretest-posttest design, with assessments performed at baseline, 4 weeks after baseline, and 8 weeks after baseline. In the artificial intelligence-supported mobile education group, participants will receive individualized and interactive content, while the same content will be delivered directly by the researcher in the face-to-face education group, and written informational materials will be provided to the control group. Valid and reliable instruments assessing dysmenorrhea self-care behaviors and genital hygiene practices will be used for data collection. The findings are expected to provide evidence on the effectiveness of digital health interventions in adolescent health and contribute to the development of nursing practices and health education programs.

Artificial Intelligence-Generated vs Academician-Developed Multiple True/False Questions in Anesthesiology Education

This prospective observational study aims to evaluate the effectiveness and educational value of artificial intelligence (AI)-generated multiple true/false questions compared to those developed by experienced academicians in anesthesiology training. A total of 27 anesthesiology residents will be included in the study. Question sets consisting of 200 multiple true/false items will be created, with half generated by academicians and the other half generated using an artificial intelligence model (ChatGPT-based system). The questions will be based on standardized educational materials from the anesthesiology training curriculum. Participants will complete the test in a single session. Each correct answer will be scored as one point, and total scores will be calculated. In addition to test performance, item difficulty, discrimination indices, and test reliability will be analyzed. Furthermore, participants' perceptions regarding question quality will be evaluated. The study aims to determine whether AI-generated questions can provide a reliable and effective alternative to traditional question development methods in medical education and contribute to more objective and standardized assessment processes.

Insulin Resistance as a Predictor of Pulsed Field Ablation Success in Atrial Fibrillation (HOMA-PULSE)

This study investigates whether insulin resistance, a metabolic condition where the body's cells respond poorly to insulin, can predict the success of atrial fibrillation (AF) ablation using pulsed field ablation (PFA) technology. Atrial fibrillation is the most common heart rhythm disorder, affecting 2-4% of adults. Catheter ablation is an effective treatment, but 20-40% of patients require a repeat procedure. Identifying patients at higher risk of ablation failure could improve treatment planning and outcomes. Scientific evidence suggests that insulin resistance - which can exist for years before diabetes develops - may contribute to electrical and structural changes in the heart that promote AF. However, no prospective study has systematically examined whether insulin resistance measured by the HOMA-IR index predicts ablation outcomes, particularly with the newest pulsed field ablation technology. HOMA-PULSE is a prospective observational study enrolling at least 120 non-diabetic patients undergoing their first AF ablation using pulsed field ablation at the Cardiocentrum, AGEL Hospital Trinec-Podlesi, Czech Republic. On the day of ablation, fasting blood samples are collected as part of routine preoperative care. A portion of these samples is used to measure insulin resistance (HOMA-IR index, calculated from fasting glucose and insulin levels) along with additional biomarkers including GDF-15, hs-CRP, NT-proBNP, IL-6, and IL-1beta. Detailed procedural and clinical data are recorded. Patients attend a single follow-up visit at 4-5 months post-ablation - a standard part of clinical care after AF ablation. The primary outcome is the clinical decision regarding need for repeat ablation (reablation), made by the treating physician blinded to the HOMA-IR result. The study does not involve any additional procedures, visits, or interventions beyond standard clinical care. The only research-specific element is the additional laboratory analysis of biomarkers from blood samples that would be drawn regardless of study participation. Additionally, intracardiac electrograms recorded during the ablation procedure will be analyzed using deep learning neural network models to extract electrophysiological features and evaluate whether insulin resistance has a detectable electrophysiological signature that can be captured by artificial intelligence. If a significant association between insulin resistance and ablation outcomes is confirmed, this could lead to new strategies combining ablation with metabolic optimization to improve success rates.

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-Assisted Workflow for Occult Atrial Fibrillation Detection After Ischemic Stroke: A Prospective Randomized Trial

We hypothesize that an AI-guided AF risk stratification approach, particularly when combined with intensified rhythm monitoring using wearable devices and extended ECG patches, will significantly increase AF detection rates compared with standard care. By enabling earlier identification of patients who may benefit from anticoagulation therapy, this strategy has the potential to improve clinical outcomes while minimizing unnecessary exposure to anticoagulant-related bleeding risks. Ultimately, this trial seeks to provide robust clinical evidence supporting the integration of AI-assisted ECG analysis into routine post-stroke care, advancing precision medicine and optimizing resource allocation for patients with ischemic stroke.

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.

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.

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.