[{"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":29},{"pageToken":4,"total":5,"offset":6,"count":7,"results":8},null,56,0,25,[9,55,86,114,144,165,188,220,248,285,308,335,357,381,405,433,458,487,508,530,552,574,595,617,642],{"id":10,"slug":11,"hasResults":12,"nctId":13,"briefTitle":14,"officialTitle":15,"acronym":16,"eligibilityCriteria":17,"healthyVolunteers":18,"sex":19,"minAge":4,"maxAge":4,"enrollmentInfo":20,"targetDuration":4,"studyType":23,"phases":24,"briefSummary":26,"conditions":27,"keywords":32,"overallStatus":43,"whyStopped":4,"lastUpdateSubmitDate":44,"lastUpdatePostDateStruct":45,"startDateStruct":48,"completionDateStruct":50,"leadSponsor":52,"locationsCount":4},"100641792","ambient-audio-visual-capture-for-clinical-documentation-and-assessment-100641792",false,"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",true,"ALL",{"count":21,"type":22},60,"ESTIMATED","INTERVENTIONAL",[25],"NA","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.",[28,29,30,31],"Clinical Documentation","Artificial Intelligence","Surgical Education","Medical Education",[33,34,35,36,37,38,39,40,41,42],"ambient scribe","audio-visual capture","ward round","formative feedback","clinical assessment","AI documentation","inpatient","software as a medical device","inter-rater reliability","trainee assessment","NOT_YET_RECRUITING","2026-06-10",{"date":46,"type":47},"2026-06-16","ACTUAL",{"date":49,"type":22},"2026-09-01",{"date":51,"type":22},"2026-11-30",{"name":53,"class":54},"BlackFrame.ai","INDUSTRY",{"id":56,"slug":57,"hasResults":12,"nctId":58,"briefTitle":59,"officialTitle":60,"acronym":4,"eligibilityCriteria":61,"healthyVolunteers":18,"sex":19,"minAge":62,"maxAge":4,"enrollmentInfo":63,"targetDuration":4,"studyType":23,"phases":65,"briefSummary":66,"conditions":67,"keywords":74,"overallStatus":43,"whyStopped":4,"lastUpdateSubmitDate":76,"lastUpdatePostDateStruct":77,"startDateStruct":79,"completionDateStruct":80,"leadSponsor":82,"locationsCount":85},"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.","18 Years",{"count":64,"type":22},78,[25],"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",[68,69,70,71,29,72,73],"Nursing Process Competence","Artificial Intelligence Perception and Attitude","Nursing Education","Nursing Process","Clinical Competence","Artificial Intelligence (AI)",[29,71,70,72,75],"Attitude of Health Personnel","2026-05-24",{"date":78,"type":47},"2026-06-01",{"date":78,"type":22},{"date":81,"type":22},"2026-12-31",{"name":83,"class":84},"University of Yalova","OTHER",1,{"id":87,"slug":88,"hasResults":12,"nctId":89,"briefTitle":90,"officialTitle":91,"acronym":92,"eligibilityCriteria":93,"healthyVolunteers":12,"sex":94,"minAge":62,"maxAge":95,"enrollmentInfo":96,"targetDuration":4,"studyType":23,"phases":98,"briefSummary":99,"conditions":100,"keywords":4,"overallStatus":103,"whyStopped":4,"lastUpdateSubmitDate":104,"lastUpdatePostDateStruct":105,"startDateStruct":107,"completionDateStruct":109,"leadSponsor":111,"locationsCount":113},"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.","FEMALE","42 Years",{"count":97,"type":22},644,[25],"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.",[101,102,29],"Infertility","Ovarian Stimulation","RECRUITING","2026-04-21",{"date":106,"type":47},"2026-04-22",{"date":108,"type":47},"2026-04-08",{"date":110,"type":22},"2027-09",{"name":112,"class":84},"Fundacion Dexeus",5,{"id":115,"slug":116,"hasResults":12,"nctId":117,"briefTitle":118,"officialTitle":118,"acronym":119,"eligibilityCriteria":120,"healthyVolunteers":12,"sex":19,"minAge":62,"maxAge":4,"enrollmentInfo":121,"targetDuration":4,"studyType":23,"phases":123,"briefSummary":124,"conditions":125,"keywords":128,"overallStatus":43,"whyStopped":4,"lastUpdateSubmitDate":135,"lastUpdatePostDateStruct":136,"startDateStruct":138,"completionDateStruct":140,"leadSponsor":142,"locationsCount":85},"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":122,"type":22},400,[25],"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.",[126,127,29],"Atrial