[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"conditionNormalized\":\"artificial-intelligence\",\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:artificial-intelligence":30},{"pageToken":4,"total":5,"offset":6,"count":7,"results":8},null,60,0,25,[9,44,70,96,122,144,174,199,235,263,289,317,336,359,391,419,456,479,505,526,549,577,600,628,649],{"id":10,"slug":11,"hasResults":12,"nctId":13,"briefTitle":14,"officialTitle":14,"acronym":15,"eligibilityCriteria":16,"healthyVolunteers":12,"sex":17,"minAge":18,"maxAge":19,"enrollmentInfo":20,"targetDuration":4,"studyType":22,"phases":23,"briefSummary":25,"conditions":26,"keywords":4,"overallStatus":31,"whyStopped":4,"lastUpdateSubmitDate":32,"lastUpdatePostDateStruct":33,"startDateStruct":36,"completionDateStruct":38,"leadSponsor":40,"locationsCount":43},"100553930","phase-2-glioma-adaptive-radiotherapy-with-development-of-an-artificial-intelligence-workflow-100553930",false,"NCT06492486","Glioma Adaptive Radiotherapy With Development of an Artificial Intelligence Workflow","GLADIATOR","Inclusion Criteria:\n\n* Histological diagnosis of diffuse glioma. Patients with IDH-negative GBM (stratum A) and IDH-mutant glioma (astrocytoma or oligodendroglioma) need radiotherapy (stratum B).\n\nAge: 18-70 years. Karnofsky Performance Scale (KPS) ≥60\n\nExclusion Criteria:\n\n* Multifocal or multicentric disease Not eligible for radical intent radiation. IDH status is unknown or uninterpretable (IHC or gene sequencing). Use of prior radiotherapy to the head-neck region or brain or chemotherapy. Contraindication\u002Funable to undergo MRI or PET scan during radiation.","ALL","18 Years","70 Years",{"count":5,"type":21},"ESTIMATED","INTERVENTIONAL",[24],"PHASE2","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.\n\nModifications 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.\n\nThe 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.",[27,28,29,30],"Diffuse Glioma","Glioblastoma","Adaptive Radiotherapy","Artificial Intelligence","RECRUITING","2026-08-13",{"date":34,"type":35},"2026-08-17","ACTUAL",{"date":37,"type":35},"2026-07-27",{"date":39,"type":21},"2028-07-30",{"name":41,"class":42},"Tata Memorial Centre","OTHER",1,{"id":45,"slug":46,"hasResults":12,"nctId":47,"briefTitle":48,"officialTitle":48,"acronym":4,"eligibilityCriteria":49,"healthyVolunteers":50,"sex":17,"minAge":4,"maxAge":4,"enrollmentInfo":51,"targetDuration":4,"studyType":53,"phases":4,"briefSummary":54,"conditions":55,"keywords":58,"overallStatus":31,"whyStopped":4,"lastUpdateSubmitDate":60,"lastUpdatePostDateStruct":61,"startDateStruct":63,"completionDateStruct":65,"leadSponsor":67,"locationsCount":69},"100504406","assisting-pulmonary-disease-diagnosis-with-ophthalmic-artificial-intelligence-technology-100504406","NCT05847894","Assisting Pulmonary Disease Diagnosis With Ophthalmic Artificial Intelligence Technology","Inclusion Criteria:\n\n* Those aged ≥18 years; or those aged \\\u003C18 years who can cooperate with the relevant examination and are accompanied and informed by a guardian;\n* People with respiratory-related diseases who were to undergo pulmonary examination, or those who volunteered to participate in the trial through publicity recruitment;\n* expected survival time of 3 months or more;\n* Those with no previous serious underlying disease and no history of serious eye disease;\n* Those who can cooperate with ophthalmologic and pulmonary-related examinations and have regular follow-up examinations;\n* Those who gave informed consent to the study prior to the trial and voluntarily signed the informed consent form;\n* Other conditions that can be included in the study as judged by the investigator.\n\nExclusion Criteria:\n\n* Patients who are unable to complete ophthalmology or pulmonary-related examinations and regular follow-ups due to serious diseases, trauma or surgery (serious ophthalmology diseases such as extremely poor vision that cannot be fixed, ocular atrophy, severe refractive interstitial clouding that prevents fundus photography, etc.);\n* People with poor compliance due to various reasons such as alcohol or drug dependence, or mental disorders;\n* Those without informed consent;\n* Other conditions judged by the investigator to be unsuitable for participation in the trial.",true,{"count":52,"type":21},10000,"OBSERVATIONAL","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.",[56,57,30],"Pulmonary Diseases","Ophthalmological Diagnostic Techniques",[56,57,59],"Artificial intelligence","2026-08-11",{"date":62,"type":35},"2026-08-12",{"date":64,"type":35},"2020-06-29",{"date":66,"type":21},"2027-05",{"name":68,"class":42},"Zhongshan Ophthalmic Center, Sun Yat-sen University",4,{"id":71,"slug":72,"hasResults":12,"nctId":73,"briefTitle":74,"officialTitle":75,"acronym":76,"eligibilityCriteria":77,"healthyVolunteers":12,"sex":78,"minAge":18,"maxAge":4,"enrollmentInfo":79,"targetDuration":4,"studyType":22,"phases":81,"briefSummary":83,"conditions":84,"keywords":4,"overallStatus":31,"whyStopped":4,"lastUpdateSubmitDate":87,"lastUpdatePostDateStruct":88,"startDateStruct":90,"completionDateStruct":92,"leadSponsor":94,"locationsCount":43},"100552170","study-on-female-patients-mammographic-texture-features-100552170","NCT06469606","Study on Female Patients' Mammographic Texture Features","A Cohort Study on feMale Patients' mammogRaphic texturE featureS: the COMPRESS Trial","COMPRESS","Inclusion Criteria:\n\n* Candidate is a biological female aged 18 years or above;\n* Candidate is willing and able to give informed consent and gives their written consent for the participation in the study;\n* There is a clinical indication for a uni- or bilateral mastectomy\n\nExclusion Criteria:\n\n* Candidate lacks the capacity to provide informed consent;\n* Candidate has breast implants","FEMALE",{"count":80,"type":21},200,[82],"NA","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.",[85,30,86],"Breast Cancer","Mammography","2026-08-04",{"date":89,"type":35},"2026-08-05",{"date":91,"type":35},"2024-06-17",{"date":93,"type":21},"2038-12-15",{"name":95,"class":42},"Tampere University Hospital",{"id":97,"slug":98,"hasResults":12,"nctId":99,"briefTitle":100,"officialTitle":101,"acronym":102,"eligibilityCriteria":103,"healthyVolunteers":12,"sex":17,"minAge":104,"maxAge":4,"enrollmentInfo":105,"targetDuration":4,"studyType":22,"phases":107,"briefSummary":108,"conditions":109,"keywords":4,"overallStatus":112,"whyStopped":4,"lastUpdateSubmitDate":113,"lastUpdatePostDateStruct":114,"startDateStruct":116,"completionDateStruct":118,"leadSponsor":120,"locationsCount":43},"100649491","artificially-intelligent-robot-control-100649491","NCT07738016","Artificially Intelligent Robot Control","Asthma Under Artificially Intelligent Robot (AIR) Control: Robotic Support for Pediatric Education","AIR","Inclusion Criteria for Caregivers or Parents:\n\n* The participant must be at least 18 years old.