moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Expansion of Integrated AI Solution for Diabetic Retinopathy Screening in Thailand: An Implementation Research
Trial ID: NCT05527535
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-10-03
Completion date: 2023-09-30
Sponsor: Department of Medical Services Ministry of Public Health of Thailand
Collaborators: Health Systems Research Institute,Thailand
Conditions: Diabetic Retinopathy
Interventions: Diabetic retinopathy screening by artificial intelligence; Diabetic retinopathy screening by healthcare personnel
Enrollment: 34500
Age range: 18 Years - None
Sex: ALL

Summary:
Efficiency and effectiveness of real-world diabetic retinopathy screening by artificial intelligent (AI) are limited. Investigators will implement AI for diabetic retinopathy screening in 13 health districts in Thailand and investigate the efficiency, effectiveness as well as patients and health care personnel's satisfaction by an implementation research.

Eligibility criteria:
Inclusion Criteria:

1. Type 1 or 2 diabetes mellitus patients whose name are in primary hospital record
2. No full-time ophthalmologists in those primary hospital
3. Age more than or equal to 18 years
4. Eligible for fundus photo imaging at least 1 eye

Exclusion Criteria:

1. Type 1 or 2 diabetes mellitus patients whose name are in primary hospital record that have full-time ophthalmologists
2. Patients who previously diagnosed with other causes of macular edema, for example, Age-related Macular Degeneration, Radiation Retinopathy, Retinal Vein Occlusion etc.
3. History of retinal laser or surgery
4. Other ocular diseases that require referral to ophthalmologists
5. Not eligible for fundus photo imaging for both eyes (any causes)

Outcomes:
Effectiveness of AI in diabetic retinopathy screening; Efficiency of AI in diabetic retinopathy screening; Efficiency of AI in diabetic retinopathy screening
Intelligent ICU of the Future Subtitles: *Autonomous Pain Recognition in Non-Verbal and Critically Ill Patients *Fundamental Intelligent Building Blocks of the Intensive Care Unit (ICU) of the Future *Intelligent Intensive Care Unit (I2CU): Pervasive Sensing and Artificial Intelligence for Augmented Clinical Decision-making *ADAPT: Autonomous Delirium Monitoring and Adaptive Prevention
Trial ID: NCT03905668
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2016-02-03
Completion date: 2028-07
Sponsor: University of Florida
Collaborators: National Institute for Biomedical Imaging and Bioengineering (NIBIB); U.S. National Science Foundation; National Institutes of Health (NIH); National Institute of Neurological Disorders and Stroke (NINDS)
Conditions: Pain; Delirium
Interventions: Video Monitoring; Accelerometer Monitoring; Electromyographic Monitoring; Noise Level Monitoring; Light Level Monitoring
Locations: UF Health Shands Hospital
Countries: United States
Enrollment: 71
Age range: 18 Years - 100 Years
Sex: ALL

Summary:
The objective of this project is to create deep learning and machine learning models capable of recognizing patient visual cues, including facial expressions such as pain and functional activity. Many important details related to the visual assessment of patients, such as facial expressions like pain, head and extremity movements, posture, and mobility are captured sporadically by overburdened nurses or are not captured at all. Consequently, these important visual cues, although associated with critical indices, such as physical functioning, pain, and impending clinical deterioration, often cannot be incorporated into clinical status. The study team will develop a sensing system to recognize facial and body movements as patient visual cues. As part of a secondary evaluation method the study team will assess the models ability to detect delirium.

Eligibility criteria:
ICU Patients:

Inclusion Criteria:

* patient admitted to University of Florida (UF) Health Gainesville ICU

Exclusion Criteria:

* Anticipated ICU stay is less than one day
* Patient is on any form of contact precaution or isolation
* Patient is unable to wear a Shimmer3 unit

ICU Patient Friends/Family:

Inclusion Criteria:

* Individual has their name designated on a patient's informed consent form giving them permission to view and modify facial and activity data collected about that patient

Exclusion Criteria:

* Age \< 18
* They are unable to answer short questions on a touch screen display
* They are unable to wear a proximity sensor
* They were not on the listed of designated individuals specified in their friend/family members informed consent form