Fibrillation","Stroke",[129,130,131,132,133,134],"stroke","atrial fibrillation","artificial intelligence","risk prediction","electrocardiogram","non-vitamin K antagonist oral anticoagulants","2026-04-16",{"date":137,"type":47},"2026-04-20",{"date":139,"type":22},"2026-10-01",{"date":141,"type":22},"2028-12-31",{"name":143,"class":84},"National Taiwan University Hospital",{"id":145,"slug":146,"hasResults":12,"nctId":147,"briefTitle":148,"officialTitle":149,"acronym":4,"eligibilityCriteria":150,"healthyVolunteers":12,"sex":19,"minAge":4,"maxAge":4,"enrollmentInfo":151,"targetDuration":4,"studyType":152,"phases":4,"briefSummary":153,"conditions":154,"keywords":4,"overallStatus":43,"whyStopped":4,"lastUpdateSubmitDate":156,"lastUpdatePostDateStruct":157,"startDateStruct":159,"completionDateStruct":161,"leadSponsor":163,"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":22},"OBSERVATIONAL","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.",[29,155],"Usability","2026-03-23",{"date":158,"type":47},"2026-03-25",{"date":160,"type":22},"2026-07-01",{"date":162,"type":22},"2026-12-01",{"name":164,"class":84},"Willemijn Berkhout",{"id":166,"slug":167,"hasResults":12,"nctId":168,"briefTitle":169,"officialTitle":170,"acronym":171,"eligibilityCriteria":172,"healthyVolunteers":12,"sex":19,"minAge":62,"maxAge":4,"enrollmentInfo":173,"targetDuration":4,"studyType":152,"phases":4,"briefSummary":175,"conditions":176,"keywords":179,"overallStatus":103,"whyStopped":4,"lastUpdateSubmitDate":180,"lastUpdatePostDateStruct":181,"startDateStruct":183,"completionDateStruct":185,"leadSponsor":186,"locationsCount":85},"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":174,"type":22},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.",[177,178,29],"Pulmonary Embolism and Thrombosis","Deterioration, Clinical",[177,178,29],"2026-03-08",{"date":182,"type":47},"2026-03-11",{"date":184,"type":47},"2011-01-01",{"date":81,"type":22},{"name":187,"class":84},"Shengjing Hospital",{"id":189,"slug":190,"hasResults":12,"nctId":191,"briefTitle":192,"officialTitle":193,"acronym":4,"eligibilityCriteria":194,"healthyVolunteers":12,"sex":19,"minAge":62,"maxAge":4,"enrollmentInfo":195,"targetDuration":197,"studyType":152,"phases":4,"briefSummary":198,"conditions":199,"keywords":203,"overallStatus":43,"whyStopped":4,"lastUpdateSubmitDate":211,"lastUpdatePostDateStruct":212,"startDateStruct":214,"completionDateStruct":216,"leadSponsor":218,"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":196,"type":22},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.",[200,29,201,202],"Symptoms and Signs","Artificial Intelligence Mobile Application","Needs Assessment",[204,205,206,207,208,209,210],"WeChat-based AI companion","Tumor patients,","Cancer patients","Patient satisfaction","Symptom management","Emotional support","Clinical needs assessment","2026-02-01",{"date":213,"type":47},"2026-02-09",{"date":215,"type":22},"2026-01-14",{"date":217,"type":22},"2027-11-30",{"name":219,"class":84},"Cancer Institute and Hospital, Chinese Academy of Medical Sciences",{"id":221,"slug":222,"hasResults":12,"nctId":223,"briefTitle":224,"officialTitle":225,"acronym":226,"eligibilityCriteria":227,"healthyVolunteers":18,"sex":19,"minAge":62,"maxAge":4,"enrollmentInfo":228,"targetDuration":4,"studyType":23,"phases":230,"briefSummary":231,"conditions":232,"keywords":237,"overallStatus":103,"whyStopped":4,"lastUpdateSubmitDate":238,"lastUpdatePostDateStruct":239,"startDateStruct":241,"completionDateStruct":243,"leadSponsor":245,"locationsCount":247},"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":229,"type":22},444,[25],"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.",[233,234,235,29,236],"Contouring","Segmentation","Radiation Therapy","Deep