\n* The participant must be willing and able to participate.\n* The participant can read English or Spanish and is able to fill out survey instruments by themselves or with assistance.\n* The participant cares for a child age 4-11 with asthma.\n\nInclusion Criteria for Children:\n\n* The participant must be at least 4 years old - 11 years old with asthma.\n* The child can speak English or Spanish.\n* The participant must assent to participation.\n* The participant's guardian must have consented.\n\nExclusion Criteria for Caregivers or Parents:\n\n* The participant is younger than 18 years old.\n* The participant is unwilling to participate in the study.\n* The participant is unable to complete survey instruments.\n* The participant does not care for a child with asthma who is age 4-11.\n\nExclusion Criteria for Children:\n\n* The participant is younger than 4 years old, over 11 years old, or does not have asthma.\n* The child does not speak English or Spanish.\n* The participant is unwilling to participate in the study.\n* The participant's guardian did not consent.","4 Years",{"count":106,"type":21},80,[82],"The purpose of the study is to compare the impact of standard asthma education with the standard + Artificially Intelligent Robot (AIR) Control intervention.",[110,30,111],"Asthma in Children","Caregiver Burden","NOT_YET_RECRUITING","2026-07-30",{"date":115,"type":35},"2026-08-03",{"date":117,"type":21},"2026-09",{"date":119,"type":21},"2027-08-30",{"name":121,"class":42},"University of Miami",{"id":123,"slug":124,"hasResults":12,"nctId":125,"briefTitle":126,"officialTitle":127,"acronym":128,"eligibilityCriteria":129,"healthyVolunteers":12,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":130,"targetDuration":4,"studyType":53,"phases":4,"briefSummary":132,"conditions":133,"keywords":4,"overallStatus":31,"whyStopped":4,"lastUpdateSubmitDate":37,"lastUpdatePostDateStruct":136,"startDateStruct":138,"completionDateStruct":140,"leadSponsor":142,"locationsCount":43},"100649727","diagnostic-accuracy-of-a-deep-learning-based-software-for-automated-multiparametric-echocardiographic-measurements-100649727","NCT07738419","Diagnostic Accuracy of a Deep Learning-Based Software for Automated Multiparametric Echocardiographic Measurements","PANECHO: Diagnostic Accuracy of a Deep Learning-Based Artificial Intelligence Software Developed for Automated Multiparametric Echocardiographic Measurements From Echocardiographic Video Images: A Multicenter Study of the Italian Society of Echocardiography and Cardiovascular Imaging (SIECVI)","PANECHO","Inclusion Criteria:\n\n* Adults aged 18 years or older.\n* Undergoing clinically indicated standard transthoracic echocardiography.\n* Adequate echocardiographic image quality for automated and expert analysis.\n* Written informed consent provided prior to study participation.\n\nExclusion Criteria:\n\n* Age \\\u003C18 years.\n* Frequent and\u002For complex cardiac arrhythmias during echocardiographic examination.\n* Suboptimal echocardiographic images.",{"count":131,"type":21},1157,"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.",[134,30,135],"Echocardiography","Diagnostic Imaging",{"date":137,"type":35},"2026-07-31",{"date":139,"type":35},"2026-04-27",{"date":141,"type":21},"2027-01-31",{"name":143,"class":42},"Centro Cardiologico Monzino",{"id":145,"slug":146,"hasResults":12,"nctId":147,"briefTitle":148,"officialTitle":149,"acronym":150,"eligibilityCriteria":151,"healthyVolunteers":50,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":152,"targetDuration":154,"studyType":53,"phases":4,"briefSummary":155,"conditions":156,"keywords":160,"overallStatus":31,"whyStopped":4,"lastUpdateSubmitDate":165,"lastUpdatePostDateStruct":166,"startDateStruct":168,"completionDateStruct":170,"leadSponsor":172,"locationsCount":43},"100648320","artificial-intelligence-assisted-advanced-analysis-of-knee-imaging-and-outcome-prediction-100648320","NCT07721116","Artificial Intelligence-Assisted Advanced Analysis of Knee Imaging and Outcome Prediction","Artificial Intelligence-Assisted Advanced Analysis of Knee Imaging and Outcome Prediction for Ultrasound-Guided Injections","AI; R-CNN","Objective 1: Development of an AI-Based Normative Model for the Healthy Knee\n\nInclusion Criteria:\n\n* Clinical diagnosis of healthy adult without major systemic disease\n* Age ≥18 years\n* Able to understand and follow study instructions\n* Ambulatory without walking aids\n* No pain in either knee for at least 6 months before enrollment\n\nExclusion Criteria:\n\n* Previous knee surgery\n* Rupture of one or more cruciate ligaments\n* Knee injection within the preceding 6 months\n* Major trauma involving the knee or periarticular region\n* Rheumatic or autoimmune disease\n\nObjective 2: Development of an AI-Based Model for the Identification of Pathological Knee Structures\n\nInclusion Criteria:\n\n* Clinical diagnosis of radiographic knee osteoarthritis\n* Age ≥18 years\n* Knee pain in at least one knee during the preceding year\n* Medical records confirming knee pain, soreness, or stiffness within 1 month before enrollment\n* Radiographic evidence of knee osteoarthritis, defined by at least one of the following:\n* Kellgren-Lawrence grade ≥2 on anteroposterior radiographs\n* Kellgren-Lawrence grade ≥2 on skyline (patellofemoral) radiographs\n* Superior or inferior patellar osteophytes or posterior tibial osteophytes on lateral radiographs\n\nExclusion Criteria:\n\n* Systemic rheumatic disease (e.g., rheumatoid arthritis or ankylosing spondylitis)\n* Malignancy\n* Previous major knee trauma (including fracture)\n* Previous knee surgery\n* Intra-articular corticosteroid injection within the preceding 3 months\n\nObjective 3: Development of an AI-Assisted Predictive Model for Injection Treatment Outcomes\n\nInclusion Criteria:\n\n* Clinical diagnosis of radiographic knee osteoarthritis requiring ultrasound-guided injection therapy\n* Age ≥18 years\n* Knee pain in at least one knee during the preceding year\n* Medical records confirming knee pain, soreness, or stiffness within 1 month before enrollment\n* Radiographic evidence of knee osteoarthritis, defined by at least one of the following:\n* Kellgren-Lawrence grade ≥2 on anteroposterior radiographs\n* Kellgren-Lawrence grade ≥2 on skyline (patellofemoral) radiographs\n* Superior or inferior patellar osteophytes or posterior tibial osteophytes on lateral radiographs\n* Willingness to undergo ultrasound-guided injection therapy and complete scheduled follow-up assessments\n\nExclusion Criteria:\n\n* Systemic rheumatic disease (e.g., rheumatoid arthritis or ankylosing spondylitis)\n* Malignancy\n* Previous major knee trauma (including fracture)\n* Previous knee surgery\n* Intra-articular corticosteroid injection within the preceding 3 months",{"count":153,"type":21},310,"1 Day","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.