moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Rapid dEtection of HyperkAlemia (K+) in the EmergenCy Department Using a SmarTphone-enabled Single-lead EKG (REACT)
Trial ID: NCT05441852
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2022-03-31
Completion date: 2023-07-07
Sponsor: Mayo Clinic
Conditions: Hyperkalemia
Locations: Mayo Clinic
Countries: United States
Enrollment: 1151
Age range: 50 Years - 89 Years
Sex: ALL

Summary:
The purpose of this study is to validate the real-world performance of a previously developed Artificial Intelligence - Electrocardiogram (AI-ECG) algorithm for identification of hyperkalemia with a six-lead mobile-enhanced device .

Eligibility criteria:
Inclusion Criteria:

* Age greater than/equal to 50 years and able to provide consent.
* Patients with eGFR (from serum creatinine) \< 45 ml/minute and/or a history of serum potassium \> 5.2 mEq/l.

Exclusion Criteria:

* Patients underage \< 50.
* Do not meet inclusion criteria.
* Unstable patients requiring emergent resuscitation.
* Patients unable to provide consent.

Outcomes:
Hyperkalemia detection by AI enhanced ECG
AI Chatbot-Based Learning on Dry Eye and Eye Strain Knowledge Among Nursing Students
Trial ID: NCT06785155
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-02-04
Completion date: 2025-03-04
Sponsor: Gaziantep Islam Science and Technology University
Conditions: Health Behaviors; Artificial Intelligence (AI); Chatbot; Dry Eye; Eye Strain
Interventions: Chatbot group; website assisted
Locations: Deparment of Nursing, Kilis 7 Aralık University
Countries: Turkey (Türkiye)
Enrollment: 63
Age range: 18 Years - None
Sex: ALL

Summary:
The aim of this project is to evaluate the effectiveness of using chatbots for patient education about dry eye disease. The study will examine how the chatbot affects patients' levels of information, managing symptoms, and overall satisfaction.

Eligibility criteria:
Inclusion Criteria:

* to be actively enrolled in the nursing program
* to demonstrate baseline digital literacy.

Exclusion Criteria:

* diagnosed visual impairments,
* those with prior specialized ophthalmological training,
* individuals with limited digital technology proficiency

Outcomes:
Dry Eye and eye strain knowledge questionnaire
Comparing Expert and Trainee Levels of Surgical Technical Skills in Virtual Reality Surgical Simulation - A Case Series Study
Trial ID: NCT05191550
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2015-03-01
Completion date: 2016-05-31
Sponsor: McGill University
Conditions: Training Groups
Interventions: Virtual reality brain tumor simulation
Enrollment: 50
Age range: 18 Years - None
Sex: ALL

Summary:
Background:

The use of virtual reality surgical simulators has been explored as a means of providing objective assessments in surgery. The enormous data generated by simulators can provide an objective and novel insight into the technical composites of expertise.

The objective of this work to form a data repository from expert and trainee levels of surgical performance data using the NeuroVR surgical simulation platform with haptic feedback, which will be used to assess face, content and construct validity of the simulated surgical tasks and develop intelligent systems in future studies.

Specific Aims: 1) Construct a data repository including expert and trainee levels of surgical performance data on virtual reality simulation. 2) Outline the face and content validity of the simulated tasks.

Design: 50 participants from a single university were recruited in this consecutive retrospective case series study.

Setting: Neurosurgical Simulation and Artificial Intelligence Learning Centre, McGill University Participants: Neurosurgeons (14), neurosurgical fellows and neurosurgical residents (24) from Montreal Neurological Institute and Hospital along with medical studies (12).

Task: Complete removal of a simulated tumor with minimal bleeding and damage to surrounding healthy brain using two surgical instruments of the NeuroVR (CAE Healthcare) surgical simulator.

Intervention: A single 28-minute simulation session, including 5 practice scenarios (3 minutes per task) and one complex realistic scenario (13 minutes). Pre- and post-questionnaires to obtain participant demographics and assess face and content validity of the simulated tasks.

Main Outcomes and Measures:

Primary outcome are surgical performance metrics recorded during the simulated tasks including.

