moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Effect of Intelligent Tutor Induced Pausing on Learning Simulated Surgical Skills Among Medical Students: A Randomized Controlled Trial
Trial ID: NCT06235788
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-01-30
Completion date: 2024-09
Sponsor: McGill University
Conditions: Surgical Education
Interventions: Experimental Group ICEMS verbal feedback with pause group; Experimental Group ICEMS audio feedback with pause and expert video
Locations: Neurosurgical Simulation and Artificial Intelligence Learning Centre
Countries: Canada
Enrollment: 129
Age range: 18 Years - None
Sex: ALL

Summary:
Traditional training of surgical technical skills relies on mentorship from experienced surgeons, who continuously evaluate and change trainee performance to prevent errors and potential patient harm by providing verbal instructions. These educators may also pause the procedure, explaining the risks associated with the trainee's actions, and may personally demonstrate proper techniques to the students. Studies examining pausing while providing medical care outline that these approaches allow for learning.

An artificial intelligent (AI) tutoring system, the Intelligent Continuous Expertise Monitoring System (ICEMS), improves learning in a surgical simulated operation by providing trainees with verbal instructions upon error identification. However, the effect of including a pause during this AI teaching has not been studied. Therefore, the ICEMS post-error identification methodology has been altered to include a pause with the intelligent tutor voice instruction.

The aim of this study is to determine the effect of pausing on surgical skill acquisition and transfer among pre-medical and medical students. This will be done by comparing their performance in repeated simulated tumour resection tasks.

Eligibility criteria:
Inclusion Criteria:

* First and second year medical students who are actively enrolled in any Quebec institution who do not meet the exclusion criteria.
* Students actively enrolled in medical school in a preparatory year in any Quebec institution who do not meet the exclusion criteria.

Exclusion Criteria:

* Participation in previous trials involving the NeuroVR (CAE Healthcare) simulator

Outcomes:
Change in performance; Transfer of learning; Objective Structured Assessment of Technical Skills (OSATS) global rating scale
A Preliminary Study on Artificial Intelligence for Infant Motor Screening
Trial ID: NCT04684173
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2020-11-17
Completion date: 2021-12-31
Sponsor: National Taiwan University Hospital
Conditions: Motor Disorders
Locations: School and Graduate Institute of Physical Therapy, National Taiwan University
Countries: Taiwan
Enrollment: 47
Age range: 4 Months - 18 Months
Sex: ALL

Summary:
The purpose of this preliminary study is to apply AI technology on a sample of infants aged 4 to 18 months to develop an action tracking and recognition algorithm for infant motor screening and to determine the accuracy of the captured movements during the Alberta Infant Motor Scale (AIMS) assessment using an experienced physical therapists' assessment results as the reference.

Eligibility criteria:
Inclusion Criteria:

* The inclusion criteria for term infants are: gestational age 37-42 weeks, birth weight \>2,500 grams, and no congenital/genetic abnormalities.
* The inclusion criteria for preterm infants are: gestational age \<37 weeks, birth weight \<2,500 grams, and no congenital/genetic abnormalities.
* Their mothers are older than 20 years of age, have no history of alcohol or drug abuse, and are married or live with fathers.

Exclusion Criteria:

* No

Outcomes:
gross motor development
Research on Dynamic Risk Prediction for Patients With Pulmonary Hypertension Based on Multimodal Data Fusion: A Prospective Observational Study
Trial ID: NCT07131241
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-06-27
Completion date: 2029-06-30
Sponsor: First Affiliated Hospital of Fujian Medical University
Conditions: Pulmonary Hypertension
Locations: The First Affiliated Hospital of Fujian Medical University
Countries: China
Enrollment: 1000
Age range: 18 Years - None
Sex: ALL

Summary:
Pulmonary hypertension (PH) is a progressive cardiopulmonary disease characterized by elevated pulmonary artery pressure and vascular remodeling, which leads to right heart failure and increased mortality. Despite advances in diagnostics, risk stratification remains limited due to the disease's heterogeneity. This study aims to develop and validate a dynamic risk prediction model for PH by integrating multimodal data-including echocardiography, Cardiac MRI, PET-MR, ECG, biomarkers, and clinical features-using advanced machine learning algorithms. The study will establish a prospective cohort of PH patients to explore predictive markers, stratify prognosis, and provide a scientific basis for early warning and individualized management.