\n\nThe 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.\n\nThe 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.",[157,158,159,30],"Degenerative Knee Disorders","Knee Osteoarthritis","Musculoskeletal Ultrasonography",[161,162,163,164],"artificial intelligence","ultrasonography","knee","ultrasound-guided injections","2026-07-19",{"date":167,"type":35},"2026-07-22",{"date":169,"type":21},"2026-07-01",{"date":171,"type":21},"2029-12-31",{"name":173,"class":42},"National Taiwan University Hospital",{"id":175,"slug":176,"hasResults":12,"nctId":177,"briefTitle":178,"officialTitle":178,"acronym":4,"eligibilityCriteria":179,"healthyVolunteers":50,"sex":17,"minAge":180,"maxAge":181,"enrollmentInfo":182,"targetDuration":4,"studyType":22,"phases":184,"briefSummary":185,"conditions":186,"keywords":190,"overallStatus":112,"whyStopped":4,"lastUpdateSubmitDate":169,"lastUpdatePostDateStruct":192,"startDateStruct":194,"completionDateStruct":195,"leadSponsor":197,"locationsCount":4},"100646797","a-practical-exploration-of-developing-a-full-english-orthopedic-nursing-curriculum-based-on-deepseek-intelligence-100646797","NCT07688499","A Practical Exploration of Developing a Full-English Orthopedic Nursing Curriculum Based on DeepSeek Intelligence","Inclusion Criteria:\n\n* ① On-duty nurses in the orthopedics department ② Obtained the nurse professional qualification certificate ③ Obtained the patient's informed consent and signed the informed consent form ④ The research was approved by the ethics committee of this hospital.\n\nExclusion Criteria:\n\n* Nurses who have not completed 1\u002F2 of the course content","20 Years","40 Years",{"count":183,"type":21},20,[82],"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:\n\nWill the DeepSeek-based full English course improve orthopedic nurses' professional English test scores?\n\nWill nurses' transcultural nursing self-efficacy and nurse-patient therapeutic interaction ability improve after the course training?\n\nWhat are nurses' experiences when using the DeepSeek AI platform for learning?\n\nResearchers will compare the English proficiency changes of the same group of orthopedic nurses before and after the training to observe the course effects.\n\nParticipants will:\n\nAttend an 11-week full English course based on the DeepSeek platform, approximately 1-2 hours per week\n\nComplete a professional English written test and an oral proficiency assessment before and after the training\n\nComplete the Transcultural Self-Efficacy Tool (TSET-CV) and the Nurse-Patient Therapeutic Interaction Scale (NuPTIS) before and after the training\n\nComplete a teaching satisfaction and AI learning experience questionnaire after the course",[187,188,30,189],"Orthopedic Nursing","Education, Nursing","Language",[191],"DeepSeek; artificial intelligence; orthopedic nursing; full English course; nursing English teaching; transcultural nursing; nursing education",{"date":193,"type":35},"2026-07-07",{"date":169,"type":21},{"date":196,"type":21},"2027-07-01",{"name":198,"class":42},"The Fourth Affiliated Hospital of Zhejiang University School of Medicine",{"id":200,"slug":201,"hasResults":12,"nctId":202,"briefTitle":203,"officialTitle":204,"acronym":205,"eligibilityCriteria":206,"healthyVolunteers":50,"sex":17,"minAge":4,"maxAge":4,"enrollmentInfo":207,"targetDuration":4,"studyType":22,"phases":208,"briefSummary":209,"conditions":210,"keywords":214,"overallStatus":112,"whyStopped":4,"lastUpdateSubmitDate":225,"lastUpdatePostDateStruct":226,"startDateStruct":228,"completionDateStruct":230,"leadSponsor":232,"locationsCount":4},"100641792","ambient-audio-visual-capture-for-clinical-documentation-and-assessment-100641792","NCT07649772","Ambient Audio-Visual Capture for Clinical Documentation and Assessment","Ambient Audio-Visual Capture for Clinical Documentation, Assessment and Feedback in Medical Education","BLACKFRAME-AV-","Inclusion\n\nTrainee participants:\n\n* Doctor in training (FY1 through registrar\u002FST grade) undertaking a supervised clinical activity at a participating NHS study site\n* Able to provide written informed consent in English\n\nPatient participants:\n\n* Adult inpatient aged 18 years or over\n* Able to provide written informed consent in English\n* Admitted under a surgical team at a participating study site\n* Clinically stable at the time of approach\n\nExclusion\n\nTrainee participants:\n\n* Unwilling to be audio-visually recorded\n* Unable to provide written informed consent\n* Any trainee where participation could create a direct conflict with a concurrent formal assessment or appraisal process at that session\n\nPatient participants:\n\n* Age under 18 years\n* Unable to provide informed consent (including temporary incapacity due to acute illness, sedation, or delirium)\n* Acute clinical deterioration at the time of approach\n* Encounter involves sensitive disclosures in mental health, sexual health, or safeguarding unless a specific sub-protocol with additional consent measures is in place\n* Patient has previously declined participation and does not wish to be re-approached\n* Non-English speaking patients where no appropriate interpreter is available to support the consent process",{"count":5,"type":21},[82],"AI-powered tools that automatically document clinical conversations are being adopted rapidly in outpatient settings but have not been evaluated in hospital wards. Existing tools use audio recording only, which cannot capture physical examination findings, procedural observations, or clinical safety behaviours - elements of a ward round that are visible but not audible.\n\nThis study evaluates an ambient audio-visual (AV) capture system - BlackFrame - that uses both microphone and camera to generate accurate clinical documentation and structured educational feedback in a real inpatient surgical ward setting.