Learning",[233,234,235,29,236],"2026-01-27",{"date":240,"type":47},"2026-01-29",{"date":242,"type":47},"2025-02-11",{"date":244,"type":22},"2030-04-30",{"name":246,"class":84},"Royal North Shore Hospital",3,{"id":249,"slug":250,"hasResults":12,"nctId":251,"briefTitle":252,"officialTitle":252,"acronym":253,"eligibilityCriteria":254,"healthyVolunteers":12,"sex":19,"minAge":62,"maxAge":4,"enrollmentInfo":255,"targetDuration":4,"studyType":23,"phases":257,"briefSummary":258,"conditions":259,"keywords":267,"overallStatus":43,"whyStopped":4,"lastUpdateSubmitDate":276,"lastUpdatePostDateStruct":277,"startDateStruct":279,"completionDateStruct":281,"leadSponsor":283,"locationsCount":85},"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":256,"type":22},770,[25],"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.",[260,261,29,262,263,264,265,266],"Drug-related Iatrogenesis","Emergency Department","Clinical Decision Support","Prescription","Transcription","Medication","Reconciliation",[268,269,131,270,271,272,273,274,275],"Drug-related iatrogenesis","emergency department","clinical decision support","randomized trial","medication","reconciliation","prescription","transcription","2026-01-06",{"date":278,"type":47},"2026-01-08",{"date":280,"type":22},"2026-01",{"date":282,"type":22},"2027-01",{"name":284,"class":84},"Centre Hospitalier Universitaire, Amiens",{"id":286,"slug":287,"hasResults":12,"nctId":288,"briefTitle":289,"officialTitle":289,"acronym":4,"eligibilityCriteria":290,"healthyVolunteers":12,"sex":94,"minAge":62,"maxAge":291,"enrollmentInfo":292,"targetDuration":4,"studyType":152,"phases":4,"briefSummary":294,"conditions":295,"keywords":4,"overallStatus":103,"whyStopped":4,"lastUpdateSubmitDate":299,"lastUpdatePostDateStruct":300,"startDateStruct":302,"completionDateStruct":304,"leadSponsor":306,"locationsCount":85},"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":293,"type":22},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.",[296,29,297,298],"Infertility, Female","Quality of Life","Anxiety","2025-12-17",{"date":301,"type":47},"2025-12-29",{"date":303,"type":47},"2025-10-23",{"date":305,"type":22},"2026-10-23",{"name":307,"class":84},"Acibadem University",{"id":309,"slug":310,"hasResults":12,"nctId":311,"briefTitle":312,"officialTitle":313,"acronym":4,"eligibilityCriteria":314,"healthyVolunteers":12,"sex":19,"minAge":62,"maxAge":315,"enrollmentInfo":316,"targetDuration":4,"studyType":23,"phases":318,"briefSummary":319,"conditions":320,"keywords":323,"overallStatus":43,"whyStopped":4,"lastUpdateSubmitDate":327,"lastUpdatePostDateStruct":328,"startDateStruct":330,"completionDateStruct":331,"leadSponsor":333,"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.","70 Years",{"count":317,"type":22},576,[25],"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.",[29,321,322],"Colonoscopy","Real-time Feedback",[321,324,325,326],"Intubation","Real-time feedback","Red-out","2025-11-27",{"date":329,"type":47},"2025-12-10",{"date":329,"type":22},{"date":332,"type":22},"2028-04-20",{"name":334,"class":84},"The First Affiliated Hospital of Anhui Medical University",{"id":336,"slug":337,"hasResults":12,"nctId":338,"briefTitle":339,"officialTitle":339,"acronym":4,"eligibilityCriteria":340,"healthyVolunteers":12,"sex":19,"minAge":341,"maxAge":342,"enrollmentInfo":343,"targetDuration":4,"studyType":152,"phases":4,"briefSummary":345,"conditions":346,"keywords":4,"overallStatus":103,"whyStopped":4,"lastUpdateSubmitDate":327,"lastUpdatePostDateStruct":349,"startDateStruct":351,"completionDateStruct":353,"leadSponsor":355,"locationsCount":247},"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","40 Years","75 Years",{"count":344,"type":22},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).",[347,29,348],"Intestinal Metaplasia of Gastric Mucosa","Endoscopy",{"date":350,"type":47},"2025-12-02",{"date":352,"type":47},"2025-11-01",{"date":354,"type":22},"2027-12-31",{"name":356,"class":84},"Qilu