Clinical Research Directory

Advanced Symptom Palliation Through Integrated Relief Engagement

Beacon is a digital platform that processes objective and subjective aggregated data provided by patients. Objective data is provided by standard wearables, while subjective data is provided by patient-reported outcome measures (PROMs), comprising written and vocal patient reporting. The ASPIRE.AI study is a prospective study evaluating the feasibility of clinicians' use of aggregated data that was provided by patients and analyzed through "Beacon", and its influence on advanced cancer patients' palliative symptoms management. Approximately 40 consecutive eligible ambulatory advanced cancer patients first attending the palliative unit in the Davidoff Center will be enrolled. The trial will continue for \~1 year, with each patient participating in this trial for a total of about 12 weeks. All participants will receive the intervention. The intervention comprises the palliative standard of care treatment along with the usage of the Beacon digital platform, which enables comprehensive data collection and aggregation regarding the patient's biopsychosocial status, and thus, the patient's symptom burden. Data collected and aggregated through Beacon includes Beacon data provided by the patients via wearables (smartwatch/sensors), smartphones, and written and recorded PROMs. Researchers will then evaluate physician engagement with the platform, Influence on treatment, and the physician user experience rating as well as patients' adherence, satisfaction with Beacon usage, and changes in patients' symptom burden and quality of life.

AI-Driven Multimodal Imaging Integration for Diagnosis and Prognostication of Digestive System Diseases

The goal of this observational, retrospective and prospective study is to develop a noninvasive disease assessment system by leveraging artificial intelligence (AI) to comprehensively analyze multi-modal imaging features, including magnetic resonance enterography (MRE) and computed tomography enterography (CTE), for the diagnosis and prognostication of digestive diseases. To this end, we retrospectively enrolled imaging, endoscopic, and clinical data from 21 centers across China to construct and iteratively optimize the AI model. The model's performance will be prospectively validated in two centers, and its accuracy in lesion localization will be verified through real-world deployment in endoscopy suites. Participants will be randomly assigned to either conventional endoscopy or virtual endoscopy groups. The predictive performance of both groups for prognostic indicators, such as clinical remission rate and recurrence risk, will be compared during follow-up to verify the non-inferiority of the virtual endoscopy group.

Improving Artificial Intelligence-derived Algorithms for Estimating Length and Weight in NEonateS and infanTs up to 6 Months of Age (NEST)

The NEST study is a prospective, observational research study designed to collect clinical measurements and image data to develop and evaluate artificial intelligence (AI)-derived algorithms for estimating anthropometric parameters in neonates and young infants. The study focuses on infants from birth up to 6 months of age and aims to assess the accuracy of AI-based estimations of length, weight, and head circumference using photographs and/or video recordings captured during routine clinical care. These AI-derived measurements will be compared against standard clinical measurements obtained by trained healthcare professionals in neonatal and infant care settings.

AI-based Physiotherapy Evaluation System for Range of Motion in Oral Cancer Patients

This study aims to evaluate the validity and reliability of a novel AI-based physiotherapy evaluation system for measuring oromandibular and neck-shoulder range of motion (ROM). Traditional ROM assessments rely on manual measurements, which may be influenced by rater experience and variability. The proposed AI system uses automated keypoint tracking to provide objective and standardized measurements. In this cross-sectional study, healthy adult participants will perform standardized ROM tasks. Measurements obtained from the AI system will be compared with those from two independent raters using conventional clinical tools. Repeated measurements will be conducted to assess intra-rater and inter-rater reliability. The agreement between the AI system and human raters will be evaluated to determine the system's clinical applicability.

Scientific Validity Assessment and Optimization of AI-Generated A3/A4 Type Questions for the Chinese Medical Licensing Examination

This is a cross-sectional study that primarily employs quantitative analysis, supplemented by qualitative assessment. The research is conducted in two stages: Phase I consists of a model performance comparison experiment, and Phase II involves an item quality evaluation experiment. The entire study adheres to the principles of single-blinding, randomization, and standardization to ensure scientific rigor and reproducibility. The single-blind design is implemented during the "standardized testing" phase, where the system intersperses AI-generated items with those authored by human experts. Participants remain blinded to the source of each item (AI-generated vs. human-authored) throughout the testing and scoring processes, thereby ensuring the objectivity of the evaluation results.

