[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"conditionNormalized\":\"artificial-intelligence-ai\",\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:artificial-intelligence-ai":31},{"pageToken":4,"total":5,"offset":6,"count":7,"results":8},null,88,0,25,[9,54,89,130,156,184,208,230,256,283,302,332,356,378,401,427,456,477,503,527,550,575,599,626,646],{"id":10,"slug":11,"hasResults":12,"nctId":13,"briefTitle":14,"officialTitle":15,"acronym":16,"eligibilityCriteria":17,"healthyVolunteers":18,"sex":19,"minAge":20,"maxAge":4,"enrollmentInfo":21,"targetDuration":4,"studyType":24,"phases":25,"briefSummary":27,"conditions":28,"keywords":32,"overallStatus":42,"whyStopped":4,"lastUpdateSubmitDate":43,"lastUpdatePostDateStruct":44,"startDateStruct":47,"completionDateStruct":49,"leadSponsor":51,"locationsCount":4},"100644861","ai-timing-in-chest-x-ray-interpretation-using-eye-tracking-100644861",false,"NCT07675694","AI Timing in Chest X-ray Interpretation Using Eye-Tracking","A Within-Subject Eye-Tracking Study Examining How the Timing of AI Decision Support Influences Visual Search Behaviour, Diagnostic Accuracy, and Trust During Chest X-ray Interpretation","CREAITED","Inclusion Criteria:\n\nMain reader study:\n\n* Healthcare professionals aged 18 years or over\n* Registered to practise in the UK\n* Employed by the NHS or another healthcare service operating in the UK\n* Current or recent, within the last 3 years, clinical experience involving chest X-ray interpretation, review, or use in clinical practice\n* Able to attend two onsite study sessions at University Hospitals of Leicester NHS Trust at mutually agreed times\n* Able and willing to provide written informed consent\n* Compatible with the eye-tracking equipment\n\nSupplementary survey:\n\n* Adults aged 18 years or over\n* Live in the UK or have used the NHS or another UK healthcare service within the last 5 years\n* Able to provide informed electronic consent\n* Belong to one of the following respondent groups: healthcare professionals or healthcare staff, patients or carers, or members of the public\n* Healthcare professional respondents may include adults involved in requesting, interpreting, checking, or acting on chest X-ray findings in clinical practice\n\nExclusion Criteria:\n\nMain reader study:\n\n* Inability to attend both onsite study sessions at University Hospitals of Leicester NHS Trust\n* Eye-tracking incompatibility, such as visual, neurological, or physical conditions preventing adequate gaze tracking or participant comfort\n* Direct involvement in selection, adjudication, or preparation of the chest X-rays used in the study\n* Conflicts of interest, including direct involvement in development of the AI system under evaluation\n* Prior participation in a closely related AI chest X-ray study where overlap in image sets or study procedures may compromise validity, assessed on a case-by-case basis\n\nSupplementary survey:\n\n* Aged under 18 years\n* Does not live in the UK and has not used the NHS or another UK healthcare service within the last 5 years\n* Unable to provide informed electronic consent\n* Does not meet one of the eligible respondent groups for the survey",true,"ALL","18 Years",{"count":22,"type":23},24,"ESTIMATED","INTERVENTIONAL",[26],"NA","Chest X-rays are commonly used to help diagnose and manage chest conditions. Artificial intelligence (AI) tools are increasingly being used to support chest X-ray interpretation. However, it is not yet clear whether the timing of AI information affects how clinicians review images, make decisions, and use AI support.\n\nThis study will look at whether showing AI information before or after a clinician first reviews a chest X-ray changes how they look at the image, how long they take, their interpretation decisions, their confidence, and their trust in AI support.\n\nHealthcare professional participants will complete two chest X-ray interpretation sessions in a controlled NHS research setting. During each session, participants will review de-identified chest X-ray images while wearing eye-tracking equipment. Eye-tracking will record where a participant looks on the image and how long they spend looking at different areas.\n\nIn one session, AI information will be shown before the participant reviews the chest X-ray. In the other session, AI information will be shown after the participant has first reviewed the chest X-ray. The order of these two sessions will be balanced across participants.\n\nThe study uses de-identified chest X-ray images from existing examinations. It does not involve patients directly, does not change clinical care, and no clinical decisions will be made from the study readings. Participants will also complete a short questionnaire about their experience of using AI support. A separate anonymous survey will collect wider views from clinicians, patients, members of the public, and healthcare staff about the use of AI in chest X-ray interpretation.",[29,30,31],"Diagnostic Imaging","Eye Tracking","Artificial Intelligence (AI)",[33,34,35,36,37,38,39,40,41],"Artificial intelligence timing","Chest X-ray interpretation","Visual search behaviour","Diagnostic accuracy","Trust in automation","Automation bias","Clinician confidence","Decision support systems","Human-AI interaction","NOT_YET_RECRUITING","2026-06-29",{"date":45,"type":46},"2026-06-30","ACTUAL",{"date":48,"type":23},"2026-06",{"date":50,"type":23},"2026-12",{"name":52,"class":53},"University Hospitals, Leicester","OTHER",{"id":55,"slug":56,"hasResults":12,"nctId":57,"briefTitle":58,"officialTitle":59,"acronym":60,"eligibilityCriteria":61,"healthyVolunteers":12,"sex":19,"minAge":20,"maxAge":4,"enrollmentInfo":62,"targetDuration":4,"studyType":64,"phases":4,"briefSummary":65,"conditions":66,"keywords":70,"overallStatus":79,"whyStopped":4,"lastUpdateSubmitDate":80,"lastUpdatePostDateStruct":81,"startDateStruct":83,"completionDateStruct":84,"leadSponsor":86,"locationsCount":88},"100639433","diagnostic-accuracy-of-gpt-4o-and-claude-for-heart-score-calculation-in-chest-pain-100639433","NCT07626060","Diagnostic Accuracy of GPT-4o and Claude for HEART Score Calculation in Chest Pain","Diagnostic Accuracy of Large Language Models (GPT-4o and Claude) in HEART Score Calculation and 30-Day MACE Prediction in Emergency Department Chest Pain Patients: A Prospective Observational Validation Study Against Three-Expert Consensus","LLM-HEART","INCLUSION CRITERIA:\n\n* Age \\>=18 years\n* Chief complaint of non-traumatic chest pain at the emergency department\n* Written informed consent obtained from the patient or legally authorized representative\n* Availability for 30-day follow-up (reachable by telephone and\u002For actively registered in the e-Nabiz national health database)\n\nEXCLUSION CRITERIA:\n\n* Traumatic chest pain etiology\n* ST-elevation myocardial infarction (STEMI) at presentation requiring immediate reperfusion protocol\n* Refusal or subsequent withdrawal of informed consent\n* Inability to complete the mandatory 30-day follow-up period\n\nWITHDRAWAL CRITERIA:\n\n* Patient or representative requests data withdrawal after initial consent\n* Administrative identification of retrospective data entry after enrollment",{"count":63,"type":23},690,"OBSERVATIONAL","This prospective observational diagnostic accuracy study evaluates whether large language models (LLMs) - GPT-4o (OpenAI, gpt-4o-2024-11-20) and Claude (Anthropic, claude-sonnet-4-6) - can accurately calculate HEART scores from unstructured Turkish clinical notes and predict 30-day major adverse cardiac events (MACE) in emergency department patients presenting with non-traumatic chest pain.\n\nThe study will enroll 600 consecutive adult patients. For each patient, the same anonymized data (free-text anamnesis, ECG report text, troponin value, and age) will be independently processed by both LLMs via separate API calls with deterministic settings (temperature=0, JSON format). A three-expert consensus HEART score - derived through blinded independent scoring by three emergency medicine physicians with majority-vote adjudication - serves as the reference standard for agreement analysis. Actual 30-day MACE (all-cause death, AMI Type 1\u002F2\u002F4b, unplanned revascularization) determined via national health database and telephone follow-up serves as the outcome for diagnostic accuracy analysis.