Hospital of Shandong University",{"id":358,"slug":359,"hasResults":12,"nctId":360,"briefTitle":361,"officialTitle":362,"acronym":4,"eligibilityCriteria":363,"healthyVolunteers":12,"sex":19,"minAge":341,"maxAge":4,"enrollmentInfo":364,"targetDuration":4,"studyType":23,"phases":366,"briefSummary":367,"conditions":368,"keywords":370,"overallStatus":103,"whyStopped":4,"lastUpdateSubmitDate":373,"lastUpdatePostDateStruct":374,"startDateStruct":376,"completionDateStruct":378,"leadSponsor":379,"locationsCount":85},"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":365,"type":22},1000,[25],"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.",[369,29],"Gastric Neoplasm",[369,371,372],"Artificial intelligence","Miss rate","2025-11-19",{"date":375,"type":47},"2025-11-20",{"date":377,"type":47},"2024-11-01",{"date":81,"type":22},{"name":380,"class":84},"The University of Hong Kong",{"id":382,"slug":383,"hasResults":12,"nctId":384,"briefTitle":385,"officialTitle":385,"acronym":386,"eligibilityCriteria":387,"healthyVolunteers":12,"sex":19,"minAge":62,"maxAge":315,"enrollmentInfo":388,"targetDuration":4,"studyType":23,"phases":389,"briefSummary":391,"conditions":392,"keywords":4,"overallStatus":43,"whyStopped":4,"lastUpdateSubmitDate":396,"lastUpdatePostDateStruct":397,"startDateStruct":399,"completionDateStruct":401,"leadSponsor":403,"locationsCount":4},"100553930","phase-2-glioma-adaptive-radiotherapy-with-development-of-an-artificial-intelligence-workflow-100553930","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.",{"count":21,"type":22},[390],"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.",[393,394,395,29],"Diffuse Glioma","Glioblastoma","Adaptive Radiotherapy","2025-09-15",{"date":398,"type":47},"2025-09-16",{"date":400,"type":22},"2025-11-30",{"date":402,"type":22},"2028-07-30",{"name":404,"class":84},"Tata Memorial Centre",{"id":406,"slug":407,"hasResults":12,"nctId":408,"briefTitle":409,"officialTitle":410,"acronym":4,"eligibilityCriteria":411,"healthyVolunteers":18,"sex":19,"minAge":412,"maxAge":413,"enrollmentInfo":414,"targetDuration":4,"studyType":23,"phases":416,"briefSummary":417,"conditions":418,"keywords":422,"overallStatus":43,"whyStopped":4,"lastUpdateSubmitDate":424,"lastUpdatePostDateStruct":425,"startDateStruct":427,"completionDateStruct":429,"leadSponsor":431,"locationsCount":85},"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":415,"type":22},50,[25],"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.",[419,420,421,29],"Autism Spectrum Disorder","Child","Oral Health",[423],"Autism Spectrum Disorder, Toothbrushing, Oral Hygiene, Artificial Intelligence.","2025-08-29",{"date":426,"type":47},"2025-09-08",{"date":428,"type":22},"2025-10-01",{"date":430,"type":22},"2027-02-10",{"name":432,"class":84},"University of Sao Paulo",{"id":434,"slug":435,"hasResults":12,"nctId":436,"briefTitle":437,"officialTitle":437,"acronym":4,"eligibilityCriteria":438,"healthyVolunteers":18,"sex":19,"minAge":62,"maxAge":4,"enrollmentInfo":439,"targetDuration":4,"studyType":23,"phases":441,"briefSummary":442,"conditions":443,"keywords":447,"overallStatus":43,"whyStopped":4,"lastUpdateSubmitDate":450,"lastUpdatePostDateStruct":451,"startDateStruct":452,"completionDateStruct":454,"leadSponsor":456,"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":440,"type":22},200,[25],"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.",[444,445,446,29],"Health Literacy","Diagnostic Imaging","Imaging Results",[448,449,29],"Patient-centered Understanding","Radiology Reports","2025-08-28",{"date":426,"type":47},{"date":453,"type":22},"2026-12",{"date":455,"type":22},"2028-12",{"name":457,"class":84},"Harvard Medical School (HMS and