Eligibility criteria:
Inclusion Criteria:

* Adults aged 18 years or older
* Pulmonary artery systolic pressure (PASP) ≥35 mmHg as estimated by echocardiography
* Provided written informed consent

Exclusion Criteria:

* Severe hepatic or renal insufficiency
* Malignancy under active treatment
* Severe infection
* Active autoimmune disease
* Major surgery within the past 3 months
* Pregnant or breastfeeding women
* Severe psychiatric disorder impairing ability to comply with the study protocol

Outcomes:
Time to clinical worsening; All-cause mortality
Exploring the Clinical Value of an AI-Assisted Patient Self-Assessment App for Bowel Preparation: A Multicenter Study
Trial ID: NCT07337694
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-01-01
Completion date: 2027-12-31
Sponsor: Sixth Affiliated Hospital, Sun Yat-sen University
Collaborators: Tianjin Haihe Hospital; Guangdong Second Provincial General Hospital; Dongguan Hospital of Traditional Chinese Medicine; Cangzhou Central Hospital
Conditions: Bowel Preparation for Colonoscopy; Colonoscopy
Interventions: The app group
Enrollment: 524
Age range: 18 Years - None
Sex: ALL

Summary:
The quality of bowel preparation hinges on how well patients follow the prep-drug regimen, so intensive education is essential. Phone calls, texts, short videos, and mini-programs have all been shown to boost compliance and improve prep quality. Still, we also need a way to spot-early-those patients who are likely to prep poorly so we can step in with a rescue plan.

In our pilot work the investigators built an AI-assisted mini-program that lets patients photograph their effluent and get an instant quality read-out. The single-center RCT showed excellent performance. Because these findings came from one center, the investigators are now launching a multicenter study to test the tool more broadly. Patients will use the AI mini-program at home; if the algorithm predicts inadequate prep it will prompt them to come in early or alert staff so the investigators can initiate a rescue protocol and, ultimately, improve bowel-cleansing quality.

Eligibility criteria:
Inclusion Criteria:

1. Age ≥ 18 years
2. Undergoing colonoscopy at a participating site
3. Able to defecate in a toilet (or in a setting where stool characteristics can be observed) after taking the bowel-prep solution
4. Proficient in using a smartphone
5. Willing to participate voluntarily

Exclusion Criteria:

* American Society of Anesthesiologists (ASA) class III or IV;

  * Gastric-outlet or intestinal obstruction; ③ Active gastrointestinal bleeding; ④ Enterostomy (colostomy/ileostomy); ⑤ Status post total colectomy.

Outcomes:
Compare the bowel preparation quality between the app group and the control group.
Prospective Evalulation of Artificial Intelligence-assisted Monitoring of Effective Withdrawal Time Versus Standard Withdrawal Time on Adenoma Detected Per Colonoscopy
Trial ID: NCT06345105
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-11-01
Completion date: 2025-01-31
Sponsor: The University of Hong Kong
Conditions: Colon Polyp; Colon Adenoma; Artificial Intelligence
Interventions: Endoscreener QC
Locations: Queen Mary Hospital, the University of Hong Kong
Countries: Hong Kong
Enrollment: 198
Age range: 40 Years - None
Sex: ALL

Summary:
The goal of this observational study is to assess the correlation between the artificial intelligence (AI) derived effective withdrawal time (EWT) during colonoscopy and endoscopists' adenoma per colonoscopy (APC), polyp per colonoscopy(PPC), adenoma detection rate (ADR), polyp detection rate (PDR) and during colonoscopy for each endoscopists. The association between the AI derived EWT with all colonoscopy outcomes during the prospective colonoscopy series would also be determined. The colonoscopy video of participants will be monitored by the AI. The result will be compared with standard withdrawal time (SWT).