\n\nA secondary documentation-quality sub-study will quantify how spontaneously Turkish emergency anamnesis notes capture HEART score parameters.",[67,31,68,69],"Emergency Medicine","Artificial Intelligence (AI) in Diagnosis","Chest Pain Rule Out Myocardial Infarction",[71,72,73,74,75,76,77,78],"Large Language Model","GPT-4o","Claude Sonnet","Emergency Department","Diagnostic Accuracy","Medical Informatics","Physician vs AI","HEART score","RECRUITING","2026-06-22",{"date":82,"type":46},"2026-06-23",{"date":48,"type":23},{"date":85,"type":23},"2027-06",{"name":87,"class":53},"Marmara University Pendik Training and Research Hospital",1,{"id":90,"slug":91,"hasResults":12,"nctId":92,"briefTitle":93,"officialTitle":94,"acronym":95,"eligibilityCriteria":96,"healthyVolunteers":18,"sex":19,"minAge":20,"maxAge":4,"enrollmentInfo":97,"targetDuration":4,"studyType":24,"phases":99,"briefSummary":100,"conditions":101,"keywords":110,"overallStatus":42,"whyStopped":4,"lastUpdateSubmitDate":121,"lastUpdatePostDateStruct":122,"startDateStruct":124,"completionDateStruct":126,"leadSponsor":128,"locationsCount":88},"100644109","morphology-in-oral-rare-syndromes--artificial-intelligence-for-clinical-diagnosis-100644109","NCT07666269","Morphology in Oral Rare Syndromes & Artificial Intelligence for Clinical Diagnosis","Geometric Morphometric Characterization of Oro-Dental Anomalies in Rare Bone and Cartilage Diseases From 3D Digital Data (MOSAIC)","MOSAIC","Inclusion Criteria:\n\n* For cases: Diagnosis of a rare bone and cartilage disorder confirmed by the Rare Disease Competence Center for Constitutional Bone Disorders (MOC) or Calcium and Phosphate Metabolism Disorders (CaP), genetically and\u002For clinically.\n* Ability to undergo a 3D intra-oral scan;\n* Ability of the participant to understand the information notice provided regarding the use of their medical data and 3D digital models for research purposes, and to express informed non-objection to participation in the research.\n* For controls: healthy adults recruited in the Dental Medicine Department.\n\nExclusion Criteria:\n\n* History of major orthodontic\u002Forthognathic treatment;\n* Craniofacial conditions unrelated to the studied diseases (e.g., cleft palate, non-target craniofacial syndromes);\n* Impossibility to obtain a 3D optical impression;\n* Refusal or inability of the participant to understand the information notice and\u002For to express informed non-objection to participation in the research.",{"count":98,"type":23},240,[26],"MOSAIC aims to determine whether oro-dental morphological anomalies, particularly palatal morphology, associated with rare bone and cartilage diseases can be precisely characterized using 3D digital models analysed through geometric morphometrics. The study will also evaluate whether these morphological signatures can train an artificial intelligence (AI) algorithm to classify syndromes. A prospective monocentric case-control cohort will be constituted, including 3D intra-oral scans and associated clinical data. The final goal is to improve diagnostic accuracy and reduce diagnostic delay in rare bone disorders.",[102,103,104,105,106,107,108,31,109],"Osteogenesis Imperfecta","Rare Bone Disorders","Hypophosphatemia","X-Linked","Mucopolysaccharidoses","Tooth Abnormalities","Palate; Deformity","Machine Learning",[111,112,113,114,115,116,117,118,119,120],"Rare bone diseases","palatal morphology","geometric morphometrics","3D intra-oral scan","machine learning","artificial intelligence","diagnostic classification","osteogenesis imperfecta","X-linked hypophosphatemia","mucopolysaccharidosis","2026-06-18",{"date":123,"type":46},"2026-06-24",{"date":125,"type":23},"2026-09-01",{"date":127,"type":23},"2028-03-01",{"name":129,"class":53},"University Hospital, Bordeaux",{"id":131,"slug":132,"hasResults":12,"nctId":133,"briefTitle":134,"officialTitle":135,"acronym":4,"eligibilityCriteria":136,"healthyVolunteers":12,"sex":19,"minAge":4,"maxAge":4,"enrollmentInfo":137,"targetDuration":4,"studyType":64,"phases":4,"briefSummary":139,"conditions":140,"keywords":143,"overallStatus":79,"whyStopped":4,"lastUpdateSubmitDate":148,"lastUpdatePostDateStruct":149,"startDateStruct":150,"completionDateStruct":152,"leadSponsor":154,"locationsCount":88},"100644086","comparison-of-digital-analysis-and-artificial-intelligence-for-cephalometric-tracing-100644086","NCT07664488","Comparison of Digital Analysis and Artificial Intelligence for Cephalometric Tracing","Cephalometric Tracing: A Comparison Between Digital Analysis and Artificial Intelligence","Inclusion Criteria:\n\n* Availability of digital lateral cephalometric radiographs of adequate diagnostic quality\n* Radiographs acquired with patients in centric occlusion and proper head positioning using a cephalostat\n* Patients of any age and sex\n* Absence of congenital or acquired craniofacial anomalies\n* No previous orthodontic treatment\n* No previous orthognathic surgical treatment\n* Absence of agenesis of incisors or first molars\n* Absence of supernumerary teeth overlapping the region of interest\n\nExclusion Criteria:\n\n* Radiographs presenting artifacts or inadequate visualization of anatomical structures\n* History of significant craniofacial trauma\n* Radiographs acquired without a cephalostat\n* Presence of severe skeletal asymmetries\n* Incomplete clinical or radiographic records\n* Radiographs unsuitable for manual or AI-based cephalometric landmark identification",{"count":138,"type":23},100,"This study aims to evaluate the accuracy and reliability of artificial intelligence (AI)-based cephalometric analysis compared with digital manual tracing. A total of 100 standardized lateral cephalometric radiographs will be analyzed using Delta-Dent software with manual landmark identification and three fully automated AI-based systems (WebCeph, QuantX, and Smartee). Sagittal, vertical, dental, and soft tissue cephalometric parameters will be compared among the different methods. Statistical analysis will assess inter-method agreement and the clinical relevance of any observed discrepancies. The study seeks to determine whether AI-based systems provide measurements comparable to conventional digital tracing and whether they can be considered reliable adjunctive tools in orthodontic diagnosis and treatment planning.",[141,142,31,68],"Cephalometric Analysis","Cephalometry",[116,144,145,146,147],"cephalometric analysis","lateral cephalogram","landmark identification","automated cephalometric tracing","2026-06-17",{"date":123,"type":46},{"date":151,"type":46},"2026-06-01",{"date":153,"type":23},"2026-09-30",{"name":155,"class":53},"University of Pavia",{"id":157,"slug":158,"hasResults":12,"nctId":159,"briefTitle":160,"officialTitle":161,"acronym":4,"eligibilityCriteria":162,"healthyVolunteers":18,"sex":19,"minAge":20,"maxAge":4,"enrollmentInfo":163,"targetDuration":4,"studyType":64,"phases":4,"briefSummary":165,"conditions":166,"keywords":171,"overallStatus":42,"whyStopped":4,"lastUpdateSubmitDate":176,"lastUpdatePostDateStruct":177,"startDateStruct":179,"completionDateStruct":180,"leadSponsor":182,"locationsCount":4},"100643796","non-contrast-breast-mri-diagnosis-and-risk-stratification-using-dwi-generated-synthetic-contrast-enhancement-100643796","NCT07598084","Non-Contrast Breast MRI Diagnosis and Risk Stratification Using DWI-Generated Synthetic Contrast Enhancement","Artificial Intelligence Solution for Simplifying the Diagnostic Workflow of Breast MRI: Development and Clinical Validation of a Diffusion-Weighted Imaging-Based Synthetic Contrast-Enhanced MRI System for Non-Contrast Breast Cancer Diagnosis and Risk Stratification","Inclusion Criteria:\n\n1. Complete breast MRI data;\n2. Negative pathology biopsy results or negative follow-up examinations for at least 12 months for non-cancer cases;\n3. Positive biopsy results that meet the requirements for the pathological subtype of cancer for cancer cases;\n4. Original data that can be used to verify clinical status, including radiological and pathological reports;\n\nExclusion Criteria:\n\n1. Partial mastectomy or puncture biopsy on the diseased side of the breast prior to breast MRI examination;\n2. Poor image quality;\n3. Implants in the affected breast;",{"count":164,"type":23},12000,"This study is conducted under the ethics-approved project titled \"Artificial Intelligence Solution for Simplifying the Diagnostic Workflow of Breast MRI''.The goal of this observational study is to develop an integrated breast MRI system that uses diffusion-weighted imaging (DWI) to create synthetic contrast-enhanced images. This system aims to diagnose and screen for breast cancer without the need for contrast agents, while using a generated risk score to perform imaging-based triage and risk stratification.