\n\nMedical students and doctors in training participate in supervised ward round encounters with consenting adult inpatients. The BlackFrame AI platform generates: (a) a structured draft clinical note for the supervising clinician to review and countersign before any use in the patient record; and (b) formative feedback for the trainee, delivered within 30 minutes, covering clinical communication, examination technique, and documentation quality.\n\nThe study measures whether AI-generated feedback improves trainee clinical performance over a placement, how much documentation time is saved, and whether the system is acceptable to patients and clinicians. No AI-generated text enters the patient record without explicit clinician review and sign-off. All participation is voluntary.",[211,30,212,213],"Clinical Documentation","Surgical Education","Medical Education",[215,216,217,218,219,220,221,222,223,224],"ambient scribe","audio-visual capture","ward round","formative feedback","clinical assessment","AI documentation","inpatient","software as a medical device","inter-rater reliability","trainee assessment","2026-06-10",{"date":227,"type":35},"2026-06-16",{"date":229,"type":21},"2026-09-01",{"date":231,"type":21},"2026-11-30",{"name":233,"class":234},"BlackFrame.ai","INDUSTRY",{"id":236,"slug":237,"hasResults":12,"nctId":238,"briefTitle":239,"officialTitle":240,"acronym":4,"eligibilityCriteria":241,"healthyVolunteers":50,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":242,"targetDuration":4,"studyType":22,"phases":244,"briefSummary":245,"conditions":246,"keywords":253,"overallStatus":112,"whyStopped":4,"lastUpdateSubmitDate":255,"lastUpdatePostDateStruct":256,"startDateStruct":258,"completionDateStruct":259,"leadSponsor":261,"locationsCount":43},"100639995","the-effect-of-ai-assisted-nursing-process-training-on-nursing-process-competence-perception-and-attitudes-towards-artificial-intelligence-in-nurses-a-randomized-controlled-study-100639995","NCT07618975","The Effect of AI-Assisted Nursing Process Training on Nursing Process Competence, Perception and Attitudes Towards Artificial Intelligence in Nurses: A Randomized Controlled Study","Hemşirelerde Yapay Zeka Destekli Hemşirelik Süreci Eğitiminin Hemşirelik Süreci Yetkinliğine, Yapay Zeka Algı ve Tutumuna Etkisi: Randomize Kontrollü Bir Çalışma","Inclusion Criteria:\n\n* Volunteering to participate in the study.\n* Working actively as a nurse in the specified institution (Yalova Training and Research Hospital).\n* Not having previously used artificial intelligence in the nursing process.\n\nExclusion Criteria:\n\n* Refusing to participate in the study.\n* Having previously used artificial intelligence in the nursing process. Submitting incomplete data collection forms.\n* Requesting to withdraw from the study.",{"count":243,"type":21},78,[82],"This study aims to determine how applied artificial intelligence (AI) training affects nurses' ability to manage the nursing process and their perceptions and attitudes toward AI technology\n\n* The nursing process is a scientific, six-stage approach used by nurses to identify patient needs and provide holistic care\n\nThe research is a randomized controlled trial involving 78 nurses at Yalova Education and Research Hospital\n\n. Participants will be split into two groups: Both groups will receive standard theoretical training on the nursing process\n\n. The intervention group will receive additional specialized training on using AI tools (such as ChatGPT and Deepseek) to help create nursing care plans through practical case studies\n\n. Nurses' skills and views will be measured using specific scales before the training and one month after the intervention to evaluate the training's effectiveness\n\n* This study is expected to provide valuable insights into how AI can support clinical decision-making and help healthcare providers adapt to new technologies\n* The research has been approved by the Yalova University Ethics Committee (Protocol 2026\u002F183) and will be conducted between May and December 2026",[247,248,249,250,30,251,252],"Nursing Process Competence","Artificial Intelligence Perception and Attitude","Nursing Education","Nursing Process","Clinical Competence","Artificial Intelligence (AI)",[30,250,249,251,254],"Attitude of Health Personnel","2026-05-24",{"date":257,"type":35},"2026-06-01",{"date":257,"type":21},{"date":260,"type":21},"2026-12-31",{"name":262,"class":42},"University of Yalova",{"id":264,"slug":265,"hasResults":12,"nctId":266,"briefTitle":267,"officialTitle":268,"acronym":269,"eligibilityCriteria":270,"healthyVolunteers":12,"sex":78,"minAge":18,"maxAge":271,"enrollmentInfo":272,"targetDuration":4,"studyType":22,"phases":274,"briefSummary":275,"conditions":276,"keywords":4,"overallStatus":31,"whyStopped":4,"lastUpdateSubmitDate":279,"lastUpdatePostDateStruct":280,"startDateStruct":282,"completionDateStruct":284,"leadSponsor":286,"locationsCount":288},"100632548","ai-top-study-artificial-intelligence-for-trigger-optimization-100632548","NCT07515118","AI-TOP Study Artificial Intelligence for Trigger Optimization.","An Artificial Intelligence Based Approach for Selecting the Optimal Day for Triggering.","AI-TOP","Inclusion Criteria:\n\n* Undergoing COS for IVF with autologous oocytes, oocyte donation and elective fertility preservation with all monitoring USS (ultrasound scan) conducted at our centers.\n\nExclusion Criteria:\n\n* Medically indicated fertility preservation\n* Inability to attend clinic visits for monitoring.","42 Years",{"count":273,"type":21},644,[82],"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.",[277,278,30],"Infertility","Ovarian Stimulation","2026-04-21",{"date":281,"type":35},"2026-04-22",{"date":283,"type":35},"2026-04-08",{"date":285,"type":21},"2027-09",{"name":287,"class":42},"Fundacion Dexeus",5,{"id":290,"slug":291,"hasResults":12,"nctId":292,"briefTitle":293,"officialTitle":293,"acronym":294,"eligibilityCriteria":295,"healthyVolunteers":12,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":296,"targetDuration":4,"studyType":22,"phases":298,"briefSummary":299,"conditions":300,"keywords":303,"overallStatus":112,"whyStopped":4,"lastUpdateSubmitDate":309,"lastUpdatePostDateStruct":310,"startDateStruct":312,"completionDateStruct":314,"leadSponsor":316,"locationsCount":43},"100634467","ai-assisted-workflow-for-occult-atrial-fibrillation-detection-after-ischemic-stroke-a-prospective-randomized-trial-100634467","NCT07540065","AI-Assisted Workflow for Occult Atrial Fibrillation Detection After Ischemic Stroke: A Prospective Randomized Trial","AI-AFIS","Inclusion Criteria:\n\n1. 