HSDM)",{"id":459,"slug":460,"hasResults":12,"nctId":461,"briefTitle":462,"officialTitle":463,"acronym":464,"eligibilityCriteria":465,"healthyVolunteers":18,"sex":19,"minAge":62,"maxAge":4,"enrollmentInfo":466,"targetDuration":4,"studyType":23,"phases":467,"briefSummary":468,"conditions":469,"keywords":474,"overallStatus":103,"whyStopped":4,"lastUpdateSubmitDate":477,"lastUpdatePostDateStruct":478,"startDateStruct":480,"completionDateStruct":482,"leadSponsor":484,"locationsCount":486},"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":440,"type":22},[25],"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",[29,470,471,472,473],"Ultrasonography","Elasticity Imaging Techniques","Liver Diseases","Metastasis to Liver",[475,476],"Quantitative Ultrasound","Radiofrequency Data","2025-08-18",{"date":479,"type":47},"2025-08-20",{"date":481,"type":47},"2024-04-01",{"date":483,"type":22},"2025-12",{"name":485,"class":84},"Technische Universität Dresden",4,{"id":488,"slug":489,"hasResults":12,"nctId":490,"briefTitle":491,"officialTitle":492,"acronym":4,"eligibilityCriteria":493,"healthyVolunteers":12,"sex":19,"minAge":62,"maxAge":4,"enrollmentInfo":494,"targetDuration":4,"studyType":152,"phases":4,"briefSummary":495,"conditions":496,"keywords":4,"overallStatus":103,"whyStopped":4,"lastUpdateSubmitDate":477,"lastUpdatePostDateStruct":499,"startDateStruct":501,"completionDateStruct":503,"leadSponsor":505,"locationsCount":507},"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":415,"type":22},"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.",[29,497,498],"Endobronchial Ultrasound","Lung Cancer",{"date":500,"type":47},"2025-08-22",{"date":502,"type":47},"2023-05-01",{"date":504,"type":22},"2027-12-01",{"name":506,"class":84},"Norwegian University of Science and Technology",2,{"id":509,"slug":510,"hasResults":12,"nctId":511,"briefTitle":512,"officialTitle":512,"acronym":4,"eligibilityCriteria":513,"healthyVolunteers":12,"sex":19,"minAge":514,"maxAge":342,"enrollmentInfo":515,"targetDuration":4,"studyType":152,"phases":4,"briefSummary":517,"conditions":518,"keywords":4,"overallStatus":103,"whyStopped":4,"lastUpdateSubmitDate":520,"lastUpdatePostDateStruct":521,"startDateStruct":523,"completionDateStruct":525,"leadSponsor":527,"locationsCount":247},"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.","20 Years",{"count":516,"type":22},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.",[29,519],"Lung Nodule","2025-07-30",{"date":522,"type":47},"2025-08-01",{"date":524,"type":47},"2025-06-01",{"date":526,"type":22},"2025-10-30",{"name":528,"class":529},"Anhui Provincial Hospital","OTHER_GOV",{"id":531,"slug":532,"hasResults":12,"nctId":533,"briefTitle":534,"officialTitle":535,"acronym":4,"eligibilityCriteria":536,"healthyVolunteers":12,"sex":19,"minAge":62,"maxAge":342,"enrollmentInfo":537,"targetDuration":4,"studyType":152,"phases":4,"briefSummary":539,"conditions":540,"keywords":4,"overallStatus":103,"whyStopped":4,"lastUpdateSubmitDate":543,"lastUpdatePostDateStruct":544,"startDateStruct":546,"completionDateStruct":548,"leadSponsor":550,"locationsCount":85},"100505505","a-real-world-study-of-the-efficacy-and-safety-of-icis-as-first-line-therapy-for-advanced-malignancies-100505505","NCT05862259","A Real-world Study of the Efficacy and Safety of ICIs as First-line Therapy for Advanced Malignancies","A Multicenter, Multiomics, Non-interventional, Real-world Study of the Efficacy and Safety of Immune Checkpoint Inhibitors as First-line Therapy for Advanced Malignancies","Inclusion Criteria:\n\n1. Informed consent has been signed and, in the judgment of the investigator, the patient is able to comply with the study protocol and sign a written informed consent.\n2. the advanced malignant tumors (solid tumors of the non-small-cell lung carcinoma, stomach, breast, urinary system, etc.) were diagnosed by histopathology.\n3. the stage IV according to the eighth edition of IASLC.\n4. PS 0-2, the expected survival \\> 3 months.\n5. the age of 18-75 years.\n6. no contraindication to treatment with immune checkpoint inhibitors.