Clinical Research Directory

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.

Designing a Large Language Model Architecture for Shared Decision Making and Informed Intentions

This study investigates a novel, agentic Large Language Model (LLM) architecture designed to facilitate Shared Decision-Making (SDM) in healthcare. While patients increasingly use standard LLMs for health information, these models often struggle with multi-turn conversations and fail to adapt to varying reading levels, disadvantaging vulnerable groups. By utilizing an agentic state-machine, this project aims to overcome common LLM deficits-such as context loss and uncritical agreement-to ensure clinically accurate, participatory patient conversations. The study evaluates whether this architecture improves informed decision-making compared to standard LLMs, particularly for patients with low health literacy.

Social Software-Based Intelligent Management System for Bowel Preparation

Colorectal cancer (CRC) is one of the most common malignancies worldwide, and colonoscopy is an important method for CRC screening and early detection. The effectiveness of colonoscopy is closely related to the quality of bowel preparation. However, inadequate patient understanding and poor adherence to bowel preparation instructions may reduce preparation quality. This randomized controlled trial aims to evaluate the effectiveness of an artificial intelligence (AI)-assisted bowel preparation management system based on WeChat in patients undergoing colonoscopy. Participants will be randomly assigned to either an intervention group receiving AI-assisted bowel preparation guidance and management through WeChat or a control group receiving conventional bowel preparation education and management. The study will compare bowel preparation quality between the two groups and assess whether AI-assisted management can improve patients' adherence to bowel preparation instructions and the overall effectiveness of bowel preparation.

AI-Generated Explanations and Dental Students' Clinical Decision-Making

This randomized controlled trial evaluated the effect of AI-generated explanations on clinical decision-making among undergraduate dental students. UG dental students were randomly assigned in a 1:1:1 ratio to one of three groups: a no-explanation control group, a correct AI-explanation group, or a plausible misleading AI-explanation group. All participants completed the same 15 clinical case-based scenario questions and rated their confidence after each response. The primary outcome was clinical decision-making accuracy. Secondary outcomes included confidence and confidence calibration, as well as selection of the predefined incorrect answer supported by the misleading explanation. The study was designed to determine whether correct AI-generated explanations improve performance and whether plausible but misleading explanations can direct students toward specific clinical errors.

Artificial Intelligence for Automated Clinical Data Exploration From Electronic Medical Records (CardioMining-AI)

The purpose of this study is to highlight the usefulness of artificial intelligence and machine learning to develop computer algorithms that will achieve with great reliability, speed and accuracy the automatic extraction and processing of large volumes of raw and unstructured clinical data from electronic medical files.

AI Prediction of PIVC-Related Phlebitis

This study aims to develop and validate multimodal AI-based prediction models for peripheral intravenous catheter (PIVC)-related phlebitis by integrating demographic, clinical, catheter-related, and phlebitis image data. The models will be evaluated for their ability to accurately predict phlebitis risk, with the goal of facilitating early identification, timely intervention, and improved patient safety.

A Prospective, Multicenter, Portable Pulmonary Function Comparison Study Aimed at Evaluating the External Clinical Validation of the Chronic Obstructive Pulmonary Disease Auxiliary Diagnostic Software LH-002 (Version: 2.0.0) in Clinical Diagnosis Accuracy

Chronic Obstructive Pulmonary Disease (COPD) is widely underdiagnosed in low- and middle-income countries and regions, due to the lack of accessible tools. Conventional spiromery tests for COPD diagnosis are expensive, require trained personnel to operate properly. Portable spirometry highly relies on the quality assurance of the tests. Since artificial intelligence (AI)-based cough analysis has demonstrated promising results in COPD diagnosis, this study aimed to evaluate the accuracy of an AI-based cough analysis software (Cough Search) compared to a portable spirometer for the auxiliary diagnosis of COPD.

Use of and Attitudes Towards Large Language Models for Health Information Among People With Cancer, the Public, and Health-care Professionals

This cross-sectional survey examined how people with cancer, the general public and health-care professionals use large language models (LLMs) such as ChatGPT for health information, what they expect from them, how comfortable people with cancer are with clinicians using them, and which conditions they set for their use. Three English-language questionnaires were deployed through the Prolific research panel between December 24, 2024, and March 30, 2025. The public sample used US and UK recruitment quotas, whereas recruitment of people with cancer and professionals also included other countries. After exclusion of blank, duplicate and repeat records and of main-survey responses from pilot participants, the analysed sample comprised 2142 people with cancer, 1957 public respondents and 2714 professionals (6632 distinct participants). Six principal comparisons of people with cancer with the public on items with identical wording were tested at a Bonferroni-adjusted alpha of 0.0083. All other analyses were exploratory. The study was registered retrospectively after data collection.