18 years or older who are acutely hospitalized due to ischemic stroke;\n2. A 12-lead electrocardiogram upon admission showing sinus rhythm;\n\nExclusion Criteria:\n\n1. Previous diagnosis of atrial fibrillation or flutter;\n2. Diagnosis of atrial fibrillation during hospitalization;\n3. Pre-existing cardiac implantable electronic device (IVD);\n4. Expected inability to attend regular outpatient follow-ups after discharge, or to undergo multiple 14-day ECG recordings;\n5. Requires long-term anticoagulant use for other reasons, including but not limited to chronic pulmonary embolism;\n6. Known contraindications to non-vitamin K antagonist oral anticoagulants due to comorbidities, including but not limited to end-stage renal disease, severe mitral stenosis due to rheumatic heart disease, or metallic heart valves;\n7. Unwilling to sign a participant consent form.",{"count":297,"type":21},400,[82],"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.",[301,302,30],"Atrial Fibrillation","Stroke",[304,305,161,306,307,308],"stroke","atrial fibrillation","risk prediction","electrocardiogram","non-vitamin K antagonist oral anticoagulants","2026-04-16",{"date":311,"type":35},"2026-04-20",{"date":313,"type":21},"2026-10-01",{"date":315,"type":21},"2028-12-31",{"name":173,"class":42},{"id":318,"slug":319,"hasResults":12,"nctId":320,"briefTitle":321,"officialTitle":322,"acronym":4,"eligibilityCriteria":323,"healthyVolunteers":12,"sex":17,"minAge":4,"maxAge":4,"enrollmentInfo":324,"targetDuration":4,"studyType":53,"phases":4,"briefSummary":325,"conditions":326,"keywords":4,"overallStatus":112,"whyStopped":4,"lastUpdateSubmitDate":328,"lastUpdatePostDateStruct":329,"startDateStruct":331,"completionDateStruct":332,"leadSponsor":334,"locationsCount":4},"100630895","ai-based-informational-assistant-for-automated-point-of-care-documentation-and-protocol-retrieval-100630895","NCT07493616","AI-based Informational Assistant for Automated Point-of-care Documentation and Protocol Retrieval","Evaluation of an AI-based Informational Assistant for Automated Point-of-care Documentation and Protocol Retrieval in the Intensive Care Unit","Inclusion Criteria:\n\n* ICU physician (nurse practicioner, resident, or staff intensivist) at the Erasmus MC.\n* Signed informed-consent for study participation.\n\nExclusion Criteria:\n\n\\- Physicians not expected to work on the ICU during the study period will not be approached.",{"count":7,"type":21},"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.\n\nThe 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\u002Foverview using predefined formatting, and (3) retrieves relevant passages from protocols to enable rapid consistency checks by the clinician.\n\nThe 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.",[30,327],"Usability","2026-03-23",{"date":330,"type":35},"2026-03-25",{"date":169,"type":21},{"date":333,"type":21},"2026-12-01",{"name":335,"class":42},"Willemijn Berkhout",{"id":337,"slug":338,"hasResults":12,"nctId":339,"briefTitle":340,"officialTitle":341,"acronym":342,"eligibilityCriteria":343,"healthyVolunteers":12,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":344,"targetDuration":4,"studyType":53,"phases":4,"briefSummary":346,"conditions":347,"keywords":350,"overallStatus":31,"whyStopped":4,"lastUpdateSubmitDate":351,"lastUpdatePostDateStruct":352,"startDateStruct":354,"completionDateStruct":356,"leadSponsor":357,"locationsCount":43},"100476310","adverse-outcome-of-acute-pulmonary-embolism-by-artificial-intelligence-system-based-on-ct-pulmonary-angiography-100476310","NCT05482269","Adverse Outcome of Acute Pulmonary Embolism by Artificial Intelligence System Based on CT Pulmonary Angiography","Prediction of Adverse Outcome of Acute Pulmonary Embolism by Artificial Intelligence System Based on CT Pulmonary Angiography","PEAICTPA","Inclusion Criteria:\n\n* age of ≥ 18 years and a pulmonary embolism diagnosis based on CT pulmonary angiography\n\nExclusion Criteria:\n\n* pregnancy\n* reception of reperfusion treatment before admission\n* missing data regarding CT parameters, echocardiography, cardiac troponin I (c-Tn I), and N-terminal-pro brain natriuretic peptide (NT-pro BNP) levels.",{"count":345,"type":21},2000,"The investigators aim to build a predictive tool for Adverse Outcome of Acute Pulmonary Embolism by Artificial Intelligence System Based on CT Pulmonary Angiography.",[348,349,30],"Pulmonary Embolism and Thrombosis","Deterioration, Clinical",[348,349,30],"2026-03-08",{"date":353,"type":35},"2026-03-11",{"date":355,"type":35},"2011-01-01",{"date":260,"type":21},{"name":358,"class":42},"Shengjing Hospital",{"id":360,"slug":361,"hasResults":12,"nctId":362,"briefTitle":363,"officialTitle":364,"acronym":4,"eligibilityCriteria":365,"healthyVolunteers":12,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":366,"targetDuration":368,"studyType":53,"phases":4,"briefSummary":369,"conditions":370,"keywords":374,"overallStatus":112,"whyStopped":4,"lastUpdateSubmitDate":382,"lastUpdatePostDateStruct":383,"startDateStruct":385,"completionDateStruct":387,"leadSponsor":389,"locationsCount":4},"100623400","baixiaoai-ai-companion-for-cancer-patient-follow-up-100623400","NCT07396142","BaiXiaoAi AI Companion for Cancer Patient Follow-up","Application of BaiXiaoAi Companion AI in the Diagnosis, Treatment, and Follow-up Management of Oncology Patients: A Single-Center, Prospective, Exploratory Study","Inclusion Criteria:\n\n* Adults aged 18 years or older;\n* Patients with a confirmed diagnosis of malignant neoplasms who are currently undergoing treatment or in follow-up, or their primary caregivers;\n* Able to read and communicate in Chinese and independently use WeChat;\n* Willing and able to provide informed consent.