\n\nExclusion Criteria:\n\n1. the patients' compliance was poor, which violated the rules of the trial;\n2. the patients with severe dysfunction of vital organs (heart, liver and kidney) ;\n3. the patients with other malignant tumors;\n4. the researchers considered that the patients should not participate in other conditions of the trial.",{"count":538,"type":22},120,"In this study, we collected the data of immunohistochemistry, gene detection, image, OS, PFS, Orr, and so on. Secondly, the database of immunotherapy for malignant tumor was established, and the predictive model was constructed to verify and establish the rationality and validity of the biomarkers and predictive system of immunotherapy",[541,542,29],"Malignant Tumor","Immunotherapy","2025-07-11",{"date":545,"type":47},"2025-07-14",{"date":547,"type":47},"2023-08-10",{"date":549,"type":22},"2025-12-31",{"name":551,"class":84},"Xinqiao Hospital of Chongqing",{"id":553,"slug":554,"hasResults":12,"nctId":555,"briefTitle":556,"officialTitle":556,"acronym":4,"eligibilityCriteria":557,"healthyVolunteers":18,"sex":19,"minAge":4,"maxAge":4,"enrollmentInfo":558,"targetDuration":4,"studyType":152,"phases":4,"briefSummary":560,"conditions":561,"keywords":564,"overallStatus":103,"whyStopped":4,"lastUpdateSubmitDate":565,"lastUpdatePostDateStruct":566,"startDateStruct":568,"completionDateStruct":570,"leadSponsor":572,"locationsCount":486},"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.",{"count":559,"type":22},10000,"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.",[562,563,29],"Pulmonary Diseases","Ophthalmological Diagnostic Techniques",[562,563,371],"2025-05-19",{"date":567,"type":47},"2025-05-23",{"date":569,"type":47},"2020-06-29",{"date":571,"type":22},"2026-05",{"name":573,"class":84},"Zhongshan Ophthalmic Center, Sun Yat-sen University",{"id":575,"slug":576,"hasResults":12,"nctId":577,"briefTitle":578,"officialTitle":579,"acronym":4,"eligibilityCriteria":580,"healthyVolunteers":12,"sex":19,"minAge":4,"maxAge":4,"enrollmentInfo":581,"targetDuration":4,"studyType":152,"phases":4,"briefSummary":583,"conditions":584,"keywords":4,"overallStatus":103,"whyStopped":4,"lastUpdateSubmitDate":587,"lastUpdatePostDateStruct":588,"startDateStruct":590,"completionDateStruct":591,"leadSponsor":593,"locationsCount":85},"100591278","research-on-early-prediction-model-of-ischemic-cerebrovascular-disease-based-on-artificial-intelligence-technology-100591278","NCT06978348","Research on Early Prediction Model of Ischemic Cerebrovascular Disease Based on Artificial Intelligence Technology.","Establish an Artificial Intelligence Clinical Decision Support System for Patients With Carotid\u002FVertebral Artery Stenosis.","Inclusion Criteria:\n\nPatients undergoing vascular (carotid\u002Fvertebral artery) B-ultrasound\n\nExclusion Criteria:\n\nPatients with missing clinical data such as medical history, cerebrovascular ultrasound results and biochemical data",{"count":582,"type":22},244296,"Establish an artificial intelligence clinical decision support system for patients with carotid\u002Fvertebral artery cerebrovascular stenosis, early identification of patients who may have cerebral infarction. With the support of this project, it is expected that a secondary prevention clinical decision support system for chronic stroke will be established, which is likely to become an important auxiliary tool for the management of cerebrovascular diseases in the future.",[585,29,586],"Ischemic Cerebrovascular Disease","Prediction Model","2025-05-10",{"date":589,"type":47},"2025-05-18",{"date":587,"type":47},{"date":592,"type":22},"2025-06-10",{"name":594,"class":84},"Shanghai Jiao Tong University School of