Eligibility criteria:
Inclusion Criteria:

* All adult patients, aged 40 or above, undergoing outpatient colonoscopy will be recruited

Exclusion Criteria:

* history of inflammatory bowel disease
* history of colorectal cancer
* previous bowel resection (apart from appendectomy)
* Peutz-Jeghers syndrome, familial adenomatous polyposis or other polyposis syndromes
* bleeding tendency or severe comorbid illnesses for which polypectomy is considered unsafe.
* Cecum could not be intubated for various reasons
* Poor bowel preparation with Boston Bowel Preparation Scale (BBPS) \< 6

Outcomes:
Adenoma per colonoscopy of the endoscopists
Advancing Diagnostic Excellence For Older Adults Through Collective Intelligence And Imitation Learning
Trial ID: NCT07553559
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-05
Completion date: 2026-09
Sponsor: University of Pennsylvania
Collaborators: Penn Artificial Intelligence and Technology (PennAITech) Collaboratory for Healthy Aging; National Academy of Medicine (NAM)
Conditions: Diagnostic Support
Interventions: Artificial intelligence-based clinical decision support tool for diagnostic support
Locations: University of Pennsylvania
Countries: United States
Enrollment: 40
Age range: 65 Years - None
Sex: ALL

Summary:
Older adults commonly experience diagnostic errors that may lead to direct harms and increased healthcare costs. Older adults are especially at risk because of higher rates of comorbidity burden, medical complexity, frailty, and cognitive impairment. An artificial intelligence (AI) clinical decision support system (CDSS) offer a promising approach to promote diagnostic excellence for older adults.

The purpose of this study is to assess the acceptability and feasibility of a new AI CDSS for older adults in primary care. The goal of this AI CDSS is to provide diagnostic support during primary care visits (i.e., help make timely and accurate diagnoses) and support communication amongst patients, doctors, and caregivers about the patient's health.

In this study, participants will use the AI CDSS in a primary care visit and review its suggestions for diagnoses and tests. Afterwards, they will complete a feedback survey and interview where they share their thoughts about and experience using the AI CDSS.

Eligibility criteria:
Inclusion Criteria:

Clinicians

1. Work in the University of Pennsylvania Health System
2. Work in a primary care setting (i.e., Internal Medicine, Geriatric Medicine, Family Medicine, and Penn Primary Care)
3. Actively treat adult patients who are 65 years old or older

Patients

1. 65 years of age or older
2. Have an upcoming encounter with a participating primary care clinician
3. Indicate a new or worsening health concern that they wish to discuss at the upcoming encounter

Caregivers

1. 18 years old or older
2. Accompanying the participating patient during the encounter with the AI CDSS
3. Identified by the patient as a caregiver

Exclusion Criteria:

An individual who meets any of the following criteria will be excluded from participation in this study:

1. Under the age of 18 years old
2. Unable to provide informed consent in the opinion of the investigator
3. Has a "Research Do Not Contact" status in the electronic health record

Outcomes:
Feasibility of embedding the AI CDSS into a primary care visit; Feasibility of embedding the AI CDSS into a primary care visit; Acceptability of embedding the AI CDSS into a primary care visit; Acceptability of embedding the AI CDSS into a primary care visit; Acceptability of embedding the AI CDSS into a primary care visit; Acceptability of embedding the AI CDSS into a primary care visit
Real Time Determination of the Hill Grade During Gastroscopy Using Artificial Intelligence
Trial ID: NCT06040723
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-10-10
Completion date: 2023-12-22
Sponsor: Wuerzburg University Hospital
Conditions: Gastroesophageal Hernia
Interventions: EndoMind
Locations: Universitätsklinikum Würzburg
Countries: Germany
Enrollment: 195
Age range: 18 Years - None
Sex: ALL

Summary:
The Hill classification, also known as the Hill grade, is a system used to classify the severity of gastroesophageal valve incompetence, specifically related to gastroesophageal reflux disease (GERD) and hiatal hernia. This study aims to compare the ability of physicians versus an AI model to asses the Hill grade during gastroscopy.