\n\nExclusion Criteria:\n\n* Presence of severe psychiatric disorders (e.g., schizophrenia, bipolar disorder) or cognitive impairment (MMSE score \\\u003C 24);\n* Inability to use WeChat or communicate in Chinese;\n* Considered by the investigators to be unsuitable for participation due to psychological or physical conditions.",{"count":367,"type":21},300,"6 Months","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.",[371,30,372,373],"Symptoms and Signs","Artificial Intelligence Mobile Application","Needs Assessment",[375,376,377,378,379,380,381],"WeChat-based AI companion","Tumor patients,","Cancer patients","Patient satisfaction","Symptom management","Emotional support","Clinical needs assessment","2026-02-01",{"date":384,"type":35},"2026-02-09",{"date":386,"type":21},"2026-01-14",{"date":388,"type":21},"2027-11-30",{"name":390,"class":42},"Cancer Institute and Hospital, Chinese Academy of Medical Sciences",{"id":392,"slug":393,"hasResults":12,"nctId":394,"briefTitle":395,"officialTitle":396,"acronym":397,"eligibilityCriteria":398,"healthyVolunteers":50,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":399,"targetDuration":4,"studyType":22,"phases":401,"briefSummary":402,"conditions":403,"keywords":408,"overallStatus":31,"whyStopped":4,"lastUpdateSubmitDate":409,"lastUpdatePostDateStruct":410,"startDateStruct":412,"completionDateStruct":414,"leadSponsor":416,"locationsCount":418},"100558090","locally-optimised-contouring-with-ai-technology-for-radiotherapy-100558090","NCT06546592","Locally Optimised Contouring With AI Technology for Radiotherapy","LOCATOR - Locally Optimised Contouring With AI Technology for Radiotherapy","LOCATOR","Inclusion Criteria:\n\n* 18 years and older who are planned for primary breast malignancy\n* ECOG performance 0-2\n* Ability to understand and willingness to sign a written informed consent document\n* The target volume must be able to be objectively reviewed by current published national or international clinical guidelines\n\nExclusion Criteria:\n\n* Patients under 18 years of age\n* Patients unable to understand consent documents",{"count":400,"type":21},444,[82],"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.\n\nLOCATOR uses the LOCATOR software which is an in-house software developed locally and trained on local data.",[404,405,406,30,407],"Contouring","Segmentation","Radiation Therapy","Deep Learning",[404,405,406,30,407],"2026-01-27",{"date":411,"type":35},"2026-01-29",{"date":413,"type":35},"2025-02-11",{"date":415,"type":21},"2030-04-30",{"name":417,"class":42},"Royal North Shore Hospital",3,{"id":420,"slug":421,"hasResults":12,"nctId":422,"briefTitle":423,"officialTitle":423,"acronym":424,"eligibilityCriteria":425,"healthyVolunteers":12,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":426,"targetDuration":4,"studyType":22,"phases":428,"briefSummary":429,"conditions":430,"keywords":438,"overallStatus":112,"whyStopped":4,"lastUpdateSubmitDate":447,"lastUpdatePostDateStruct":448,"startDateStruct":450,"completionDateStruct":452,"leadSponsor":454,"locationsCount":43},"100616930","optimization-of-medical-time-in-the-emergency-department-impact-of-an-ai-based-system-on-prescription-entry-100616930","NCT07312019","Optimization of Medical Time in the Emergency Department: Impact of an AI-Based System on Prescription Entry","YGénHIAL","Inclusion Criteria:\n\n* Age ≥18 years\n* Admission to emergency department at a participating center\n* Polymedicated patients with prescriptions including ≥8 medication lines (including those for long-term illnesses)\n* Signed informed consent\n\nExclusion Criteria:\n\n* Patient under legal protection\u002Fjudicial measures (guardianship\u002Fcustody)\n* Lack of signed informed consent",{"count":427,"type":21},770,[82],"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.",[431,432,30,433,434,435,436,437],"Drug-related Iatrogenesis","Emergency Department","Clinical Decision Support","Prescription","Transcription","Medication","Reconciliation",[439,440,161,441,442,443,444,445,446],"Drug-related iatrogenesis","emergency department","clinical decision support","randomized trial","medication","reconciliation","prescription","transcription","2026-01-06",{"date":449,"type":35},"2026-01-08",{"date":451,"type":21},"2026-01",{"date":453,"type":21},"2027-01",{"name":455,"class":42},"Centre Hospitalier Universitaire, Amiens",{"id":457,"slug":458,"hasResults":12,"nctId":459,"briefTitle":460,"officialTitle":460,"acronym":4,"eligibilityCriteria":461,"healthyVolunteers":12,"sex":78,"minAge":18,"maxAge":462,"enrollmentInfo":463,"targetDuration":4,"studyType":53,"phases":4,"briefSummary":465,"conditions":466,"keywords":4,"overallStatus":31,"whyStopped":4,"lastUpdateSubmitDate":470,"lastUpdatePostDateStruct":471,"startDateStruct":473,"completionDateStruct":475,"leadSponsor":477,"locationsCount":43},"100616665","the-relationship-between-quality-of-life-anxiety-levels-and-attitudes-toward-artificial-intelligence-among-women-undergoing-infertility-treatment-100616665","NCT07308561","The Relationship Between Quality of Life, Anxiety Levels, and Attitudes Toward Artificial Intelligence Among Women Undergoing Infertility Treatment","Inclusion Criteria:\n\n* Women aged 18-45 years diagnosed with infertility (primary or secondary infertility).\n* Women undergoing infertility treatment and those who have experienced various treatment modalities (IUI, IVF, ICSI).\n* Women who voluntarily agree to participate in the study.\n* Women who are able to understand and speak Turkish.\n\nExclusion Criteria:\n\n* Women with diagnosed psychological disorders (e.g., clinical depression, anxiety disorders).\n* Women who are not undergoing infertility treatment.","45 Years",{"count":464,"type":21},191,"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.",[467,30,468,469],"Infertility, Female","Quality of Life","Anxiety","2025-12-17",{"date":472,"type":35},"2025-12-29",{"date":474,"type":35},"2025-10-23",{"date":476,"type":21},"2026-10-23",{"name":478,"class":42},"Acibadem University",{"id":480,"slug":481,"hasResults":12,"nctId":482,"briefTitle":483,"officialTitle":484,"acronym":4,"eligibilityCriteria":485,"healthyVolunteers":12,"sex":17,"minAge":18,"maxAge":19,"enrollmentInfo":486,"targetDuration":4,"studyType":22,"phases":488,"briefSummary":489,"conditions":490,"keywords":493,"overallStatus":112,"whyStopped":4,"lastUpdateSubmitDate":497,"lastUpdatePostDateStruct":498,"startDateStruct":500,"completionDateStruct":501,"leadSponsor":503,"locationsCount":4},"100613999","real-time-feedback-of-red-out-within-colonoscopy-intubation-100613999","NCT07273890","Real-time Feedback of Red-out Within Colonoscopy Intubation","Prospective, Multicenter, Controlled Study on the Impact of Real-time Feedback on Red-out","Inclusion Criteria:\n\n1. Study Participants (Patients):\n\n   Aged 18 to 70 years, any gender. Individuals scheduled to undergo diagnostic or screening colonoscopy at the investigational site.\n2. Colonoscopists:\n\nExpert-level colonoscopists (having performed a total of \\>1000 colonoscopy procedures).\n\nRight-handed.