Outcomes:
Defense Veterans Pain Reporting Scale (DVPRS)
USING ARTIFICIAL INTELLIGENCE FOR MASS SCREENING OF THE DIABETIC RETINOPATHY
Trial ID: NCT06112691
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-02-01
Completion date: 2024-11
Sponsor: The Filatov Institute of Eye Diseases and Tissue Therapy
Collaborators: Komisarenko Institute of Endocrinology and Metobolism; CheckEye LLC
Conditions: Diabetic Retinopathy
Interventions: taking fundus photos using non-mydriatic fundus camera - FundusScope Rodenstock
Locations: The Filatov Institute of Eye Diseases and Tissue Therapy
Countries: Ukraine
Enrollment: 660
Age range: 18 Years - 90 Years
Sex: ALL

Summary:
The purpose of this study is to create a patient-centric environment for early detection of DR with AI-driven solutions.

Eligibility criteria:
Inclusion Criteria:

1. Documented diagnosis of Diabetes mellitus as defined by:

   A. Having met the criteria established by either the World Health Organization (WHO) or the American Diabetes Association (ADA) B. Hemoglobin A1c (HbA1c)\>= 6.5% based on repeated assessments C. Fasting Plasma Glucose (FPG) \>= 126 mg/dL (7.0 mmol/L) based on repeated assessments D. Oral Glucose Tolerance test with 2 hr plasma glucose \>= 200 mg/dL (11.1mmol/L) using equivalent of 75g anhydrous glucose dose in water.

   E. Symptoms of hyperglycemia or hyperglycemic crisis with random plasma glucose \>=200mg/dL (11.1 mmol/L).
2. Understanding of the Study and willingness and ability to sign informed consent
3. Patient age 18 or above
4. Diagnostic for diabetes: 4a) Type 1 diabetes of a lest 5 years of evolution; or 4b) Type 2 diabetes

   \-

Exclusion Criteria:

1. Patients under 18 years of age;
2. Failure to give informed consent;
3. Presence of retinal diseases - acquired disease: age-related macular degeneration (AMD), occlusion of retinal vessels (ORV), etc.; birth defects: coloboma of choroid or optic nerve disc, etc.; hereditary diseases: retinitis pigmentosa, angioid streaks of the retina, etc.
4. A patient who has already undergone treatment (surgery, laser, etc.) for any disease of the retina: age-related macular degeneration (AMD), retinal vascular occlusion (ARV), etc. These patients should be excluded or allocated to a separate group.

Outcomes:
The accuracy
Dual Tandem Study Comparing the Adenoma Detection and Miss-rate of Standard Colonoscopy to That of Artificial Intelligence (CAD-EYE) and to That of Artificial Intelligence (CAD-EYE) and G-EYE® Aided Colonoscopy
Trial ID: NCT05237310
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-02-02
Completion date: 2022-09
Sponsor: Smart Medical Systems Ltd.
Collaborators: Fujifilm
Conditions: Colorectal Cancer; Adenoma
Interventions: Standard Colonoscopy; G-EYE Colonoscopy; CAD-EYE
Locations: Helios Dr. Horst Schmidt Kliniken Wiesbaden GmbH
Countries: Germany
Enrollment: 372
Age range: 18 Years - None
Sex: ALL

Summary:
The purpose of this study is to compare the additional diagnostic yield over Standard Colonoscopy (i.e., the adenoma miss-rate reduction) obtained by performing CADEYE and G-EYE® aided colonoscopy, vs. the additional diagnostic yield over Standard Colonoscopy (i.e., the adenoma miss-rate reduction) obtained by performing CAD-EYE aided colonoscopy.

Eligibility criteria:
Inclusion Criteria:

1. Screening and surveillance population for Adenoma and CRC.
2. Subjects age is at least 18 years
3. The patient must understand and sign a written informed consent for the procedure.