Eligibility criteria:
Inclusion Criteria:

* Adult patients (\>18 years)
* Scheduled gastroscopy

Exclusion Criteria:

Examination level

* Previous surgical interventions or altered anatomy that prevents the proper examination of the flap valve
* Flap-valve not inspected

Data Level:

* Image during flap-valve inspection not stored
* Expert committee not resulting in a majority vote

Outcomes:
Accuracy of assessments for the Hill classification for physicians and AI method.
Prospective Evaluation of Artificial Intelligence-assisted Monitoring of Effective Withdrawal Time on Adenoma Detection
Trial ID: NCT06063720
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-11-01
Completion date: 2025-01-31
Sponsor: The University of Hong Kong
Conditions: Colonic Polyp; Colon Adenoma; Artificial Intelligence
Interventions: Endoscreen QC
Locations: Queen Mary Hospital, the University of Hong Kong
Countries: Hong Kong
Enrollment: 193
Age range: 40 Years - None
Sex: ALL

Summary:
This study prospectively evaluated the role of EWT versus SWT on adenoma detection rate (ADR) and other key quality metrics. In this prospective single-center study, patients undergoing colonoscopy were enrolled. EWT was calculated in real-time using an AI system with endoscopists blinded to the results. We performed multivariable analyses to assess the association of EWT and SWT with binary (e.g., ADR) and count outcomes (e.g., adenoma per colonoscopy \[APC\]), after adjusting for patient and procedural characteristics.

Eligibility criteria:
Inclusion Criteria: All adult patients, aged 40 or above, undergoing outpatient colonoscopy will be recruited

Exclusion Criteria:

* history of inflammatory bowel disease
* history of colorectal cancer
* previous bowel resection (apart from appendectomy)
* Peutz-Jeghers syndrome, familial adenomatous polyposis or other polyposis syndromes
* bleeding tendency or severe comorbid illnesses for which polypectomy is considered unsafe.
* Cecum could not be intubated for various reasons
* Poor bowel preparation with Boston Bowel Preparation Scale (BBPS) \< 6

Outcomes:
Adenoma detection rates
Artificial Intelligence Versus Expert Interpretation of ROTEM: A Prospective Study of Agreement and Clinical Decision-Making
Trial ID: NCT07502950
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-09-01
Completion date: 2027-07-01
Sponsor: Masarykova Nemocnice v Usti nad Labem, Krajska Zdravotni a.s.
Conditions: Coagulopathy; Thromboelastography; Agreement
Interventions: Large Language Model (LLM) artificial intelligence assesment
Enrollment: 400
Age range: 18 Years - None
Sex: ALL

Summary:
This prospective multicenter observational study aims to evaluate the agreement between artificial intelligence (AI)-based interpretation and expert interpretation of rotational thromboelastometry (ROTEM) findings in clinically relevant settings. ROTEM is widely used to guide hemostatic therapy in perioperative and critically ill patients, but its interpretation is complex and subject to interobserver variability.

The primary objective is to determine whether AI-based interpretation achieves agreement comparable to variability between expert clinicians. Secondary objectives include comparison of interpretation time, assessment of consistency of AI outputs, and evaluation of potential differences in clinical decision-making.

ROTEM datasets will be independently assessed by multiple expert anesthesiologists and by an AI-based model using standardized input. Agreement between methods and variability of interpretation will be analyzed.

The study aims to determine whether AI-assisted interpretation could serve as a reliable decision-support tool and reduce variability in ROTEM-guided clinical practice.

Eligibility criteria:
Inclusion Criteria:

Adult patients (age ≥18 years)

* ROTEM analysis performed using a ROTEM Sigma device as part of routine clinical care
* Availability of complete ROTEM output (graphical and numerical data)
* ROTEM measurement obtained in a clinical context where assessment of coagulation status is indicated (e.g., perioperative setting, trauma, or critical illness)

Exclusion Criteria:

* ROTEM measurements performed using the HEPTEM channel
* Incomplete or missing ROTEM data preventing standardized evaluation
* ROTEM measurements obtained under non-standardized or technically unreliable conditions

Outcomes:
Agreement between AI-based and expert interpretation of ROTEM findings
Artificial Intelligence Augmented Ultrasound for Developmental Dysplasia of the Hip: a Validity Study
Trial ID: NCT06647225
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-12-06
Completion date: 2026-11
Sponsor: Murdoch Childrens Research Institute
Conditions: Developmental Dysplasia of Hip
Interventions: Artificial intelligence augmented ultrasound
Locations: Royal Children's Hospital
Countries: Australia
Enrollment: 240
Age range: 4 Weeks - 20 Weeks
Sex: ALL

Summary:
The goal of this clinical trial is to learn if an ultrasound scan using artificial intelligence can accurately screen for hip dysplasia. Researchers will compare the artificial intelligence ultrasound results to the standard ultrasound measures to see if the artificial intelligence ultrasound scan can accurately screen for hip dysplasia.