\n\nParticipants will include people aged 18 and older who require a breast MRI either for evaluation of a suspicious finding or for high-risk screening.\n\nThis study seeks to answer two main questions:\n\n* Can synthetic contrast-enhanced images generated from DWI match real contrast-enhanced images in their ability to distinguish benign from malignant breast lesions?\n* Can the risk score derived from DWI-based synthetic images enable imaging-level risk stratification, allowing people at lower risk to avoid contrast agent injection? Researchers will compare the quality of synthetic images against real contrast-enhanced images and will recruit radiologists to assess how well these images perform for diagnostic and screening tasks. MRI data from participants undergoing breast MRI will be used to train, validate, and test this integrated system.",[167,31,168,169,170],"Breast Neoplasms","Magnetic Resonance Imaging (MRI)","Diffusion Magnetic Resonance Imaging","Deep Learning",[172,173,174,175],"Breast","Magnetic Resonance Imaging","Artificial Intelligence","Deep learning","2026-06-05",{"date":178,"type":46},"2026-06-09",{"date":48,"type":23},{"date":181,"type":23},"2027-05",{"name":183,"class":53},"Peking University People's Hospital",{"id":185,"slug":186,"hasResults":12,"nctId":187,"briefTitle":188,"officialTitle":189,"acronym":4,"eligibilityCriteria":190,"healthyVolunteers":18,"sex":19,"minAge":20,"maxAge":4,"enrollmentInfo":191,"targetDuration":4,"studyType":64,"phases":4,"briefSummary":193,"conditions":194,"keywords":198,"overallStatus":79,"whyStopped":4,"lastUpdateSubmitDate":200,"lastUpdatePostDateStruct":201,"startDateStruct":202,"completionDateStruct":204,"leadSponsor":205,"locationsCount":207},"100639932","artificial-intelligence-in-perioperative-nursing-100639932","NCT07601373","Artificial Intelligence in Perioperative Nursing","Artificial Intelligence in Perioperative Nursing: A Mixed-Methods Study on Current Perspectives and Future Directions Among Surgical Ward and Operating Room Nurses.","Inclusion Criteria:\n\n* Licensed registered nurses\n* Minimum 6 months of perioperative experience\n* Currently working in surgical wards or operating rooms\n* Direct involvement in patient care\n\nExclusion Criteria:\n\n\\-",{"count":192,"type":23},150,"This mixed-methods study aims to assess current perspectives, attitudes, and preparedness of perioperative nurses regarding the integration of artificial intelligence (AI) in clinical practice. The study targets nurses working in surgical wards and operating rooms to explore AI utilization, perceived usability, professional impact, and readiness for future implementation. Quantitative and qualitative data will be collected concurrently and integrated to generate comprehensive insights into AI adoption and future directions in perioperative nursing.",[31,195,196,197],"Perioperative Nursing","Surgical Care","Nursing Informatics",[174,195,199],"Operating Room","2026-06-04",{"date":176,"type":46},{"date":203,"type":46},"2026-05-30",{"date":48,"type":23},{"name":206,"class":53},"Alexandria University",2,{"id":209,"slug":210,"hasResults":12,"nctId":211,"briefTitle":212,"officialTitle":213,"acronym":214,"eligibilityCriteria":215,"healthyVolunteers":12,"sex":19,"minAge":20,"maxAge":4,"enrollmentInfo":216,"targetDuration":4,"studyType":64,"phases":4,"briefSummary":218,"conditions":219,"keywords":4,"overallStatus":79,"whyStopped":4,"lastUpdateSubmitDate":222,"lastUpdatePostDateStruct":223,"startDateStruct":224,"completionDateStruct":226,"leadSponsor":228,"locationsCount":207},"100618587","development-and-pre-validation-of-a-machine-learning-based-prediction-algorithm-for-early-functional-recovery-in-patients-undergoing-hip-and-knee-replacement-surgery-100618587","NCT07333560","Development and Pre-validation of a Machine Learning-based Prediction Algorithm for Early Functional Recovery in Patients Undergoing Hip and Knee Replacement Surgery","Development and Pre-validated Multiple Variable Prediction Model Using Machine Learning for Early Functional Recovery After Joint Replacement Surgery.","FISIO_IA","Inclusion Criteria:\n\n* Adults aged 18 years or older\n* Patients underwent elective hip or knee arthroplasty.\n* Patients for whom postoperative physiotherapy was initiated.\n\nExclusion Criteria:\n\n* Patients who underwent surgery for oncologic disease, femoral fracture, or revision joint arthroplasty.\n* Patients for whom postoperative physiotherapy was not provided due to postoperative complications\n* clinical data are unavailable.",{"count":217,"type":23},943,"The goal of this observational study is to develop and pre-validate a machine learning algorithm to predict early recovery of mobility in patients undergoing hip or knee joint replacement surgery. The primary research question is:\n\nCan a machine learning model accurately classify patients with faster versus slower recovery of autonomous mobility in the first days after joint replacement surgery?\n\nPatients who have undergone elective hip or knee arthroplasty and received post-operative physiotherapy will have their clinical and perioperative data collected retrospectively (2020-2023) and prospectively (March 2026-December 2027). The algorithm will be trained on retrospective data and tested prospectively to evaluate its predictive performance for early mobilization and length of hospital stay.",[31,109,220,221],"Joint Replacement","Predictive Model","2026-05-27",{"date":151,"type":46},{"date":225,"type":46},"2026-03-09",{"date":227,"type":23},"2027-12",{"name":229,"class":53},"Istituto Ortopedico Rizzoli",{"id":231,"slug":232,"hasResults":12,"nctId":233,"briefTitle":234,"officialTitle":235,"acronym":4,"eligibilityCriteria":236,"healthyVolunteers":18,"sex":19,"minAge":20,"maxAge":4,"enrollmentInfo":237,"targetDuration":4,"studyType":24,"phases":239,"briefSummary":240,"conditions":241,"keywords":247,"overallStatus":42,"whyStopped":4,"lastUpdateSubmitDate":249,"lastUpdatePostDateStruct":250,"startDateStruct":251,"completionDateStruct":252,"leadSponsor":254,"locationsCount":88},"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":238,"type":23},78,[26],"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",[242,243,244,245,174,246,31],"Nursing Process Competence","Artificial Intelligence Perception and Attitude","Nursing Education","Nursing Process","Clinical Competence",[174,245,244,246,248],"Attitude of Health Personnel","2026-05-24",{"date":151,"type":46},{"date":151,"type":23},{"date":253,"type":23},"2026-12-31",{"name":255,"class":53},"University of Yalova",{"id":257,"slug":258,"hasResults":12,"nctId":259,"briefTitle":260,"officialTitle":261,"acronym":4,"eligibilityCriteria":262,"healthyVolunteers":18,"sex":19,"minAge":4,"maxAge":4,"enrollmentInfo":263,"targetDuration":4,"studyType":24,"phases":265,"briefSummary":266,"conditions":267,"keywords":270,"overallStatus":42,"whyStopped":4,"lastUpdateSubmitDate":275,"lastUpdatePostDateStruct":276,"startDateStruct":278,"completionDateStruct":280,"leadSponsor":281,"locationsCount":4},"100640308","effect-of-ai-supported-case-analysis-on-nursing-students-100640308","NCT07611383","Effect of AI-Supported Case Analysis on Nursing Students","The Effect of AI-Supported Case Analysis on Nursing Students' Knowledge, Learning Satisfaction Levels, and Clinical Decision-Making Skills","Inclusion Criteria\n\n* Students who will be active second-year nursing students during the spring semester of the 2025-2026 academic year,\n* Who have previously taken the theoretical course on the nursing process,\n* Who own a smartphone with an internet connection,\n* Who have previously prepared a patient-specific care plan for an inpatient in at least one internal medicine clinic will be included in the sample.