Exclusion Criteria:

1. Subjects with inflammatory bowel disease;
2. Subjects with a personal history of hereditary polyposis syndrome;
3. Subjects with suspected chronic stricture potentially precluding complete colonoscopy;
4. Subjects with diverticulitis or toxic megacolon;
5. Subjects with prior colonic surgery (exclusion appendectomy)
6. Subjects with a history of radiation therapy to abdomen or pelvis;
7. Pregnant or lactating female subjects;
8. Subjects who are currently enrolled in another clinical investigation.
9. Subjects with current oral or parenteral use of anticoagulants, not considered eligible by the investigator.
10. Subjects with recent (within the last 3 mounts) coronary ischemia or CVA (stroke)
11. Any patient condition deemed too risky for the study by the investigator

Outcomes:
Additional Adenoma Detection
Persistent Atrial Fibrillation in Southwest China Cohort Study-Stepwise Strategy for Treatment of Atrial Fibrillation Substrate Extended Study
Trial ID: NCT07380620
Status: ENROLLING_BY_INVITATION
Study type: OBSERVATIONAL
Start date: 2025-10-09
Completion date: 2035-12-31
Sponsor: Yuehui Yin
Conditions: Atrial Fibrillation
Interventions: other
Locations: the second affiliated hospital of CQMU
Countries: China
Enrollment: 5000
Age range: 18 Years - None
Sex: ALL

Summary:
This is an observational, bidirectional cohort study, aiming to establish a long-term registry of adult patients with Persistent atrial fibrillation (ps-AF) in Southwest China to characterize patient profiles and evaluate embolism risk, rate control, rhythm control and complication management and outcomes over 5 years' follow-up. The objectives of the study are to: Describe the demographic, clinical, and treatment characteristics of ps-AF patients across Southwest China and assess the effectiveness and safety of various AF treatment strategies, including oral anticoagulation, electrical cardioversion, catheter ablation, and left atrial appendage closure, in routine clinical practice. Identify patient- and treatment-related factors that influence therapeutic outcomes. Explore the characteristics of imaging multi-omics and blood multi-omics during follow-up. Explore the role of advanced technologies and artificial intelligence in improving procedural performance, risk stratification, and clinical decision-making in AF care. Participants will undergo standardized baseline evaluation and longitudinal follow-up to collect data on heart rhythm monitoring, treatment exposures, healthcare utilization, clinical events, and patient-reported outcomes. This cohort will generate real-world evidence to inform personalized and evidence-based management of AF.

Eligibility criteria:
Inclusion Criteria:

* Aged ≥18 years;
* diagnosed with persistent atrial fibrillation (ps-AF), defined as episodes lasting \>7 days or requiring pharmacological/electrical cardioversion for rhythm restoration;
* Residing in Southwest China and capable of cooperating with long-term follow-up management;
* History of ps-AF treatment with complete medical records, and voluntary signing of the informed consent for long-term follow-up.

Exclusion Criteria:

* Comorbid severe hepatic/renal dysfunction or malignant tumors;
* Estimated survival period \<1 year.

Outcomes:
Composite endpoint of stroke, major bleeding, worsening and new-onset heart failure, all-cause mortality; Major bleeding events, perioperative complications and adverse drug reactions
Intelligent Voice Model: A New Paradigm Exploration for Child Health Management
Trial ID: NCT06542120
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2024-08-21
Completion date: 2025-12-30
Sponsor: Xinhua Hospital, Shanghai Jiao Tong University School of Medicine
Conditions: Congenital Heart Disease; Bronchopneumonia; Abdominal Disease
Interventions: Heart Auscultation and Echocardiography; Chest Auscultation and Chest imaging examinations; Abdominal Auscultation and Abdominal imaging examinations
Locations: Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science and Technology; Human Children's Hospital; Xinhua Hospital,Shanghai Jiao Tong University School of Medicine; Shanghai Children's Medical Center Affiliated to Shanghai Jiaotong University School of Medicine; Kunming children's Hospital
Countries: China; China; China; China; China
Enrollment: 30000
Age range: None - 18 Years
Sex: ALL

Summary:
The purpose of this research is to develop a body voice artificial intelligence (AI) recognition device, also referred to as an AI-assisted body sound identification device, by utilizing a deep learning-based novel AI algorithm in conjunction with a big body voice model. It could identify normal and abnormal heart, breath, and bowel sounds, and to provide early screening and auxiliary diagnosis of congenital heart disease (CHD), respiratory infections, diarrhea and other common multi-occurring diseases.

Eligibility criteria:
Inclusion Criteria:

1. Age 0\~18 years old, gender is not limited
2. Children who have been diagnosed with congenital heart disease by cardiac ultrasound or who do not have congenital heart disease
3. Children diagnosed with bronchopneumonia or without bronchopneumonia
4. Children who are clinically diagnosed with intestinal diseases or who do not suffer from intestinal diseases
5. Informed consent

Exclusion Criteria:

1. ≥ 18 years old
2. Children who are unable to undergo cardiac ultrasound, chest imaging or other related examinations
3. Subjects who are unable to obtain informed consent, or who are unwilling to cooperate with the provision of diagnosis and treatment related data for further analysis and research as required by the study.