1. Simulated bipolar and simulated ultrasonic aspirator separation distance Mean instrument tip separation distance in mm
2. Simulated bipolar force application Mean bipolar force application in Newtons
3. Simulated ultrasonic aspirator force application Mean ultrasonic aspirator force application in Newtons

Secondary outcomes include:

1.Rating of face validity of simulation

Questionnaire using 5 point Likert scale will assess face validity

2 Rating of content validity of simulation

Questionnaire using 5 point Likert scale will assess content validity

.

Eligibility criteria:
Inclusion Criteria:

Neurosurgeons

Neurosurgical fellows

Neurosurgical residents

Medical students

Exclusion Criteria:

Experience with previous similar brain tumor simulation

Outcomes:
Simulated bipolar and simulated ultrasonic aspirator separation distance; Simulated bipolar force application; Simulated ultrasonic aspirator force application
Automatic Segmentation by a Convolutional Neural Network (Artificial Intelligence - Deep Learning) of Polycystic Livers, as a Model of Multi-lesional Dysmorphic Livers
Trial ID: NCT03960710
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2019-04-01
Completion date: 2019-09
Sponsor: Hospices Civils de Lyon
Conditions: Polycystic Liver Disease; Polycystic Hepatorenal Disease; Liver Injury
Interventions: Anonymized CT examinations; Training (1); Training (2); Validation (1); Validation (2)
Locations: Service de radiologie - Pavillon B - Cellule Recherche imagerie, Hôpital Edouard Herriot (HCL)
Countries: France
Enrollment: 120
Age range: 18 Years - None
Sex: ALL

Summary:
Assessing the volume of the liver before surgery, predicting the volume of liver remaining after surgery, detecting primary or secondary lesions in the liver parenchyma are common applications that require optimal detection of liver contours, and therefore liver segmentation.

Several manual and laborious, semi-automatic and even automatic techniques exist.

However, severe pathology deforming the contours of the liver (multi-metastatic livers...), the hepatic environment of similar density to the liver or lesions, the CT examination technique are all variables that make it difficult to detect the contours. Current techniques, even automatic ones, are limited in this type of case (not rare) and most often require readjustments that make automatisation lose its value.

All these criteria of segmentation difficulties are gathered in the livers of hepatorenal polycystosis, which therefore constitute an adapted study model for the development of an automatic segmentation tool.

To obtain an automatic segmentation of any lesional liver, by exceeding the criteria of difficulty considered, investigators have developed a convolutional neural network (artificial intelligence - deep learning) useful for clinical practice.

Eligibility criteria:
Inclusion Criteria:

* Patients ≥ 18 years old
* Patients with hepato-renal polycystosis, with or without surgery
* Patients with at least one abdominal-pelvic CT scan without injection or with injection between January 1, 2016 and August 2018
* Patients with good quality and available images

Exclusion Criteria:

* Patients with no CT scan images available
* Patients with bad quality of CT scan images

Outcomes:
Test of automatic segmentation by the convolutional neural network on these group and collection of data set
Exploratory Randomised Controlled Trial of an Artificial Intelligence Self Harm Application
Trial ID: NCT06019663
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-06-01
Completion date: 2025-12-30
Sponsor: Pakistan Institute of Living and Learning
Conditions: Self-harm; Suicide
Interventions: The Artificial Intelligence intervention for Self-Harm prevention Application (AISHA) is a mobile intervention based on Y-CMAP
Locations: Benazir Bhutto Hospital; Civil Hospital
Countries: Pakistan; Pakistan
Enrollment: 312
Age range: 16 Years - 25 Years
Sex: ALL

Summary:
This is a mixed-methods study. The quantitative component will comprise of a multicentre rater-blind, randomized controlled trial to evaluate the feasibility and acceptability of AISHA in addition to treatment as usual (TAU) compared to TAU alone in Pakistan.

In-depth interviews and focus groups with other stakeholders will take place to consider the wider implementation of AISHA in mental health services in Pakistan.