\n\nExclusion Criteria:\n\n* Students who have not taken the elective course in oncology nursing,\n* Students who have not planned care for inpatients in internal medicine clinics during their previous clinical rotations,\n* Students who do not agree to participate in the study will not be included in the research",{"count":264,"type":23},42,[26],"The aim of this study is to determine the effect of AI-supported oncology case analysis on nursing students' knowledge, level of learning satisfaction, and clinical decision-making skills. This study is planned to be conducted using a single-blind randomized controlled trial design for the quantitative research component and an interview design for the qualitative research component. The students will be divided into two groups: an intervention group (artificial intelligence) and a control group (traditional instruction).",[268,31,269],"Nursing Students","Clinical Decision-Making in Nursing",[271,272,273,274],"nursing student","airtificial intelligence","nursing education","clinical decision","2026-05-22",{"date":277,"type":46},"2026-05-28",{"date":279,"type":23},"2026-05-25",{"date":203,"type":23},{"name":282,"class":53},"Nevsehir Haci Bektas Veli University",{"id":284,"slug":285,"hasResults":12,"nctId":286,"briefTitle":287,"officialTitle":288,"acronym":4,"eligibilityCriteria":289,"healthyVolunteers":18,"sex":19,"minAge":4,"maxAge":4,"enrollmentInfo":290,"targetDuration":4,"studyType":24,"phases":292,"briefSummary":293,"conditions":294,"keywords":295,"overallStatus":42,"whyStopped":4,"lastUpdateSubmitDate":275,"lastUpdatePostDateStruct":296,"startDateStruct":298,"completionDateStruct":299,"leadSponsor":300,"locationsCount":88},"100637204","ai-supported-case-analysis-among-nursing-students-100637204","NCT07614503","AI-Supported Case Analysis Among Nursing Students","The Effect of AI-Supported Case Analysis on Nursing Students' Learning Experience, Learning Outcomes, Clinical Self-Efficacy, and Cognitive Load","Inclusion Criteria:\n\n* Students who are active fourth-year nursing students during the spring semester of the 2025-2026 academic year,\n* Have previously taken the theoretical course on the nursing process,\n* Own a cell phone with an internet connection,\n* And have previously prepared a patient-specific care plan for an inpatient in at least one internal medicine clinic will be included in the sample.\n\nExclusion Criteria:\n\n* Students who have not taken the course,\n* Students who have never participated in case-based learning sessions,\n* Students who do not agree to participate in the study will not be included in the research.",{"count":291,"type":23},40,[26],"The aim of this study is to determine the effect of AI-supported internal medicine nursing case analysis on students' case management performance, learning outcomes, learning experience, clinical self-efficacy, and cognitive load levels. This study will be conducted using a single-blind randomized controlled trial design for the quantitative research and an individual interview design for the qualitative research. Students will be randomly assigned to either the intervention (artificial intelligence) or control (case analysis) group.",[268,31],[271,272,273],{"date":297,"type":46},"2026-05-29",{"date":279,"type":23},{"date":45,"type":23},{"name":301,"class":53},"TC Erciyes University",{"id":303,"slug":304,"hasResults":12,"nctId":305,"briefTitle":306,"officialTitle":307,"acronym":4,"eligibilityCriteria":308,"healthyVolunteers":18,"sex":309,"minAge":20,"maxAge":310,"enrollmentInfo":311,"targetDuration":4,"studyType":24,"phases":313,"briefSummary":314,"conditions":315,"keywords":319,"overallStatus":42,"whyStopped":4,"lastUpdateSubmitDate":323,"lastUpdatePostDateStruct":324,"startDateStruct":326,"completionDateStruct":328,"leadSponsor":330,"locationsCount":4},"100639382","the-impact-of-ai-powered-training-on-gynecological-examination-anxiety-and-satisfaction-100639382","NCT07599358","The Impact of AI-Powered Training on Gynecological Examination Anxiety and Satisfaction","Digital Transformation in Women's Health: The Impact of AI-Powered Training on Gynecological Examination Anxiety and Satisfaction","Inclusion Criteria:\n\n* Applying to the outpatient clinic for a gynecological examination\n* Being between 18-65 years of age\n* Agreeing to participate in the study\n\nExclusion Criteria:\n\n* Communication barrier\n* Having a psychological diagnosis,\n* Being pregnant","FEMALE","65 Years",{"count":312,"type":23},114,[26],"Gynecological cancers, particularly cervical, ovarian, and endometrial cancers, pose a global problem. Cervical cancers are quite common worldwide, and this rate is even higher in developing countries. Cervical cancers are easily treatable when detected early, and screening is quite easy. Diagnosis is routinely made through human papillomavirus (HPV) testing and cytological screening. Eliminating anxiety, fear, and uncertainty about gynecological examinations makes the examination process easier, thus enabling early diagnosis and treatment of diseases. Keeping up with developing and changing technology and using it to improve women's health is an undeniable change in recent times. This study aims to determine the effect of an AI-assisted informational training program on women's anxiety and satisfaction levels regarding gynecological examinations.",[316,31,317,318],"Gynecological Examination","Patient Satisfaction","Anxiety",[320,116,321,322],"gynecological examination","patient satisfaction","anxiety","2026-05-14",{"date":325,"type":46},"2026-05-20",{"date":327,"type":23},"2026-05-02",{"date":329,"type":23},"2026-09-29",{"name":331,"class":53},"Fenerbahce University",{"id":333,"slug":334,"hasResults":12,"nctId":335,"briefTitle":336,"officialTitle":336,"acronym":4,"eligibilityCriteria":337,"healthyVolunteers":18,"sex":19,"minAge":310,"maxAge":4,"enrollmentInfo":338,"targetDuration":4,"studyType":24,"phases":340,"briefSummary":341,"conditions":342,"keywords":4,"overallStatus":42,"whyStopped":4,"lastUpdateSubmitDate":347,"lastUpdatePostDateStruct":348,"startDateStruct":350,"completionDateStruct":352,"leadSponsor":354,"locationsCount":88},"100640256","development-of-a-chatbot-supported-personalized-exercise-program-for-older-adults-and-evaluation-of-its-effects-on-cognitive-functions-100640256","NCT07596082","Development of a Chatbot-supported Personalized Exercise Program for Older Adults and Evaluation of Its Effects on Cognitive Functions","Inclusion Criteria:\n\n* Aged 65 years and older\n* Normal or mildly impaired cognitive status\n* Medical condition that does not prevent participation in physical activity\n* Ability to use a smartphone or tablet (for chatbot access)\n* Voluntary participation\n\nExclusion Criteria:\n\n* Moderate to severe dementia\n* Severe depression or serious psychiatric disorders\n* Acute cardiovascular risk\n* Communication difficulties due to significant hearing or visual impairment\n* Regular engagement in structured exercise within the past 6 months (participants in the maintenance phase of exercise behavior)",{"count":339,"type":23},90,[26],"The purpose of this study is to develop an artificial intelligence-based chatbot application to support exercise behavior in individuals aged 60 and over who do not regularly exercise, and to evaluate its effectiveness. In addition, the study aims to examine the effects of changes in exercise habits on the cognitive (mental) functions of older adults.\n\nIn this study, the impact of a chatbot-supported personalized exercise program on cognitive functions in older individuals will be evaluated. A total of 90 participants is planned for inclusion in this study.\n\nIf you agree to participate in this study, depending on the group you are assigned to, you may receive:\n\n* An artificial intelligence-based chatbot program, along with educational materials about the importance of exercise, or\n* Only educational materials (brochures) prepared by the researchers about the importance of exercise.\n\nAt the beginning of the study, you will be asked to complete a data collection form. The same form will also be administered at week 12 and week 24.