Medicine",{"id":596,"slug":597,"hasResults":12,"nctId":598,"briefTitle":599,"officialTitle":600,"acronym":4,"eligibilityCriteria":601,"healthyVolunteers":18,"sex":19,"minAge":62,"maxAge":4,"enrollmentInfo":602,"targetDuration":4,"studyType":152,"phases":4,"briefSummary":604,"conditions":605,"keywords":4,"overallStatus":103,"whyStopped":4,"lastUpdateSubmitDate":608,"lastUpdatePostDateStruct":609,"startDateStruct":611,"completionDateStruct":613,"leadSponsor":615,"locationsCount":85},"100584603","evaluating-an-ai-generated-health-podcast-100584603","NCT06891495","Evaluating an AI-Generated Health Podcast","Feasibility and Validity Assessment of an AI-Generated Health Podcast: A Pilot Observational Study","Expert Group\n\nInclusion Criteria:\n\n* Licensed professionals and recognized experts in relevant fields such as dentistry, public health, and health communication\n* Minimum of 5 years of professional experience in their respective fields\n* Prior involvement in healthcare education, research, or digital health communication\n* Willingness to review and provide detailed feedback on the adapted questionnaire\n* Ability to complete the survey in English\n\nExclusion Criteria:\n\n* Professionals without formal training or relevant expertise in the specified fields\n* Individuals with conflicts of interest that might compromise the objectivity of their evaluations\n* Experts who are unable to commit the necessary time to provide thorough feedback\n\nGeneral Public Group\n\nInclusion Criteria:\n\n* Adults aged 18 years or older\n* Individuals fluent in English\n* Access to a digital audio device and an internet connection to listen to the podcast and complete the survey\n* Consent to participate in the study\n\nExclusion Criteria:\n\n* Individuals with significant hearing impairments that might hinder the ability to comprehend the audio content\n* Healthcare professionals or individuals with advanced training in dentistry or health communication\n* Persons who have participated in similar educational podcast studies previously",{"count":603,"type":22},30,"This study aims to explore a new way of delivering health information using an AI-generated podcast. The podcast, created with Google NotebookLM, uses verified content from the American Academy of Periodontology website to provide easy-to-understand information on gum health and prevention.\n\nThe goal is to determine whether this AI-generated podcast is a useful, engaging, and clear tool for educating the general public about health topics. Traditional health podcasts often feature expert interviews and can be lengthy, which sometimes limits their appeal and accessibility. By using AI to generate the podcast, investigator hope to offer a more standardized and concise presentation that avoids technical jargon.\n\nTo evaluate the podcast, investigator developed a questionnaire based on the Questionnaire for Assessing Educational Podcasts (QAEP). This questionnaire was adapted to better suit a non-specialist audience and covers four key areas: how easy the podcast is to access and use, the design and structure of the podcast, the clarity and completeness of the content, and the podcast's value as a learning tool.\n\nBefore using this questionnaire with the general public, investigator sent it to 10 experts in dentistry, public health, and communication for their review and feedback. Their input helped us make minor modifications to ensure the questionnaire is both clear and scientifically sound. After these revisions, investigator conducted a pilot study with 30 members of the general public who listened to the podcast and completed the questionnaire.