Validation on Clinical Adaptability of the Foundation Model Specific to Neuroimaging Diagnosis

This clinic trial aims to investigate whether artificial intelligence (AI) diagnostic tools at neurological diseases diagnosis on brain CT/MRI can improve the work efficiency of specialized neuroimaging physicians, with a specific focus on its clinical value in distinguishing normal from abnormal findings, critical value identification, and neurological disease classification. Using pathological and/or discharge diagnoses of neurological diseases as the gold standard, an AI model will be trained on over 10,000 CT/MRI cases to achieve diagnostic performance comparable to that of neurological radiologists before being transformed and putted to use. Furthermore, clinical trials will be conducted in sub-studies (abnormal cases identification, critical value assessment, and neurological disease classification) to validate the clinical utility of AI and human-AI collaboration in the precise diagnosis of neurological disorders. The expected outcomes include reducing missed and misdiagnosis rates, enabling rapid screening of critical conditions, and achieving precise imaging-based diagnosis by using AI tools.

Evaluation of Dora Care for Supporting Fracture Liaison Services (FLS)

What is the study about? This study is testing "Dora", an AI-powered assistant that can make phone calls to patients, for use in the Fracture Liaison Service (FLS). The FLS is a clinic that helps prevent more bone fractures after an initial "fragility fracture" (a break that happens easily, usually due to osteoporosis). Why is this being done? FLS clinicians often have to spend a lot of time on routine phone calls for assessments and follow-ups. If Dora can safely and accurately collect patient information, it might save time for staff and still give patients a good experience. What will happen to patients in the study? Invitation and consent - Patients with a new fragility fracture who are eligible will be invited to take part after informed consent. Dora call - Patients will receive an automated phone call from Dora, at the start of their FLS pathway and at follow-up. At intake, Dora will ask about risk factors for bone problems (e.g., smoking, alcohol use, family fracture history). At follow-up, Dora will ask about medication use, side effects, falls, or new fractures. Clinician call - Soon after, patients will have their usual phone appointment with an FLS clinician, who asks similar questions. Surveys/interviews - Patients will be asked to complete a short questionnaire and take part in an optional interview to say how they felt about talking to Dora. What about clinicians? Clinicians involved in the FLS pathway will be asked to complete a short survey and to take part in an optional interview to understand how useful Dora's reports might be in their work. Who can take part? Patients - Age 50+, English-speaking, with a new fragility fracture, and able to use the phone. Clinicians - Those working in FLS or similar bone health services. How long will it take? Each patient might be involved for up to about 7 months. The whole study will take about a year.