\n\nExclusion Criteria:\n\n1. Study Participants (Patients):\n\n   Individuals undergoing the following procedures:\n\n   cases with a history of colorectal surgery; cases with a history of chemotherapy, raditherapy; cases with a history of abdominal, and\u002For pelvic surgery; cases with a history of difficult colonoscopies; cases with colorectal tumours and obstructive lesions; cases with colorectal diverticula; cases with ulcerative colitis or Crohn's disease; cases with ischemic bowel disease; cases with colorectal polyposis; cases with melanosis coli; cases undergoing sigmoidoscopy; cases with poor intestional cleanliness (segment Boston bowel preparation scale (BBPS) of \\\u003C 2 points, total BBPS of \\\u003C 6 points); cases undergoing therapy procedures such as biopsy or CSP during the intubation phase; cases with transparent cap assisted colonoscopy; cases with water-assisted colonoscopy; cases with air insufflation level of M or L; cases failed caecal intubation within 15 min; cases with colonoscope stiffness level \\> 0; obese cases or underweight cases; and cases refusing participation.\n\n   Individuals who decline to provide informed consent.\n2. Colonoscopists:\n\nThose who have performed fewer than 300 complete colonoscopies in any calendar year within the past three years.\n\nThose who decline to participate in the study.",{"count":487,"type":21},576,[82],"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.\n\nThe 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.",[30,491,492],"Colonoscopy","Real-time Feedback",[491,494,495,496],"Intubation","Real-time feedback","Red-out","2025-11-27",{"date":499,"type":35},"2025-12-10",{"date":499,"type":21},{"date":502,"type":21},"2028-04-20",{"name":504,"class":42},"The First Affiliated Hospital of Anhui Medical University",{"id":506,"slug":507,"hasResults":12,"nctId":508,"briefTitle":509,"officialTitle":509,"acronym":4,"eligibilityCriteria":510,"healthyVolunteers":12,"sex":17,"minAge":181,"maxAge":511,"enrollmentInfo":512,"targetDuration":4,"studyType":53,"phases":4,"briefSummary":514,"conditions":515,"keywords":4,"overallStatus":31,"whyStopped":4,"lastUpdateSubmitDate":497,"lastUpdatePostDateStruct":518,"startDateStruct":520,"completionDateStruct":522,"leadSponsor":524,"locationsCount":418},"100612762","artificial-intelligence-in-assessing-gastric-intestinal-metaplasia-via-the-eggim-score-100612762","NCT07257796","Artificial Intelligence in Assessing Gastric Intestinal Metaplasia Via the EGGIM Score","Inclusion Criteria:\n\n* patients aged 40-75 years who undergo the IEE examination\n* patients who voluntarily sign the informed consent form\n\nExclusion Criteria:\n\n* patients with severe cardiac, cerebral, pulmonary or renal dysfunction or psychiatric disorders who cannot participate in gastroscopy\n* patients with previous surgical procedures on the stomach","75 Years",{"count":513,"type":21},3000,"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.\n\nThis 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).",[516,30,517],"Intestinal Metaplasia of Gastric Mucosa","Endoscopy",{"date":519,"type":35},"2025-12-02",{"date":521,"type":35},"2025-11-01",{"date":523,"type":21},"2027-12-31",{"name":525,"class":42},"Qilu Hospital of Shandong University",{"id":527,"slug":528,"hasResults":12,"nctId":529,"briefTitle":530,"officialTitle":531,"acronym":4,"eligibilityCriteria":532,"healthyVolunteers":12,"sex":17,"minAge":181,"maxAge":4,"enrollmentInfo":533,"targetDuration":4,"studyType":22,"phases":535,"briefSummary":536,"conditions":537,"keywords":539,"overallStatus":31,"whyStopped":4,"lastUpdateSubmitDate":541,"lastUpdatePostDateStruct":542,"startDateStruct":544,"completionDateStruct":546,"leadSponsor":547,"locationsCount":43},"100554173","miss-rate-of-gastric-neoplasms-under-computer-aided-endoscopy-100554173","NCT06495645","Miss Rate of Gastric Neoplasms Under Computer-aided Endoscopy","Computer-aided Gastric Lesion Localization and Miss Rate of Gastric Neoplasms: a Tandem, Randomized Controlled Study","Inclusion Criteria:\n\n* Patients aged 40 or older\n* Scheduled for elective upper endoscopy\n\nExclusion Criteria:\n\n* Pregnant women,\n* Inability to provide written informed consent\n* Prior gastrectomy, and\n* Patients deemed unsuitable or high-risk for endoscopy with severe comorbid illnesses",{"count":534,"type":21},1000,[82],"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.",[538,30],"Gastric Neoplasm",[538,59,540],"Miss rate","2025-11-19",{"date":543,"type":35},"2025-11-20",{"date":545,"type":35},"2024-11-01",{"date":260,"type":21},{"name":548,"class":42},"The University of Hong Kong",{"id":550,"slug":551,"hasResults":12,"nctId":552,"briefTitle":553,"officialTitle":554,"acronym":4,"eligibilityCriteria":555,"healthyVolunteers":50,"sex":17,"minAge":556,"maxAge":557,"enrollmentInfo":558,"targetDuration":4,"studyType":22,"phases":560,"briefSummary":561,"conditions":562,"keywords":566,"overallStatus":112,"whyStopped":4,"lastUpdateSubmitDate":568,"lastUpdatePostDateStruct":569,"startDateStruct":571,"completionDateStruct":573,"leadSponsor":575,"locationsCount":43},"100605284","ai-toothbrush-and-visual-pedagogy-to-improve-oral-hygiene-in-children-with-autism-spectrum-disorder-100605284","NCT07160517","AI Toothbrush and Visual Pedagogy to Improve Oral Hygiene in Children With Autism Spectrum Disorder","Effectiveness of an Artificial Intelligence-Enabled Electric Toothbrush and Visual Pedagogy Materials on Oral Hygiene of Children With Autism Spectrum Disorder","Inclusion Criteria:Children aged 5-13 years with a prior diagnosis of autism spectrum disorder (ASD) level 1 , 2 or 3 confirmed by a neuropediatrician.\n\nChild must be accompanied by a primary caregiver (≥18 years old) responsible for daily oral hygiene.\n\nCaregiver must have access to a smartphone or computer to interact with the digital platform.\n\nWritten informed consent (TCLE) from the caregiver and assent (TALE) from the child.\n\n\\-\n\nExclusion Criteria:Severe systemic medical conditions that contraindicate participation in clinical oral evaluations.\n\nChildren with advanced periodontal disease or other oral conditions requiring urgent dental treatment.\n\nCaregivers who are unable or unwilling to use basic digital platforms (smartphone, tablet, or computer).\n\nFamilies who decline participation at any stage of the study.