Outcomes:
Sensitivity; Specificity; AUC
The Effect of Chatgpt Usage on Case Analysis and Medical Artificial Intelligence Readiness Among Nursing Students: A Randomized Controlled Trial
Trial ID: NCT07090408
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-02-01
Completion date: 2025-06-30
Sponsor: Recep Tayyip Erdogan University
Conditions: Artificial Intelligence
Interventions: Artificial intelligence support
Locations: Recep Tayyip Erdogan University Faculty of Health Sciences
Countries: Turkey (Türkiye)
Enrollment: 59
Age range: 18 Years - None
Sex: ALL

Summary:
In this study, the effect of using ChatGPT, one of the artificial intelligence tools, on nursing students' case analysis and readiness for medical artificial intelligence will be evaluated.

Eligibility criteria:
Inclusion Criteria:

* Students who agree to participate in the study
* 2nd year students who are actively studying in the Nursing Department of the Faculty of Health Sciences of a university
* Students who use smartphones

Exclusion Criteria:

* Not accepting to participate in the research,
* Not using a smartphone

Outcomes:
Case analysis delivery time
The Effect of an Artificial Intelligence-Supported Health Promotion Application on the Nutrition, Exercise and Sleep Habits of Adolescents
Trial ID: NCT07524582
Status: ACTIVE_NOT_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-09-08
Completion date: 2026-05
Sponsor: Gümüşhane Universıty
Conditions: Adolescent Health; Nutrition Behavior; Physical Activity; Sleep Quality; Health Promotion
Interventions: Smart Nurse Mobile Application
Locations: Gumushane University
Countries: Turkey (Türkiye)
Enrollment: 146
Age range: 14 Years - 18 Years
Sex: ALL

Summary:
The goal of this clinical trial is to learn if the "Smart Nurse" artificial intelligence-supported mobile application improves nutrition, exercise, and sleep behaviors in high school students aged 14-18 years. The main questions it aimed to answer are:

* Did the "Smart Nurse" app improve healthy nutrition and exercise behaviors in adolescents?
* Did the "Smart Nurse" app improve sleep quality in adolescents?
* Did the "Smart Nurse" app lower body mass index (BMI) and waist circumference in adolescents?

Researchers compared participants who used the "Smart Nurse" app to participants who did not use the app to see if the app improved health behaviors.

Participants:

* Used the "Smart Nurse" mobile app every day for 12 weeks
* Recorded their daily meals, physical activity, and sleep time in the app
* Received personalized health messages based on their data
* Completed health surveys and body measurements at the beginning and end of the study

Eligibility criteria:
Inclusion Criteria:

* 10th grade high school student
* Aged 14-18 years
* Owns a smartphone
* Willing to participate voluntarily
* Parent/guardian signed informed consent form

Exclusion Criteria:

Outcomes:
Nutrition-Exercise Behavior Scale (NEBS) Subscale Scores
Research on the Application of Wrist and Ankle Electrical Stimulation Analgesia Therapy Device in Pain Management After Thyroid Surgery
Trial ID: NCT06931743
Status: ENROLLING_BY_INVITATION
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-04-30
Completion date: 2025-12-31
Sponsor: Xiaonan Huang
Conditions: Thyroid Surgery; Electrical Stimulation; Thyroid Carcinoma
Interventions: CMNS6-1 Electronic Acupuncture Therapeutic Apparatus; CMNS6-1 Electronic Acupuncture Therapeutic Apparatus
Locations: Wrist and Ankle Electrical Stimulation Analgesia Therapy Device
Countries: China
Enrollment: 668
Age range: 18 Years - None
Sex: ALL

Summary:
To evaluate the efficacy and safety of the wrist and ankle electrical stimulation analgesia therapy device in the management of analgesia after thyroidectomy by conducting clinical studies to collect data. It will also be combined with the Internet of Things technology to develop artificial intelligence equipment or applications to achieve remote monitoring and intelligent alarm, and improve the timeliness and initiative of pain management after thyroidectomy. In the future, we will further carry out a series of research work to explore the possible analgesic mechanisms through molecular biology and neurophysiology.