Eligibility criteria:
Inclusion Criteria:

* All patients presented to the participating family physicians, community health centres, emergency departments or admitted to a general hospital after an episode of self-harm at participating sites.
* Age 16 to 25 years
* Participants must be familiar with functioning of handheld mobile devices (mobile phones, tablets).
* Able to read Urdu or English language.
* Individuals able to provide written informed consent.
* Have an android mobile phone/device

Exclusion Criteria:

* Participants with ICD-I0 mental disorder; due to a general medical condition or substance misuse, dementia, delirium, alcohol or drug dependence, schizophrenia, bipolar disorder, learning disability which prevents participation.
* Participants needing inpatient psychiatric treatment as determined by their clinical teams

Outcomes:
Beck Scale for Suicide Ideation
Effectiveness of Artificial Intelligence-Based Educational Guide to Prevent Surgical Site Infection Among Women Delivering Via Caesarean Section
Trial ID: NCT07438314
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-02-17
Completion date: 2025-12-16
Sponsor: Mansoura University
Conditions: Surgical Site Infection
Interventions: Artificial Intelligence-Based educational guide
Locations: Faculty of Nursing
Countries: Egypt
Enrollment: 300
Age range: 18 Years - None
Sex: FEMALE

Summary:
The study aimed to evaluate the effectiveness of an artificial intelligence-based educational guide to prevent surgical site infection among women delivering via cesarean section.

Research hypotheses:

H0: An artificial intelligence-based educational guide will not have any effect on reducing the rate of surgical site infection among women delivering via caesarean section.

H1: An artificial intelligence-based educational guide will have a significant positive effect on reducing the rate of surgical site infection among women delivering via cesarean section.

A purposive sample of 300 CS delivered women was divided randomly by using computer-generated randomization. into a control and intervention group, 150 women each. The control group received standard care. The intervention group received standard care plus the Artificial Intelligence guide

* After discharge, the researcher contacts study subjects every day by WhatsApp and phone call for any questions and to remind them of the upcoming follow-up visit.
* Women were asked to photograph the incision site and send it via WhatsApp using an end-to-end encrypted messaging platform to evaluate the wound condition. All photos were deleted immediately after completing the study for women's privacy.
* At the 10th postoperative day (±3 days), a follow-up was performed in the Outpatient Clinic because the majority of SSIs developed between POD5 and POD10, and due to the importance of timely identification and referral of SSIs.
* According to the study protocol, all women were followed until 30 days after CS. Follow-up was performed in the outpatient clinic, and post-CS SSI screening questions were asked, a physical examination of the patient and determined whether the patient had an SSI as per the Centers for Disease Control and Prevention definition.

Eligibility criteria:
Inclusion Criteria:

* Women aged 20-60 years who delivered via cesarean section (CS).
* Own a smartphone with internet access.
* Have basic digital literacy, defined as the ability to use mobile applications, read text messages, and communicate via WhatsApp or similar platforms.
* Agree to participate voluntarily and provide informed consent.

Exclusion Criteria:

* Women with severe comorbid medical or psychiatric conditions that may affect their participation or comprehension.

  * Women without smartphone access or with limited digital skills that prevent them from interacting with the AI-based educational guide.
  * Women with communication difficulties (e.g., hearing or speech impairment) that hinder engagement with the intervention.

Outcomes:
A structured interview Questionnaire
Elucidating the Impact of Social Wellness and Artificial Intelligence on the Psychological Consequences of Breast Cancer Imaging
Trial ID: NCT06604078
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-09-05
Completion date: 2025-09-01
Sponsor: University of Central Florida
Conditions: Breast Cancer; Breast Cancer Screening
Locations: University of Central Florida
Countries: United States
Enrollment: 199
Age range: 18 Years - None
Sex: ALL

Summary:
This study aims to develop a social health platform called MammoChat (https://MammoChat.com) that allows patients to share their real-world patient data to a trusted network for development of clinical intelligence to improve patient outcomes. Therefore:

1. The investigator will establish a Discourse social network where patients can anonymously and securely share their breast imaging and interact with other patients.
2. The investigator will use standardized questionnaires to understand the impact of use of the social network on outcomes related to breast cancer screening such as anxiety.
3. The investigator will assemble a crowdsourced, de-identified radiographic repository for training, testing, and validating AI models aimed at earlier and more accurate disease detection for breast cancer.

Eligibility criteria:
Inclusion Criteria:

* Adults, ages 18 and older
* Had a radiographic breast cancer imaging test, either for screening or diagnosis of breast cancer, with either positive or negative results performed in a US institution.
* Have an email account with access to a reliable internet connection or smartphone

Exclusion Criteria:

* Minors , ages under 18
* Prisoners. Given the repository design, prisoners are unlikely to participate and provide a consent
* Adults who are unable to provide consent.