\n\nThis form will include:\n\n* Basic information such as your age and gender,\n* Questions about your exercise habits,\n* A brief test to assess your cognitive (mental) functions,\n* Questions evaluating your level of physical activity. The study duration is 24 weeks, including 12 weeks of intervention and 12 weeks of follow-up.",[343,344,345,31,346],"Aged","Exercise","Cognition","Health Behavior","2026-05-12",{"date":349,"type":46},"2026-05-19",{"date":351,"type":23},"2026-08-01",{"date":353,"type":23},"2026-10-30",{"name":355,"class":53},"Dokuz Eylul University",{"id":357,"slug":358,"hasResults":12,"nctId":359,"briefTitle":360,"officialTitle":360,"acronym":4,"eligibilityCriteria":361,"healthyVolunteers":12,"sex":19,"minAge":20,"maxAge":4,"enrollmentInfo":362,"targetDuration":4,"studyType":64,"phases":4,"briefSummary":364,"conditions":365,"keywords":4,"overallStatus":42,"whyStopped":4,"lastUpdateSubmitDate":370,"lastUpdatePostDateStruct":371,"startDateStruct":373,"completionDateStruct":374,"leadSponsor":376,"locationsCount":88},"100639884","research-on-the-whole-process-of-ai-intelligent-management-system-for-the-diagnosis-and-treatment-of-inflammatory-bowel-diseases-100639884","NCT07590271","Research on the Whole Process of AI Intelligent Management System for the Diagnosis and Treatment of Inflammatory Bowel Diseases","Inclusion Criteria:\n\nInclusion criteria for the IBD group:\n\nPatients diagnosed with IBD (K50.00 to K51.919) within the specified time interval.\n\nInclusion criteria for the non-IBD group:\n\nPatients never diagnosed with IBD (K50.00 to K51.919) within the specified time interval.\n\nExclusion Criteria:\n\nPatients not within the specified time interval Deceased patients\n\nExclusion criteria for the non-IBD group:\n\nPatients not within the specified time interval Deceased patients Patients with fewer than 5 hospital visits",{"count":363,"type":23},4500,"Inflammatory bowel disease (IBD), including Crohn's disease (CD) and ulcerative colitis (UC), is a chronic immune-mediated disorder requiring long-term management. Clinically, IBD may involve recurrent intestinal inflammation, ulcer formation, and complications such as strictures and fistulas. The etiology of IBD is associated with immune dysregulation, gut microbiome imbalance, and genetic susceptibility. Its clinical manifestations are heterogeneous; early symptoms such as abdominal pain, diarrhea, weight loss, hematochezia, or anemia often resemble gastroenteritis, irritable bowel syndrome, or infectious enterocolitis, leading to misdiagnosis and delayed diagnosis. According to international studies, the interval between initial symptom onset and confirmed diagnosis can range from several months to years, during which untreated disease progression increases the risks of hospitalization, surgery, bowel strictures, and fistulizing complications, resulting in significant impacts on patient quality of life.\n\nThis study adopts a retrospective design, analyzing our hospital's electronic medical record data from 2023 to 2025.The objective is to evaluate the performance and feasibility of an artificial intelligence (AI) model-developed and incorporating natural language processing (NLP) and phenotypic recognition algorithms-in supporting early identification and diagnosis of IBD. The model has been validated in multiple European healthcare systems and is capable of recognizing high-risk phenotypic clusters from large-scale structured and unstructured medical data. This study represents the first application of this AI technology in the Taiwanese IBD population. All data processing will occur within a de-identified and secure computing environment to ensure data privacy and information security.\n\nThe study will compare AI-generated diagnostic suggestions derived from medical records with actual clinical diagnoses to assess consistency and accuracy. The model's performance across different clinical characteristics, disease severity levels, and stages of illness will also be examined. In addition, statistical metrics such as precision and recall will be used to generate PRC curves for determining the optimal diagnostic threshold. The outcomes of this study are expected to validate the potential of AI technology in facilitating early recognition, accelerating diagnosis, and supporting clinical decision-making for IBD. The findings will provide essential data for developing localized AI models for IBD, ultimately enhancing diagnostic efficiency, shortening the diagnostic timeline, and improving long-term patient outcomes and quality of life.\n\nObjective 1：To retrospectively analyze the clinical characteristics and diagnostic pathways of patients with IBD (CD\u002FUC).\n\nObjective 2：To evaluate the performance of the AI model in identifying and providing diagnostic suggestions for high-risk IBD cases.\n\nObjective 3：To compare the accuracy and consistency between AI-generated diagnostic suggestions and actual clinical diagnoses.",[366,367,368,31,369],"Inflammatory Bowel Disease (Crohn's Disease and Ulcerative Colitis)","Crohn's Disease (CD)","Ulcerative Colitis (UC)","Natural Language Processing (NLP)","2026-05-11",{"date":372,"type":46},"2026-05-15",{"date":151,"type":23},{"date":375,"type":23},"2027-12-31",{"name":377,"class":53},"Taichung Veterans General Hospital",{"id":379,"slug":380,"hasResults":12,"nctId":381,"briefTitle":382,"officialTitle":383,"acronym":4,"eligibilityCriteria":384,"healthyVolunteers":18,"sex":19,"minAge":385,"maxAge":386,"enrollmentInfo":387,"targetDuration":4,"studyType":24,"phases":389,"briefSummary":390,"conditions":391,"keywords":4,"overallStatus":42,"whyStopped":4,"lastUpdateSubmitDate":395,"lastUpdatePostDateStruct":396,"startDateStruct":397,"completionDateStruct":398,"leadSponsor":399,"locationsCount":88},"100640494","ai-gf-gnw-on-prolonged-grief-reactions-100640494","NCT07589088","AI-GF-GNW on Prolonged Grief Reactions","AI-Assisted Grief-Focused Guided Narrative Writing for Subclinical Prolonged Grief Disorder in Bereaved Chinese Adolescents: A Three-Arm Parallel Randomized Controlled Trial.","Inclusion Criteria:\n\n* Junior and senior high school students currently studying in Chinese Mainland, aged 10-19;\n* Between 6 and 60 months prior to the experimental period, experienced the death of a significant family member or close friend or close friend;\n* The total symptom score of PG-13-R is between 20-29 points;\n* Ability to write and understand written guidelines, to use the mobile phone to interact with AI;\n* Consent to participate in the study.\n\nExclusion Criteria:\n\n* Diagnosed or previously diagnosed with mental illness\n* The bereavement time does not meet the time window\n* PG-13-R score is not within the range\n* At present, there is suicidal ideation or recent severe self harm behavior (Reynolds' Suicidal Ideation Questionnaire, SIQ-JR-4\\>0), and a crisis intervention hotline is provided when necessary.\n* Has received other grief counseling or is currently taking psychiatric medication within the past month","10 Years","19 Years",{"count":388,"type":23},126,[26],"Prolonged Grief Disorder (PGD) is a severe, disabling condition characterized by intense yearning and difficulty accepting the reality of loss, which significantly impairs the academic and psychosocial functioning of bereaved adolescents. While Grief-Focused Cognitive Behavioral Therapy (GF-CBT) is effective, its high cost and resource-intensive nature limit its accessibility for adolescents in mainland China. Grief-Focused Guided Narrative Writing (GF-GNW) offers a scalable, low-cost alternative that facilitates memory integration.\n\nFurthermore, integrating Artificial Intelligence (AI) to provide personalized, structured feedback has the potential to simulate therapist functions and enhance intervention efficacy. However, the specific efficacy of AI-assisted feedback in this context remains empirically unvalidated.