\n\nThis study will assess the feasibility and validity of using an AI-generated podcast as a health education tool. The results will help determine if this approach can effectively improve public understanding of health information and may guide the future design of digital health communication strategies.",[606,29,607],"Public Health","Feasibility Studies","2025-03-22",{"date":610,"type":47},"2025-03-26",{"date":612,"type":47},"2025-02-07",{"date":614,"type":22},"2025-05-20",{"name":616,"class":84},"Qassim University",{"id":618,"slug":619,"hasResults":12,"nctId":620,"briefTitle":621,"officialTitle":622,"acronym":4,"eligibilityCriteria":623,"healthyVolunteers":18,"sex":19,"minAge":4,"maxAge":4,"enrollmentInfo":624,"targetDuration":4,"studyType":23,"phases":626,"briefSummary":627,"conditions":628,"keywords":631,"overallStatus":43,"whyStopped":4,"lastUpdateSubmitDate":634,"lastUpdatePostDateStruct":635,"startDateStruct":637,"completionDateStruct":639,"leadSponsor":640,"locationsCount":4},"100582475","chatgpt-assisted-hypertension-knowledge-in-nursing-students-100582475","NCT06863792","ChatGPT-Assisted Hypertension Knowledge in Nursing Students","ChatGPT-Assisted Hypertension Knowledge in Nursing Students: Assessment of Accuracy, AI Anxiety, and Cognitive Load","Inclusion Criteria:\n\n* To be enrolled in the nursing program at a private university, during the 2024-2025 academic year.\n* To have taken the Internal Medicine Nursing course (In this course, students receive 4 hours of theoretical lessons on nursing care for hypertension patients).\n* To be willing to volunteer for participation in the study.\n\nExclusion Criteria:\n\n* Students who wish to withdraw from the research at any stage will not be included in the study.",{"count":625,"type":22},96,[25],"ChatGPT provides quick access to information, research support, and study materials, but concerns remain regarding its reliability, accuracy, and inability to offer personalized care principles essential in nursing. Although previous studies show its high accuracy in clinical responses, over-reliance on AI-generated medical information necessitates cautious use. The study will explore both the benefits and limitations of ChatGPT in nursing education, particularly in hypertension learning.",[629,29,630],"Nursing Students","Hypertension",[632,131,630,633],"nursing students","nursing education","2025-03-03",{"date":636,"type":47},"2025-03-07",{"date":638,"type":22},"2025-03-10",{"date":587,"type":22},{"name":641,"class":84},"Ankara University",{"id":643,"slug":644,"hasResults":12,"nctId":645,"briefTitle":646,"officialTitle":646,"acronym":4,"eligibilityCriteria":647,"healthyVolunteers":12,"sex":19,"minAge":291,"maxAge":648,"enrollmentInfo":649,"targetDuration":4,"studyType":23,"phases":651,"briefSummary":652,"conditions":653,"keywords":654,"overallStatus":103,"whyStopped":4,"lastUpdateSubmitDate":660,"lastUpdatePostDateStruct":661,"startDateStruct":663,"completionDateStruct":665,"leadSponsor":667,"locationsCount":85},"100458027","artificial-intelligence-ai-assisted-real-time-adenoma-detection-and-classification-during-colonoscopies-100458027","NCT05244278","Artificial Intelligence (AI) Assisted Real-time Adenoma Detection and Classification During Colonoscopies","Inclusion Criteria:\n\n* indication of undergoing a screening, surveillance, or diagnostic colonoscopy\n* Age 45-89 years\n\nExclusion Criteria:\n\n* Patients undergoing emergency colonoscopy\n* Patients with a known familial polyposis syndrome or a known inflammatory bowel disease","89 Years",{"count":650,"type":22},1596,[25],"This is a pragmatic, double-blind, randomized, controlled trial, to evaluate the effect of implementing a Computer-assisted detection (CADe) system within the routine clinical practice of Canadian healthcare institutions.\n\nThe main hypothesis of this study is that the ADR in the operating room equipped with the GI genius CADe system will be significantly higher than the ADR in the ordinary operating room.",[29],[655,656,657,658,659],"Polyp detection","Adenoma detection","Adenoma per colonoscopy","Sessile serrated lesion detection rate","Sessile serrated lesion per colonoscopy","2025-02-28",{"date":662,"type":47},"2025-03-05",{"date":664,"type":47},"2023-08-01",{"date":666,"type":22},"2027-01-01",{"name":668,"class":84},"Centre hospitalier de l'Université de Montréal (CHUM)"]