Development of Assist Tool for Breast Examination Using the Principle of Ultrasonic Sensor

The accuracy of breast examinations and ultrasonography performed clinically to detect breast mass varies greatly depending on the physician\'s skill level, and the accuracy of breast examinations by non-experts is particularly low. In this study, we aimed to validate whether the concurrent use of ultrasound sensor technology is an efficient strategy for the purpose of improving the sensitivity of detecting breast masses through breast examination.

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.

Autonomous Artificial Intelligence Versus AI Assisted Human Optical Diagnosis

Computer-aided image-enhanced endoscopy can predict the nature of colorectal polyps with over 90% accuracy. This technology uses artificial intelligence (AI) to analyze video recordings of polyps, learning to make diagnoses in real-time. This means that doctors can get immediate predictions about small polyps during the procedure, reducing the need for separate pathology exams and saving costs, ultimately improving patient care. Human and AI interactions are complex and a framework to reap synergistic effects CADx systems when used by humans to harness optimal performance needs to be established. AI solutions in medicine are usually developed to be used as assistive devices, however, then they rely on humans to correct AI errors. Optical polyp diagnosis is a complex task. Non experts usually achieve diagnostic accuracy in 70-80%. CADx systems have a similar diagnostic accuracy when used autonomously. Clinical evaluation of CADx systems showed that CADx assisted OD performs equally to the operator performance when using non CADx assisted OD. To harness a benefit of clinical CADx implementation we would have to find a way that synergies between human and CADx come into play to eliminate cases in which CADx assisted and/ or human OD results in low diagnostic accuracy and also addresses the problem of serrated polyp recognition.

Using AI-assisted Optical Polyp Diagnosis for Diminutive Colorectal Polyps

This is a prospective study that is the first to implement resect and discard and diagnose and leave strategies in real-time practice using stringent documentation and adjudication by 2 expert endoscopists as the gold standard. The primary aim of this study is to show the accuracy of intracolonoscopy AI-assisted optical diagnosis (CADx; autonomous or with human input) when the AI-assisted optical diagnosis made by the expert endoscopists is used as the reference standard. The specific aims are: 1. To evaluate the accuracy of intracolonoscopy AI-assisted optical polyp diagnosis (autonomous or with human input) by comparing it to the obtained optical histology diagnoses provided by two independent expert endoscopists as the reference standard. 2. To evaluate the agreement between the intracolonoscopy AI-assisted optical polyp diagnosis (autonomous or with human input) and the AI-assisted optical diagnosis performed by two independent expert endoscopists. 3. To determine whether AI-assisted optical polyp diagnosis for diminutive (1-5 mm) polyps can be implemented in routine clinical practice by demonstrating that at least 70% of the approached patients are interested in undergoing AI-assisted optical diagnosis (autonomous or with human input). 4. To evaluate the cost savings resulting from replacing pathology with AI-assisted optical diagnosis.

Artificial Intelligence (AI) Assisted Real-time Adenoma Detection and Classification During Colonoscopies

This is a pragmatic, double-blind, randomized, controlled trial, to evaluate the effect of implementing a Computer-assisted detection (CADe) system within the routine clinical practice of Canadian healthcare institutions. The main hypothesis of this study is that the ADR in the operating room equipped with the GI genius CADe system will be significantly higher than the ADR in the ordinary operating room.

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.

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.

AI Chatbots Versus Classroom Attendance Implications for Learning Outcomes and CGPA Among Physical Therapy Students

Artificial intelligence-powered chatbots (AI-PCs) are increasingly integrated into educational settings, including health care disciplines. Despite their potential to enhance learning, limited research has investigated physiotherapy (PT) students' acceptance of this technology.

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.

A Practical Exploration of Developing a Full-English Orthopedic Nursing Curriculum Based on DeepSeek Intelligence

This educational reform study aims to explore whether a full English course built on the DeepSeek artificial intelligence platform can improve orthopedic nurses' professional English competence. It will also examine nurses' satisfaction with AI-assisted teaching. The main questions it seeks to answer include: Will the DeepSeek-based full English course improve orthopedic nurses' professional English test scores? Will nurses' transcultural nursing self-efficacy and nurse-patient therapeutic interaction ability improve after the course training? What are nurses' experiences when using the DeepSeek AI platform for learning? Researchers will compare the English proficiency changes of the same group of orthopedic nurses before and after the training to observe the course effects. Participants will: Attend an 11-week full English course based on the DeepSeek platform, approximately 1-2 hours per week Complete a professional English written test and an oral proficiency assessment before and after the training Complete the Transcultural Self-Efficacy Tool (TSET-CV) and the Nurse-Patient Therapeutic Interaction Scale (NuPTIS) before and after the training Complete a teaching satisfaction and AI learning experience questionnaire after the course

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.