\n\nThis parallel randomized controlled trial aims to examine the effectiveness of AI-assisted GF-GNW (AI-GF-GNW) in treating Chinese adolescents (aged 10-19) with subclinical PGD, compared to a no-feedback NF-GF-GNW group and a free writing group. Primary outcomes include PGD symptom severity, while secondary outcomes assess depression, anxiety, and daily functioning. We hypothesize that both active intervention arms will significantly alleviate PGD and related symptoms compared to the free writing group, and that the AI-GF-GNW group will demonstrate a significantly greater reduction in symptoms and functional impairment than the NF-GF-GNW group.",[31,392,393,318,394],"Prolonged Grief Symptoms","Depression - Major Depressive Disorder","Disabilities","2026-05-09",{"date":372,"type":46},{"date":279,"type":23},{"date":181,"type":23},{"name":400,"class":53},"Peking University",{"id":402,"slug":403,"hasResults":12,"nctId":404,"briefTitle":405,"officialTitle":406,"acronym":4,"eligibilityCriteria":407,"healthyVolunteers":18,"sex":19,"minAge":20,"maxAge":4,"enrollmentInfo":408,"targetDuration":4,"studyType":64,"phases":4,"briefSummary":410,"conditions":411,"keywords":413,"overallStatus":79,"whyStopped":4,"lastUpdateSubmitDate":418,"lastUpdatePostDateStruct":419,"startDateStruct":421,"completionDateStruct":423,"leadSponsor":424,"locationsCount":88},"100635616","prospective-user-study-and-multicenter-validation-of-multimodal-medical-imaging-large-models-100635616","NCT07555002","Prospective User Study and Multicenter Validation of Multimodal Medical Imaging Large Models","Prospective User Study and Multicenter Validation of Multimodal Medical Imaging Large Models in the Diagnosis of Common Systemic Diseases","Inclusion Criteria:\n\n* Patients who underwent systemic medical imaging examinations (e.g., CT or MRI) at participating centers for common systemic diseases.\n* Imaging data must have confirmed clinical reference standards, expert consensus, or pathological diagnosis.\n* Availability of complete DICOM format images with standard acquisition protocols.\n\nExclusion Criteria:\n\n* Poor image quality (e.g., severe motion or metal artifacts) that precludes definitive diagnosis.\n* Cases with incomplete clinical or pathological reference standards.\n* Corrupted image files or duplicate cases.",{"count":409,"type":23},1000,"This study aims to evaluate the diagnostic performance and clinical utility of a multimodal medical imaging large model in identifying common systemic diseases. Through a retrospective reader study involving multiple centers, the research will compare the diagnostic accuracy, sensitivity, and specificity of radiologists with and without AI assistance. The goal is to validate the model's robustness and its impact on the diagnostic efficiency of clinicians across diverse healthcare settings.",[29,412,31],"Common Systemic Diseases",[414,170,415,416,417],"Multimodal Large Model","Radiology","Multicenter Study","Diagnostic Performance","2026-05-05",{"date":420,"type":46},"2026-05-08",{"date":422,"type":46},"2026-01-01",{"date":50,"type":23},{"name":425,"class":426},"The Third Affiliated Hospital of Southern Medical University","OTHER_GOV",{"id":428,"slug":429,"hasResults":12,"nctId":430,"briefTitle":431,"officialTitle":432,"acronym":433,"eligibilityCriteria":434,"healthyVolunteers":18,"sex":19,"minAge":435,"maxAge":436,"enrollmentInfo":437,"targetDuration":4,"studyType":24,"phases":439,"briefSummary":440,"conditions":441,"keywords":444,"overallStatus":79,"whyStopped":4,"lastUpdateSubmitDate":447,"lastUpdatePostDateStruct":448,"startDateStruct":450,"completionDateStruct":452,"leadSponsor":454,"locationsCount":207},"100589057","effectiveness-of-large-language-model-for-anaesthesia-and-procedural-consent-100589057","NCT06949462","Effectiveness of Large Language Model for Anaesthesia and Procedural Consent","Evaluating the Effectiveness of Large Language Models in Anaesthesia and Procedural Consent: A Comparative Analysis With Traditional Patient Consent Methods","PEAR","Inclusion Criteria:\n\n\\- Adults (≥21 years old) undergoing elective surgery requiring anaesthesia\n\nClassified as ASA Physical Status I to III\n\n* Able to provide informed consent\n* Able to communicate effectively in English, Chinese (Mandarin), Malay, or Tamil\n* Willing and able to complete questionnaires and interact with the PEAR chatbot (intervention arm)\n\nExclusion Criteria:\n\n* ASA Physical Status IV or above\n* Cognitive impairment or psychiatric conditions that may limit comprehension or communication\n* Non-literate patients or those unable to understand English, Chinese, Malay, or Tamil\n* Emergency surgery cases\n* Prior participation in the study (to prevent bias)","21 Years","99 Years",{"count":438,"type":23},120,[26],"Patient understanding of anaesthesia risks remains inconsistent due to time constraints, language barriers, and variable clinician communication styles. Traditional verbal consent may not consistently ensure comprehension or reduce preoperative anxiety. PEAR (Patient Education of Anesthesia Risks) is a multilingual, AI-driven chatbot developed to enhance patient education and improve the quality of anaesthesia risk counselling.\n\nStudy Objective:\n\nTo compare PEAR's performance in delivering anaesthesia risk consent against the standard face-to-face verbal method.",[442,443,31],"Consent Forms","Anesthesia",[445,446,71,174],"Anaesthesia","Informed Consent","2026-04-30",{"date":449,"type":46},"2026-05-07",{"date":451,"type":46},"2026-01-07",{"date":453,"type":23},"2026-04-27",{"name":455,"class":53},"Singapore General Hospital",{"id":457,"slug":458,"hasResults":12,"nctId":459,"briefTitle":460,"officialTitle":460,"acronym":4,"eligibilityCriteria":461,"healthyVolunteers":12,"sex":19,"minAge":20,"maxAge":4,"enrollmentInfo":462,"targetDuration":4,"studyType":24,"phases":464,"briefSummary":465,"conditions":466,"keywords":467,"overallStatus":42,"whyStopped":4,"lastUpdateSubmitDate":469,"lastUpdatePostDateStruct":470,"startDateStruct":471,"completionDateStruct":473,"leadSponsor":475,"locationsCount":88},"100552474","identification-of-depressive-and-anxiety-symptoms-among-a-sample-of-emergency-department-patients-using-artificial-intelligence-ai-technology-100552474","NCT06473558","Identification of Depressive and Anxiety Symptoms Among a Sample of Emergency Department Patients Using Artificial Intelligence (AI) Technology","Inclusion Criteria:\n\n* Adult patients (age 18 and over) who presents to the UHCMC ED voluntarily with no obvious psychological disturbance by self-report or nurse\u002Fprovider initial assessment\n* English-speaking\n* Patients with non-emergent concerns of Emergency Severity Index (ESI) level 3, 4, or 5\n\nExclusion Criteria:\n\n* Prisoners\n* Patients who are deemed to be critically ill (including life or limb threatening illness) or unable to consent\n* Non-English Speaking",{"count":463,"type":23},30,[26],"Behavioral health problems, such as depression and anxiety, are common yet often are not identified by emergency department doctors and nurses. These mental health conditions can be due to medical issues or can worsen medical problems. One way investigators hope to do a better job of learning about mental health is by training Artificial Intelligence (AI) software to detect anxiety and depression by analyzing facial expression and tone of voice.\n\nParticipants are invited to participate in a study which may help improve emergency department care. An audio and video recording of the participant's responses to some simple, non-psychological questions will be analyzed by a computer to determine whether investigators can assess mood and anxiety by analyzing speech and visual patterns. The audio and video will not be listened to nor watched by study personnel, only analyzed by a computer. The investigator's hope is that it will help others in the future by aiding in the assessment of psychological state. This study is being conducted at CMC ED only.",[31],[468,318,74],"Depression","2026-04-21",{"date":453,"type":46},{"date":472,"type":23},"2026-07-01",{"date":474,"type":23},"2027-02-28",{"name":476,"class":53},"University Hospitals Cleveland Medical Center",{"id":478,"slug":479,"hasResults":12,"nctId":480,"briefTitle":481,"officialTitle":481,"acronym":482,"eligibilityCriteria":483,"healthyVolunteers":18,"sex":19,"minAge":20,"maxAge":4,"enrollmentInfo":484,"targetDuration":4,"studyType":24,"phases":486,"briefSummary":487,"conditions":488,"keywords":491,"overallStatus":79,"whyStopped":4,"lastUpdateSubmitDate":494,"lastUpdatePostDateStruct":495,"startDateStruct":497,"completionDateStruct":499,"leadSponsor":501,"locationsCount":88},"100589638","a-prospective-study-to-evaluate-the-performance-of-a-real-time-system-in-the-estimation-of-colorectal-polyp-size-100589638","NCT06957015","A Prospective Study to Evaluate the Performance of a Real-time System in the Estimation of Colorectal Polyp Size","EndoAIM","Inclusion Criteria:\n\nSubjects are eligible if:\n\n1. They have received colonoscopy for screening, surveillance or symptom investigation;\n2. Aged 18 years older or above;\n3. Written informed consent obtained.