OpenEvidence Safety and Comparative Efficacy of Four LLM's in Clinical Practice

OpenEvidence is an online tool that aggregates and synthesizes data from peer-reviewed medical studies, then producing a response to a user's questions using generative AI. While it is in use by a number of clinicians (including residents) today, there is little to no published data on whether the tool's outputs are accurate and whether this information appropriately informs clinical decision making. Similarly, a number of clinicians are turning to other large language models (LLM's) to assist in decision making when providing clinical care. While there have been a number of studies published on the accuracy of these LLM's responses to medical boards questions or clinical vignettes, there have been few studies to date examining their performance in a real world clinical setting, and even fewer comparing this performance. In this study, investigators have two goals: 1. To determine whether the use of the AI tool "OpenEvidence" leads to clinically appropriate decisions when utilized by family medicine, internal medicine, and psychiatry residents in the course of clinical practice. 2. To determine how the output of the OpenEvidence tool compares with three other commonly-used, publicly-available large language models (OpenAI's ChatGPT, Anthropic's Claude, and Google's Gemini) in answering common questions that residents have in the course of clinical practice. To accomplish study goal #1, investigators have enlisted residents in the above specialties to use the OpenEvidence tool in the course of clinical practice. In order to mitigate any safety risks, the residents will also use a typical reference tool for their question, which is referred to as the "Gold Standard" tool. These tools include PubMed and UpToDate. The residents will: 1. State their clinical question. 2. Query OpenEvidence, capturing their prompt and the OpenEvidence output for data analysis. All residents will undergo training in prompt engineering at the start of the study. 3. State their clinical conclusion based on the OpenEvidence data. 4. Query the Gold Standard Resource. 5. State their final clinical conclusion. 6. Answer a question on whether their clinical conclusion was modified by the Gold Standard reference. 7. Answer a question on whether they had any clinical safety concerns on the output from OpenEvidence. Attending physician Subject Matter Experts (SMEs) matched by specialty with at least 5 years of post-training clinical experience will then evaluate the residents' responses. 5 years was chosen based the book "Outliers" by Malcolm Gladwell, in which he asserts that 10,000 hours of focused practice is needed to achieve expertise in a field. SMEs will be asked to evaluate the residents' initial clinical questions and their conclusions based only on OpenEvidence. They will be asked to rate the clinical appropriateness of those conclusions on a scale of 1-10. For questions where the SME's rate the clinical appropriateness of the residents' conclusions poorly (\< 5/10), they will be asked to review the OpenEvidence output and answer an additional question as to whether the output was incorrect or the resident misinterpreted the output from the tool. To accomplish goal #2, the initial prompt entered by the residents into OpenEvidence will be copied by the research team into ChatGPT, Gemini, and Claude. The outputs from each tool (including OpenEvidence) will be surfaced to SMEs, who will be asked to rate each output based on accuracy, completeness, and bias. Likert scales will be used for these ratings. SMEs will also be asked an open-ended question to identify any patient safety issues from any of the outputs.

A Prospective Validation Study of Radiomics in the Differential Diagnosis of Uterine Leiomyoma and Uterine Sarcoma

In our previous study, based on the multi-center clinical big data collected from January 2012 to January 2025, we have completed the construction of a multimodal early warning model for the malignant transformation of uterine fibroids. The model was mainly based on T2WI and DWI sequences, and was trained and optimized by support vector machine (SVM) algorithm. In the retrospective study and internal validation, the model shows high sensitivity and specificity, which preliminarily proves that it has good application potential in identifying high-risk groups and predicting the risk of malignant transformation of uterine fibroids. However, there are still some limitations in retrospective studies and internal validation results, and its application value, universality and stability in real clinical environment have not been fully verified. Therefore, we plan to conduct a prospective validation study in consecutive patients enrolled after January 2025 to evaluate the clinical performance and generalization of the model in predicting the malignant tendency or risk of malignant transformation of uterine fibroids through practical application in the real population, and further analyze the operability in the actual diagnosis and treatment process and the potential value for patient management. This study will provide reliable evidence for early screening, follow-up management and individualized treatment of high-risk population, and has important clinical and public health significance for improving the early diagnosis rate, reducing the risk of malignant transformation and improving the prognosis of patients with uterine fibroids.

The Effect of Artificial Intelligence-Supported Intramuscular and Subcutaneous Injection Training on Nursing Students

The complexity of healthcare services and technological advances today have necessitated the adoption of innovative approaches in nursing education. Among these innovative approaches, artificial intelligence (AI) has established itself as a technology that is increasingly present in nursing education processes, offering a supportive, personalized, and interactive learning experience. AI's contributions to nursing students' acquisition of fundamental competencies such as clinical decision-making, skill development, and critical thinking are rapidly increasing. Especially in high-risk, invasive, and clinically skill-intensive applications, AI-supported educational models both enhance learning quality and support patient safety. Intramuscular and subcutaneous injections are among the basic invasive skills that nursing students must learn. These applications require a high level of cognitive and psychomotor competence from students. Incorrect injection practices can lead to complications such as drug absorption problems, nerve damage, hematoma, or infection, making it critically important to teach these skills correctly and safely. In this context, AI-supported education systems stand out as an effective tool for teaching injection skills. Artificial intelligence-based chatbots provide students with both theoretical knowledge and practical guidance. For example, before injecting a muscle group, a student can learn about the anatomy of the muscle, determine the correct angle, and remember precautions against potential complications through the chatbot. Artificial intelligence also reinforces the learning process by instantly answering students' questions, preventing the acquisition of incorrect information. Recent studies emphasize that AI-supported learning tools positively influence students' attitudes toward learning, increasing their motivation and academic satisfaction levels. Accordingly, the integration of AI-based technologies in the process of training future nurses is no longer an option but a necessity. Particularly in complex and delicate skills such as intramuscular and subcutaneous injections, AI-supported chatbots can facilitate student learning, increase skill accuracy, and support clinical safety. Therefore, it is crucial for nursing education programs to combine artificial intelligence technologies with pedagogical foundations to provide student-centered, safe, and effective learning environments.