\n\n\\-","5 Years","13 Years",{"count":559,"type":21},50,[82],"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.",[563,564,565,30],"Autism Spectrum Disorder","Child","Oral Health",[567],"Autism Spectrum Disorder, Toothbrushing, Oral Hygiene, Artificial Intelligence.","2025-08-29",{"date":570,"type":35},"2025-09-08",{"date":572,"type":21},"2025-10-01",{"date":574,"type":21},"2027-02-10",{"name":576,"class":42},"University of Sao Paulo",{"id":578,"slug":579,"hasResults":12,"nctId":580,"briefTitle":581,"officialTitle":581,"acronym":4,"eligibilityCriteria":582,"healthyVolunteers":50,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":583,"targetDuration":4,"studyType":22,"phases":584,"briefSummary":585,"conditions":586,"keywords":589,"overallStatus":112,"whyStopped":4,"lastUpdateSubmitDate":592,"lastUpdatePostDateStruct":593,"startDateStruct":594,"completionDateStruct":596,"leadSponsor":598,"locationsCount":4},"100605201","multimodal-radiology-report-to-improve-patient-centered-radiology-100605201","NCT07159438","Multimodal Radiology Report to Improve Patient-centered Radiology","Inclusion Criteria:\n\n* Age 18+ adults who have taken a radiology examination.\n\nExclusion Criteria:\n\n* N\u002FA",{"count":80,"type":21},[82],"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:\n\nDo 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.",[587,135,588,30],"Health Literacy","Imaging Results",[590,591,30],"Patient-centered Understanding","Radiology Reports","2025-08-28",{"date":570,"type":35},{"date":595,"type":21},"2026-12",{"date":597,"type":21},"2028-12",{"name":599,"class":42},"Harvard Medical School (HMS and HSDM)",{"id":601,"slug":602,"hasResults":12,"nctId":603,"briefTitle":604,"officialTitle":605,"acronym":606,"eligibilityCriteria":607,"healthyVolunteers":50,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":608,"targetDuration":4,"studyType":22,"phases":609,"briefSummary":610,"conditions":611,"keywords":616,"overallStatus":31,"whyStopped":4,"lastUpdateSubmitDate":619,"lastUpdatePostDateStruct":620,"startDateStruct":622,"completionDateStruct":624,"leadSponsor":626,"locationsCount":69},"100540464","assessment-of-liver-diseases-using-a-deep-learning-approach-based-on-ultrasound-rf-data-100540464","NCT06317181","Assessment of Liver Diseases Using a Deep-Learning Approach Based on Ultrasound RF-Data","Acquisition and Frequency Spectroscopic Evaluation of Broadband Clinical Ultrasound Raw Data for Liver Cirrhosis and Focal Pathologies Using Neural Networks for Tissue and Pathology Differentiation","LivSPECTRUS","Inclusion Criteria:\n\n* scheduled for an ultrasound investigation by an independent physician\n* signed declaration of consent\n\nExclusion Criteria:\n\n* smaller interventions in the same liver during the last 2 Week (for example liver biopsy)\n* contrast enhanced ultrasound less than a day ago\n* major intervention at the liver (for example partial resection)",{"count":80,"type":21},[82],"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.\n\nThe main questions the study aims to answer are:\n\n* Can a neuronal network trained on RF Data perform equally good as elastography in the assessment of diffuse liver diseases?\n* 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?\n* Can a neuronal network trained on RF Data distinguish focal pathologies in the liver from healthy tissue?\n\nTo answer these questions participants with a clinically indicated fibroscan will undergo:\n\n* a clinical elastography in Case ob suspected diffuse liver disease\n* 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\n* a clinical ultrasound examination during which b-mode images and the corresponding RF-Data sets are captured",[30,612,613,614,615],"Ultrasonography","Elasticity Imaging Techniques","Liver Diseases","Metastasis to Liver",[617,618],"Quantitative Ultrasound","Radiofrequency Data","2025-08-18",{"date":621,"type":35},"2025-08-20",{"date":623,"type":35},"2024-04-01",{"date":625,"type":21},"2025-12",{"name":627,"class":42},"Technische Universität Dresden",{"id":629,"slug":630,"hasResults":12,"nctId":631,"briefTitle":632,"officialTitle":633,"acronym":4,"eligibilityCriteria":634,"healthyVolunteers":12,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":635,"targetDuration":4,"studyType":53,"phases":4,"briefSummary":636,"conditions":637,"keywords":4,"overallStatus":31,"whyStopped":4,"lastUpdateSubmitDate":619,"lastUpdatePostDateStruct":640,"startDateStruct":642,"completionDateStruct":644,"leadSponsor":646,"locationsCount":648},"100496063","augmented-endobronchial-ultrasound-ebus-tbna-with-artificial-intelligence-100496063","NCT05739331","Augmented Endobronchial Ultrasound (EBUS-TBNA) With Artificial Intelligence","Automatic Segmentation of Mediastinal Lymph Nodes and Blood Vessels in Endobronchial Ultrasound (EBUS) Images Using a Deep Neural Network","Inclusion Criteria:\n\n* Subjects referred to thoracic department in any of the participating hospitals with undiagnosed enlarged mediastinal and hilar lymph nodes.\n* Subjects have to be ≥ 18 years of age\n\nExclusion Criteria:\n\n* Pregnancy\n* Any patient that the Investigator feels is not appropriate for this study for any reason.",{"count":559,"type":21},"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.",[30,638,639],"Endobronchial Ultrasound","Lung Cancer",{"date":641,"type":35},"2025-08-22",{"date":643,"type":35},"2023-05-01",{"date":645,"type":21},"2027-12-01",{"name":647,"class":42},"Norwegian University of Science and Technology",2,{"id":650,"slug":651,"hasResults":12,"nctId":652,"briefTitle":653,"officialTitle":653,"acronym":4,"eligibilityCriteria":654,"healthyVolunteers":12,"sex":17,"minAge":180,"maxAge":511,"enrollmentInfo":655,"targetDuration":4,"studyType":53,"phases":4,"briefSummary":657,"conditions":658,"keywords":4,"overallStatus":31,"whyStopped":4,"lastUpdateSubmitDate":660,"lastUpdatePostDateStruct":661,"startDateStruct":663,"completionDateStruct":665,"leadSponsor":667,"locationsCount":418},"100600545","artificial-intelligence-for-pathology-diagnosis-and-prognosis-prediction-of-lung-nodule-using-smartphone-photos-100600545","NCT07098884","Artificial Intelligence for Pathology Diagnosis and Prognosis Prediction of Lung Nodule Using Smartphone Photos","Inclusion Criteria: (1) Participants scheduled for surgery for radiological finding of pulmonary lesions from the preoperative thin-section CT scans; (2) Age ranging from 20-75 years.\n\nExclusion Criteria: (1) Participants with incomplete clinical information; (2) Participants who have received anti-tumor therapy.",{"count":656,"type":21},600,"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.",[30,659],"Lung Nodule","2025-07-30",{"date":662,"type":35},"2025-08-01",{"date":664,"type":35},"2025-06-01",{"date":666,"type":21},"2025-10-30",{"name":668,"class":669},"Anhui Provincial Hospital","OTHER_GOV"]