\n\nExclusion Criteria:\n\nSubjects are excluded if they have:\n\n1. Contraindication to colonoscopy (e.g. intestinal obstruction or perforation)\n2. Contraindication or conditions precluding polyp resection (e.g. active gastrointestinal bleeding, uninterrupted anticoagulation or dual antiplatelets)\n3. Advanced comorbid conditions (defined as American Society of Anesthesiologists grade 4 or above)",{"count":485,"type":23},278,[26],"Colorectal polyp size is related to the risk of exhibiting advanced histological features. Moreover, polyps larger than 10 mm are associated with an elevated risk of metachronous advanced neoplasia and colorectal cancer (CRC). Consequently, accurate measurement of polyp size, especially at the 10 mm threshold is critical for risk stratification and surveillance intervals. Furthermore, polyp size is also important for the choice of the appropriate resection procedures. Underestimation may lead to delayed diagnosis, thereby increasing the risk of colorectal cancer, while overestimation may result in unnecessary surveillance endoscopies.",[489,490,31],"Colonic Polyps","CRC (Colorectal Cancer)",[116,492,493],"polyp size measurement","surveillance intervals","2026-04-20",{"date":496,"type":46},"2026-04-23",{"date":498,"type":46},"2025-05-13",{"date":500,"type":23},"2028-01-01",{"name":502,"class":53},"Chinese University of Hong Kong",{"id":504,"slug":505,"hasResults":12,"nctId":506,"briefTitle":507,"officialTitle":508,"acronym":4,"eligibilityCriteria":509,"healthyVolunteers":18,"sex":309,"minAge":20,"maxAge":510,"enrollmentInfo":511,"targetDuration":4,"studyType":24,"phases":513,"briefSummary":514,"conditions":515,"keywords":4,"overallStatus":42,"whyStopped":4,"lastUpdateSubmitDate":520,"lastUpdatePostDateStruct":521,"startDateStruct":522,"completionDateStruct":524,"leadSponsor":525,"locationsCount":4},"100634607","artificial-intelligence-education-and-climate-awareness-in-pregnancy-100634607","NCT07541885","Artificial Intelligence Education and Climate Awareness in Pregnancy","The Effect of Artificial Intelligence-Assisted Climate Change Education on Pregnant Women's Climate Change Concerns and Awareness: A Quasi-Experimental Pretest-Posttest Study","Inclusion Criteria:\n\n* Aged 18 years or older\n* In the second or third trimester of pregnancy\n* Able to speak Turkish\n* Willing to participate and provide informed consent\n\nExclusion Criteria:\n\n* Presence of a high-risk pregnancy\n* Multiple pregnancy (e.g., twins or higher-order gestation)\n* Failure to complete either the pre-test or the post-test questionnaire(s)","35 Years",{"count":512,"type":23},82,[26],"This quasi-experimental pretest-posttest study aimed to evaluate the effect of artificial intelligence-assisted climate change education on pregnant women's climate change concerns and awareness. The study will be conducted with pregnant women attending a pregnancy school, and participants will be assigned to intervention and control groups. The intervention group will receive AI-supported climate change education in addition to routine training, while the control group will receive only routine education. Data will be collected using the Climate Change Anxiety Scale and the Maternal-Fetal Health Awareness of Climate Change Scale. The findings are expected to contribute to improving pregnant women's awareness and reducing concerns related to climate change through innovative educational approaches.",[516,517,518,519,31],"Climate Change","Maternal Health","Fetal Health","Midwifery","2026-04-14",{"date":469,"type":46},{"date":523,"type":23},"2026-04-01",{"date":472,"type":23},{"name":526,"class":53},"Derya Kaya Senol",{"id":528,"slug":529,"hasResults":12,"nctId":530,"briefTitle":531,"officialTitle":532,"acronym":4,"eligibilityCriteria":533,"healthyVolunteers":18,"sex":19,"minAge":20,"maxAge":4,"enrollmentInfo":534,"targetDuration":536,"studyType":64,"phases":4,"briefSummary":537,"conditions":538,"keywords":4,"overallStatus":42,"whyStopped":4,"lastUpdateSubmitDate":541,"lastUpdatePostDateStruct":542,"startDateStruct":544,"completionDateStruct":546,"leadSponsor":548,"locationsCount":88},"100633873","barriers-and-facilitators-to-nursing-record-with-ai-technology-application-100633873","NCT07532343","Barriers and Facilitators to Nursing Record With AI Technology Application","Barriers and Facilitators to Nursing Record With AI Technology Application: A Mixed Methods Study Using the Consolidated Framework for Implementation Research","Inclusion Criteria:\n\n* Nurses assigned to ward\n\nExclusion Criteria:\n\n* Nurses without record-keeping task\n* Those assigned to intensive care units, emergency room, or operating rooms",{"count":535,"type":23},271,"1 Year","The goal of this study is to examine the facilitators and barriers to the comprehensive implementation of AI technology in nursing documentation. The main questions it aims to answer are:\n\nWhat are facilitators to the comprehensive implementation of AI technology in nursing documentation? What are barriers to the comprehensive implementation of AI technology in nursing documentation? What strategies can help to fully utilize artificial intelligence technology in nursing documentation?",[539,31,540],"Implementation Research","Nursing Documentation Burden","2026-04-08",{"date":543,"type":46},"2026-04-15",{"date":545,"type":23},"2026-05-01",{"date":547,"type":23},"2027-04-30",{"name":549,"class":53},"National Yang Ming Chiao Tung University Hospital",{"id":551,"slug":552,"hasResults":12,"nctId":553,"briefTitle":554,"officialTitle":555,"acronym":4,"eligibilityCriteria":556,"healthyVolunteers":12,"sex":19,"minAge":20,"maxAge":4,"enrollmentInfo":557,"targetDuration":4,"studyType":24,"phases":559,"briefSummary":560,"conditions":561,"keywords":4,"overallStatus":79,"whyStopped":4,"lastUpdateSubmitDate":565,"lastUpdatePostDateStruct":566,"startDateStruct":568,"completionDateStruct":570,"leadSponsor":572,"locationsCount":574},"100584312","the-impact-of-artificial-intelligence-electrocardiography-on-occlusion-myocardial-infarction-management-under-the-value-based-payment-system-100584312","NCT06887699","The Impact of Artificial Intelligence Electrocardiography on Occlusion Myocardial Infarction Management Under the Value-Based Payment System","A Randomized Clinical Trial Investigating the Impact of Artificial Intelligence Electrocardiography on Occlusion Myocardial Infarction Management Under the Value-Based Payment System","Inclusion Criteria:\n\n* Patients in the emergency department\n* Patients received at least 1 ECG examination.\n\nExclusion Criteria:\n\n* The patients received ECG at the period of inactive AI-ECG system.\n* Patients with a history of coronary angiography within the past 3 days.",{"count":558,"type":23},212000,[26],"This trial will prospectively evaluate the impact of integrating AI-ECG within the pay-for-performance program on improving the diagnosis, treatment, and clinical outcomes of occlusion myocardial infarction patients by promoting accurate and timely diagnoses through financial incentives.",[562,31,563,564],"OMI - Occlusion Myocardial Infarction","Electrocardiogram","Cost-effectiveness Analysis","2026-04-06",{"date":567,"type":46},"2026-04-09",{"date":569,"type":46},"2025-08-01",{"date":571,"type":23},"2028-06-30",{"name":573,"class":53},"National Defense Medical Center, Taiwan",3,{"id":576,"slug":577,"hasResults":12,"nctId":578,"briefTitle":579,"officialTitle":580,"acronym":4,"eligibilityCriteria":581,"healthyVolunteers":18,"sex":19,"minAge":20,"maxAge":4,"enrollmentInfo":582,"targetDuration":4,"studyType":24,"phases":584,"briefSummary":585,"conditions":586,"keywords":590,"overallStatus":79,"whyStopped":4,"lastUpdateSubmitDate":591,"lastUpdatePostDateStruct":592,"startDateStruct":594,"completionDateStruct":596,"leadSponsor":597,"locationsCount":88},"100631825","comparing-original-patient-educational-materials-vs-ai-simplified-materials-to-improve-patient-comprehension-and-health-literacy-100631825","NCT07505719","Comparing Original Patient Educational Materials vs. AI-Simplified Materials to Improve Patient Comprehension and Health