It will also seek to understand how parents feel about their children undergoing this scan.

Participants will:

* Have an additional ultrasound performed on their child at their scheduled outpatient's appointment for hip dysplasia
* Complete a short questionnaire about the experience of having the measurement performed on their child

Eligibility criteria:
Inclusion Criteria:

* Enrolled in the VicHip study
* Is 4-20 weeks of age at enrolment
* Is attending The Royal Children's Hospital for the purpose of the potential diagnosis of DDH
* Has a diagnostic (standard) hip ultrasound on the day of their out-patient appointment
* Has a legally acceptable representative capable of understanding the informed consent document and providing consent on the participant's behalf.

Exclusion Criteria:

Participants will be excluded from enrolment if:

• They are currently receiving treatment for DDH

Outcomes:
Artificial Intelligence augmented ultrasound screening test capability: Sensitivity; Artificial Intelligence augmented ultrasound screening test capability: Specificity; Artificial Intelligence augmented ultrasound screening test capability: Positive predictive value (PPV); Artificial Intelligence augmented ultrasound screening test capability: Negative predictive value (NPV)
Aging Well: Targeting Obesity With GLP-1 Agonists to Enhance Physical and Vascular Health in Postmenopausal Women
Trial ID: NCT07057310
Status: ENROLLING_BY_INVITATION
Study type: INTERVENTIONAL
Phase: PHASE4
Start date: 2025-05-01
Completion date: 2027-03-31
Sponsor: Mayo Clinic
Conditions: Obesity; Menopause
Interventions: semaglutide or tirzepatide; Lifestyle modification intervention
Locations: Mayo Clinic in Florida
Countries: United States
Enrollment: 30
Age range: 46 Years - 60 Years
Sex: FEMALE

Summary:
In postmenopausal women, obesity increases the risk of physical function decline, premature aging, and vascular dysfunction. Semaglutide and tirzepatide are potent obesity medications. The goal is to determine the effect of weight loss induced by the new generation of GLP-1 receptor agonists on physical function, aging biomarkers, and vascular function in postmenopausal women with obesity.

Eligibility criteria:
Inclusion Criteria:

* Postmenopausal women (with menopause defined as the cessation of menstrual periods for at least 12 months or history of bilateral oophorecomy)
* Age 46-60 years old
* BMI ≥ 30 kg/m2.

Exclusion Criteria:

* Past or current use of menopausal hormone therapy
* Early or premature menopause
* History of chemically induced menopause
* Impaired renal function (GFR ≤29)
* Thyroid-stimulating hormone ≥7 with low free T4
* Diabetes
* Cardiovascular disease
* Elevated blood pressure (\>170/\>95)
* Uncontrolled co-morbidities including inflammatory, autoimmune, infectious, hepatic, gastrointestinal, malignant, or psychiatric disease
* \>5% change in weight during the 3 months prior to screening
* Other anti-obesity medication use within the past 3 months
* History of bariatric surgery
* History of malignant neoplasms within the past 5 years prior to screening
* Current use of supplements known to affect weight
* Personal or family history of medullary thyroid cancer.
* Wheelchair bound
* Vision loss

Outcomes:
Physical Function Measure: Short Physical Performance Battery (SPPB) score.; Physical Function Measure: Modified Clinical Test of Sensory Interaction in Balance (mCTSIB) performed on the Rehabilitation Artificial Physical Intelligence Database (RAPID) foot pressure mat; Physical Function Measure: Standing Chest Throw Test with a 2-kg Weighted Ball using Motion Sensor Technology (MST); Physical Function Measure: Chair-to-Stand Tests using Motion Sensor Technology (MST); Physical Function Measure: Six-Minute Walk Test (6MWT); Physical Function Measure: Hand Grip Strength using a Hydraulic Hand Dynamometer; Physical Function Measure: Short Form-12 (SF-12) Health Survey; Physical Function Measure: Patient-Reported Outcomes Measurement Information System (PROMIS) - Fatigue and Physical Function Domains; Aging Biomarkers: Cellular Senescence Markers in Plasma; Difference between biological and chronological age; Vascular Function Assessment: Reactive Hyperemia Index (RHI) using EndoPAT