AI-Based Self-Supervised Learning Model Using Non-Contrast Breast MRI for Early Screening and Clinical Utility Evaluation

Breast cancer is the most common malignant disease among women worldwide, with rising incidence and younger age at onset in China. Early detection is critical for improving survival, yet current screening methods such as mammography and ultrasound show limited sensitivity in Chinese women, particularly those with dense breast tissue. Contrast-enhanced MRI offers higher diagnostic performance but its use is limited by high costs, safety concerns with gadolinium-based contrast agents, and limited accessibility. This investigator-initiated trial aims to evaluate the clinical application of non-contrast multiparametric MRI, combined with advanced artificial intelligence algorithms, for the early detection and diagnosis of breast cancer. The study will collect MRI imaging data from multiple centers and integrate radiomic features across T2-weighted imaging, diffusion-weighted imaging, and apparent diffusion coefficient maps. A deep learning-based model will be developed and validated to improve lesion detection, differential diagnosis, and risk stratification. The ultimate goal of this project is to establish a safe, accurate, and scalable breast cancer screening pathway suitable for Chinese women. By reducing dependence on invasive procedures and contrast agents, and by leveraging AI for standardization and efficiency, this approach may significantly improve early detection rates and contribute to better patient outcomes.

An Explainable Neuroradiologist Artificial Intelligence Assistance System for Brain CT and MRI

This clinic trial aims to validate the working performance of radiologists with or without artificial intelligence (AI) diagnostic tool at neurological diseases diagnosis on brain CT/MRI. Routine diagnosis workflow in real clinical scenario including imaging reading, feature interpretation, differential diagnosis, writing initial report and optimizing revised version. And the gold standards of diagnosis are the histopathology references for brain tumors and the discharge diagnosis integrating all the examination results for the other neurological diseases. The performance of AI-assisted tools on diagnosing should be examined in a clinical process with multiple aspects identical to human radiologists' work before being transformed and putted to use. This study hypothesizes that AI models, trained with over 100,000 patient scans, are non-inferior to radiologists in neurological disease diagnosis on brain CT and MRI. For the secondary end-points, we investigate the performance of AI-radiologist collaboration of reasoning-enhanced AI-assisted systems. We hypothesize that, by visualizing the process of imaging interpretation and diagnosis, reasoning-enhanced AI can not only improve working performance of radiologists but also boost their trust in AI tools.

Predictive Performance of a Generative Model for Corneal Tomography After ICL Implantation

To evaluate the efficacy of a corneal tomography Imaging model in predicting postoperative vault based on preoperative corneal topography in Implantable Collamer Lens (ICL) surgery.

Stepped-Wedge Cluster Randomized Trial of AI-Assisted CTA Detection for Intracranial Aneurysms in Regional Hospitals

This study (IDEAL 2) is a nationwide stepped-wedge cluster-randomized trial designed to prospectively enroll over 14,400 patients undergoing outpatient head CT angiography (CTA). The trial will be conducted across more than 72 regional hospitals in China. Clusters were randomly assigned to nine randomization groups. In accordance with the stepped-wedge design, clusters will sequentially transition from the control condition (standard human diagnosis) to the intervention condition (AI-assisted diagnosis) at regular intervals over a 10-month period, until all clusters receive the intervention. The primary outcome is the detection rate of intracranial aneurysms. Secondary outcomes include patient prognosis and clinical outcomes.

Radiomics-Based Non-Invasive MRI Differentiation of Uterine Sarcomas and Fibroids

This retrospective case-control study aims to develop and validate a diagnostic model based on multimodal big data and artificial intelligence to differentiate uterine leiomyoma from uterine sarcoma. Investigators will extract historical case data from existing inpatient and outpatient records, including medical history, physical and gynecological examination findings, MRI imaging data, laboratory results, and pathological records. The study seeks to address the question of whether integrating diverse retrospective clinical data with advanced AI techniques can accurately classify uterine tumors as benign leiomyomas or malignant sarcomas, thereby supporting clinical decision-making and optimizing diagnostic workflows.