Literacy","AI-Simplified Patient Educational Materials: Investigating the Potential for Improved Patient Comprehension and Health Literacy","Inclusion Criteria:\n\n* Parents or guardians of pediatric patients receiving treatment at Hospital for Special Surgery\n\nExclusion Criteria:\n\n* Non-English Speaking\n* primary occupation is in healthcare\n* participants with prior knowledge on the condition pertaining to the material (osteogenesis imperfecta)",{"count":583,"type":23},80,[26],"Poor health literacy and patient comprehension have been associated with adverse health outcomes. Patient educational materials (PEMs) are articles that are intended to assist patients in their understanding of a given medical condition. Given that the average American adult reads at the 8th grade level, the American Medical Association and the Center for Disease Control recommend PEM be written at the 6th grade level. However, literature has found the majority of PEMs to be written significantly higher than the 8th grade level. In order to improve their readability, a number of studies have displayed the effectiveness of large language models (LLMs) such as ChatGPT to simplify the text of a given PEM. Despite the improvement in readability, the effectiveness of these simplified PEMs on improving patient comprehension of the AI augmented material has yet to be investigated.\n\nThe purpose of our study is to test whether the improvement in readability found in AI-simplified PEMs corresponds to a greater understanding of the material compared to the original PEM. Understanding if AI-simplified PEM truly improves comprehension could further support this use case for AI and aid providers and healthcare organizations in improving the health literacy of their patients.\n\nThis study aims to answer the following question:\n\nDo AI simplified PEMs improve the comprehension of pediatric orthopaedic conditions?\n\nResearchers will compare AI-simplified PEMs to their original, unmodified counterparts in order to see if there is any difference in post reading comprehension of the participants.\n\nParticipation in the study will include:\n\n* A brief baseline survey (e.g. demographics and educational attainment)\n* A randomly assigned reading of either the original PEM or the AI simplified version.\n* A 10 question post-reading multiple choice quiz",[31,587,588,589],"Health Literacy","Patient Comprehension","Patient Educational Material",[174,587,588,589],"2026-04-02",{"date":593,"type":46},"2026-04-03",{"date":595,"type":46},"2026-02-13",{"date":181,"type":23},{"name":598,"class":53},"Hospital for Special Surgery, New York",{"id":600,"slug":601,"hasResults":12,"nctId":602,"briefTitle":603,"officialTitle":604,"acronym":605,"eligibilityCriteria":606,"healthyVolunteers":18,"sex":309,"minAge":607,"maxAge":608,"enrollmentInfo":609,"targetDuration":4,"studyType":24,"phases":611,"briefSummary":612,"conditions":613,"keywords":616,"overallStatus":79,"whyStopped":4,"lastUpdateSubmitDate":523,"lastUpdatePostDateStruct":619,"startDateStruct":620,"completionDateStruct":622,"leadSponsor":624,"locationsCount":88},"100598760","screening-mammography-single-reading-by-one-radiologist-with-ai-vs-double-reading-by-two-radiologists-ai-bcsq-100598760","NCT07075679","Screening Mammography: Single Reading by One Radiologist With AI vs. Double Reading by Two Radiologists (AI-BCSQ)","Use of Artificial Intelligence in Breast Cancer Screening: Impact of AI-Assisted Single Reading by One Radiologist on Screening Quality Indicators Compared to Standard Double Reading by Two Radiologists Without AI","AI-BCSQ","Inclusion Criteria:\n\n* age 45-69, asymptomatic woman participating in breast cancer screening programme\n\nExclusion Criteria:\n\n* clinical signs of breast disease - indication for diagnostic mammography","45 Years","69 Years",{"count":610,"type":23},8000,[26],"A randomized prospective study comparing the evaluation of mammography images in a breast cancer screening programme by a single radiologist with AI support versus standard double reading by two radiologists without AI support.",[614,31,615],"Breast Cancer Screening","Breast Cancer Screening and Diagnosis",[617,618,116],"breast cancer","screening",{"date":591,"type":46},{"date":621,"type":46},"2025-10-06",{"date":623,"type":23},"2028-10-05",{"name":625,"class":53},"University Hospital Olomouc",{"id":627,"slug":628,"hasResults":12,"nctId":629,"briefTitle":630,"officialTitle":630,"acronym":4,"eligibilityCriteria":631,"healthyVolunteers":12,"sex":19,"minAge":20,"maxAge":4,"enrollmentInfo":632,"targetDuration":4,"studyType":64,"phases":4,"briefSummary":634,"conditions":635,"keywords":4,"overallStatus":79,"whyStopped":4,"lastUpdateSubmitDate":637,"lastUpdatePostDateStruct":638,"startDateStruct":640,"completionDateStruct":642,"leadSponsor":644,"locationsCount":88},"100598587","application-evaluation-research-on-the-artificial-intelligence-assisted-support-system-for-the-diagnosis-of-colorectal-tubular-adenoma-lesions-100598587","NCT07073430","Application Evaluation Research on the Artificial Intelligence-assisted Support System for the Diagnosis of Colorectal Tubular Adenoma Lesions","Inclusion Criteria:\n\n* Patients aged ≥ 18 years, who need to undergo colonoscopy, regardless of gender.\n* Voluntarily sign the informed consent form\n* Promise to abide by the research procedures and cooperate in the implementation of the entire research process.\n\nExclusion Criteria:\n\n* Patients who has a history of abdominal or pelvic surgery or radiotherapy in the past;\n* Patients who has definite active lower gastrointestinal bleeding.\n* Existing or suspected hereditary colorectal polyposis, inflammatory bowel disease;\n* Uncontrolled hypertension (systolic blood pressure \\> 160 mmHg or diastolic blood pressure \\> 95 mmHg after standardized treatment)\n* There is a history of stroke, coronary artery disease, or vascular disease;\n* Pregnant;\n* Intestinal preparation cannot be carried out.",{"count":633,"type":23},4000,"This study is a prospective,multi-center and observational clinical study.Investigators would like to innovatively construct a \"trinity\" database of colorectal tubular adenomas based on white light - magnifying chromo - pathological images.It simulates the decision - making logic of doctors, and based on the multimodal endoscopic LAFEQ method previously proposed, develop a multimodal deep - learning diagnostic model for colon adenomas and an interpretable risk prediction model for intestinal adenomas. While achieving high - precision auxiliary treatment decisions, clearly present the decision - making basis, and break through the limitation of poor interpretability of previous medical imaging AI models.",[636,31],"Colorectal Adenoma","2026-03-21",{"date":639,"type":46},"2026-03-25",{"date":641,"type":46},"2023-11-28",{"date":643,"type":23},"2026-10-31",{"name":645,"class":53},"Renmin Hospital of Wuhan University",{"id":647,"slug":648,"hasResults":12,"nctId":649,"briefTitle":650,"officialTitle":651,"acronym":652,"eligibilityCriteria":653,"healthyVolunteers":12,"sex":19,"minAge":20,"maxAge":4,"enrollmentInfo":654,"targetDuration":655,"studyType":64,"phases":4,"briefSummary":656,"conditions":657,"keywords":659,"overallStatus":42,"whyStopped":4,"lastUpdateSubmitDate":662,"lastUpdatePostDateStruct":663,"startDateStruct":665,"completionDateStruct":667,"leadSponsor":668,"locationsCount":88},"100630268","lymphoedema-diagnosis-and-treatment-100630268","NCT07485465","Lymphoedema Diagnosis and Treatment","The Role of Chat GPT in the Diagnosis and Treatment of Lymphedema","CDTL","Inclusion Criteria:\n\n* Patients over the age of 18\n* Clinical diagnosis of lymphoedema\n* Clinical diagnosis of lipoedema\n* Clinical diagnosis of venous insufficiency\n\nExclusion Criteria:\n\n* Lack of medical history\n* Lack of demographic data\n* Lack of clinical data and\n* Lack of imaging methods",{"count":7,"type":23},"1 Day","A domain-specific, custom-trained large language model for the differential diagnosis and treatment planning of lymphedema, lipedema, and venous insufficiency.",[658,31],"Lymphedema",[660,661,116],"lymphedema","chat gpt","2026-03-17",{"date":664,"type":46},"2026-03-20",{"date":666,"type":23},"2026-03-15",{"date":523,"type":23},{"name":669,"class":53},"Fatih Sultan Mehmet Training and Research Hospital"]