Eligibility criteria:
1. Inclusion Criteria:

   * Be over 18 years of age;
   * The operation time is less than 4 hours;
   * Postoperative VAS pain score ≥3 points ;
   * Clear consciousness, able to answer questions accurately, free speech, no hearing impairment;
   * Informed consent
2. Exclusion Criteria:

   * Neck or thyroid surgery within the past ten years;
   * Menstruating, pregnant and lactating females;Those who are not satisfied with blood glucose control;
   * Skin injury or allergy; skin ulceration;
   * Past history of psychiatric disorder that met the diagnostic criteria of Diagnostic and Manual of Mental Disorders, DSM-IV;
   * Upper respiratory tract infection within 2 weeks before surgery;
   * History of implantation of pace or defibrillator;
   * Allergic to gel or intolerant to electrical stimulation;
   * History of chronic pain, pain VAS score 1 at the examination;
   * Use of sedative analgesic drugs for a long time or within 24 hours;
   * Particip in other clinical trials in the past 4 weeks.

Outcomes:
Pain VAS score; Pain-related measures
ChatGPT-Assisted Hypertension Knowledge in Nursing Students: Assessment of Accuracy, AI Anxiety, and Cognitive Load
Trial ID: NCT06863792
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-03-10
Completion date: 2025-05-10
Sponsor: Ankara University
Conditions: Nursing Students; Artificial Intelligence; Hypertension
Interventions: ChatGPT
Enrollment: 96
Age range: None - None
Sex: ALL

Summary:
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.

Eligibility criteria:
Inclusion Criteria:

* To be enrolled in the nursing program at a private university, during the 2024-2025 academic year.
* To have taken the Internal Medicine Nursing course (In this course, students receive 4 hours of theoretical lessons on nursing care for hypertension patients).
* To be willing to volunteer for participation in the study.

Exclusion Criteria:

* Students who wish to withdraw from the research at any stage will not be included in the study.

Outcomes:
Hypertension Prevention Attitudes Scale; Artificial Intelligence Anxiety Scale; Cognitive Load Scale
Evaluation of the POSOS Device for Iatrogenesis Detection in Clinical History? A Vignettes Study
Trial ID: NCT05952193
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2023-06-21
Completion date: 2024-07
Sponsor: Centre Hospitalier Universitaire, Amiens
Collaborators: Centre Hospitalier de Bourg en Bresse; Saint Antoine University Hospital; CHU de Rouen - Accueil; CHU de Créteil; Médipôle Lyon-Villeurbanne; Centre Hospitalier Universitaire de Nice; University Hospital, Montpellier; University Hospital, Lille; Centre Hospitalier de Niort; Centre Hospitalier Régional Metz-Thionville; University Hospital, Grenoble; CHU de Reims
Conditions: Iatrogenesis; Clinical Vignettes; Diagnostic Performance Study; Medical Information Search; Artificial Intelligence
Locations: CHU Amiens
Countries: France
Enrollment: 128
Age range: 18 Years - None
Sex: ALL

Summary:
This study is a multicenter evaluation of diagnostic performance using simulated clinical vignettes. It aims to test the effectiveness of the POSOS app in detecting drug-induced iatrogenesis in urgent medical situations, an issue of public health importance. Participating physicians, who are randomly assigned to either use or not use POSOS, are categorized based on their years of experience. Vignettes, including a mixture of complex, simple, and non-iatrogenesis cases, are assigned to these doctors. During the simulation, physicians respond to their respective vignettes on the YgheniVi platform, with responses recorded at two intervals (5 min and 15 min). The supervising physicians subsequently fill out an e-CRF, providing further data on the time spent, the number of medical research applications used, and the overall user experience of POSOS. A doctor/pharmacist pair then corrects the answers to the vignettes.

Eligibility criteria:
Inclusion Criteria:

* Not applicable as the study population is made up of clinical vignettes, there is no inclusion of patients.
* The physicians included are emergency physicians, selected by convenience so as to have as many physicians (seniority of practice 3 years after thesis) as (seniority of practice less).

Exclusion Criteria:

* Not applicable.
* Simulations with technical faults will be excluded: (absence of the test doctor, internet access not working, PC malfunction, serious disruptions during the simulation).

Outcomes:
the proportion of overall correct responses in each groups