Multimodal Large Model-Driven Risk and Prognosis Assessment for Brain Metastases in Lung Cancer

The goal of this nationwide, multicenter observational study is to develop and externally validate multimodal large models that can (1) predict the risk of brain metastases and (2) estimate long-term prognosis in patients with non-small cell lung cancer (NSCLC). The main questions it aims to answer are: * Can a multimodal large model that fuses imaging, pathology, genomic, and clinical data accurately identify NSCLC patients at high risk of developing brain metastases? * Can a multimodal large model reliably forecast intracranial progression-free survival, progression-free survival, and overall survival across diverse real-world treatment settings? (ie, patients receiving distinct treatment regimens, in different treatment lines and with or without intracranial local therapies). Because this is an observational study, there are no investigational treatments; instead, researchers will compare outcomes among patients who receive standard-of-care therapies (surgery, radiotherapy, systemic therapy) to determine how well the model's predictions align with observed events. Participants will: * Allow use of their routinely collected clinical information, imaging (chest CT, brain MRI), pathology slides, and molecular test results for model training and validation * Undergo standard-of-care follow-ups * Complete optional quality-of-life questionnaires during scheduled visits

Knowledge, Perception, Usage And Concerns Of Artificial Intelligence Applications In Periodontology

Statement of problem: knowledge gap about knowledge, perception ,usage and concerns of artificial intelligence applications in periodontology among periodontists. Aim of the study: To investigate ' periodontists' knowledge, perception , usage and concerns towards AI systems' applications in periodontology. Materials and Methods This will be done by a self-administered, 33-item questionnaire . The questionnaire is divided into five sections.The first section, known as Part A, focus on five open-ended questions on sociodemographic characteristics, where participants enter their age, gender, academic affiliation. Part B consists of closed-ended questions, identifying the basic knowledge of the periodontists participating in AI using a Likert three-point scale (yes / no / maybe) . Part C consists of questions assessing the perception of periodontists towards the use of AI using a Likert three-point scale (yes / no / maybe). Part D consists of questions focusing on the usage of AI applications . part E consists of questions assessing concerns of AI applications in periodontology using a Likert three-point scale (yes / no / maybe) . This study will be conducted in accordance with the code of ethics of the research ethics committee at the faculty of dentistry, at Ain Shams University. This survey aims to assess the knowledge, perception , usage and concerns of AI applications among periodontists. The questionnaire will be distributed to periodontologists in the faculty of dentistry at Ain Shams University. Participants will be voluntary and anonymous. The questionnaire consists of five parts and the average time to complete the questionnaire is 10-12 min

DOLCE: Determining the Impact of Optellum's Lung Cancer Prediction Solution

This study is a multi-centre prospective observational cohort study recruiting patients with 5-30mm solid and part-solid pulmonary nodules that have been detected on CT chest scans performed as part of routine practice. The aim is to determine whether physician decision making with the AI-based LCP tool, generates clinical and health-economic benefits over the current standard of care of these patients.

AI-driven Personalized Exercise Feedback Program on Exercise Adherence in Traumatic Brain Injury

This study aims to develop and evaluate an AI-driven Personalized Exercise Feedback Program (AI-PEF) to enhance exercise adherence and health outcomes in mTBI patients. Methods: AI-PEF integrates the transtheoretical model and self-determination theory with machine learning algorithms to provide real-time, personalized feedback. A phased randomized controlled trial will be conducted: Phase I evaluates feasibility and acceptability through Delphi methods with expert consensus and patient feedback; Phase II validates preliminary outcomes with 30 participants in a 2-arm randomized trial; and Phase III assesses the program's impact on adherence, sleep quality, depressive symptoms, and quality of life with 90 participants in a 3-arm randomized trial.

Remote Breast Cancer Screening Study

A multi-center, prospective, cohort study to evaluate the efficiency of breast cancer screening based on Automated Breast Ultrasound (AB US) with remote reading mode.