Outcomes:
AI Model Development; Psychological Impact of Breast Cancer Screening; Social Discourse for Patients on Wellness Network
Coronary Computed Tomography Angiography and Functional Analysis in Risk Stratification of Coronary Artery Disease: A Retrospective Cohort Study
Trial ID: NCT05884008
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2023-05-01
Completion date: 2025-12-31
Sponsor: Ruijin Hospital
Conditions: Coronary Artery Disease
Locations: Cangzhou Center Hospital; First affiliated hospital of Harbin Medical University; First affiliated hospital of Zhengzhou University; Union Hospital, Tongji Medical College, Huazhong University of Science and Techonology; First Hospital of Nanjing; First affiliated hospital of Dalian Medical College; General Hospital of Northern Theater Command; Ruijin Hospital, Shanghai Jiaotong University School of Medicine; Xinhua Hospital, Shanghai Jiaotong University School of Medicine
Countries: China; China; China; China; China; China; China; China; China
Enrollment: 300
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
This study is a multicenter, retrospective imaging study. The study intends to retrospectively enroll patients with acute myocardial infarction who had received coronary CTA in a certain time-window before this event. All coronary CTA will be analyzed by anatomic, functional and radiomic analysis, assisted by artificial intelligence. The purpose of this study is to establish a coronary artery disease risk stratification system by coronary CTA.

Eligibility criteria:
Inclusion Criteria:

* Age between 18-80 years old
* Received coronary CT angiography 3 months to 5 years prior to acute coronary myocardial infarction. CCTA identified coronary atherosclerotic plaques with diameter stenosis ≥ 10%

Exclusion Criteria:

* Familial hypercholesterolemia
* Prior history of percutaneous coronary intervention (PCI) or coronary artery bypass graft (CABG)
* Prior history of myocardial infarction before the recent event
* Severe liver or renal insufficiency

Outcomes:
Coronary artery plaque risk level
The Diabetic Retinopathy Screening, Prevention and Control Program
Trial ID: NCT04240652
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2018-06-05
Completion date: 2040-06-05
Sponsor: Shanghai Jiao Tong University School of Medicine
Conditions: Diabetic Retinopathy
Locations: Ruijin hospital, Shanghai Jiao-Tong University School of Medicine; Shanghai Jiao-Tong University School of Medicine
Countries: China; China
Enrollment: 500000
Age range: 18 Years - None
Sex: ALL

Summary:
The greatest harm of diabetes is various acute and chronic complications, especially diabetic retinopathy(DR), leading to extremely high rates of disability and blindness. Early screening, early diagnosis, and early treatment are the keys to maintaining vision in patients with DR. However, compared with the high prevalence of diabetes in China, the DR screening ability is relatively inadequate. To change this situation, deep learning(DL), a form of artificial intelligence (AI), might be a potential effective method to solve this dilemma.

Eligibility criteria:
Inclusion Criteria:

* Meet the diagnostic criteria for type 2 diabetes according to the World Health Organization (WHO) in 1999; Type 1 diabetes, single gene mutation diabetes, secondary diabetes caused by pancreatic damage, Cushing's syndrome, thyroid dysfunction, or acromegaly;
* Subjects from other medical institutes are diabetes, non-diabetic patients and healthy participants who are invited to participate in the study.

Exclusion Criteria:

* Those who have a history of drug abuse;
* Sexually transmitted diseases such as AIDS and syphilis, and infectious diseases such as viral hepatitis and tuberculosis which are at active phase;
* Any condition that the investigator think that the subject is not suitable for participating in the study.

For detailed In-/Ex-clusion criteria please see the study protocol.

Outcomes:
Diabetic retinopathy; Referable diabetic retinopathy; Vision threatening diabetic retinopathy; Diabetic macular edema
Study of bladdeR Cancer Detection in Standard White Light Versus AI-Supported Endoscopy-02
Trial ID: NCT06780358
Status: ENROLLING_BY_INVITATION
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-11-20
Completion date: 2025-05
Sponsor: Cystotech
Collaborators: Aarhus University Hospital
Conditions: Bladder Cancer
Interventions: AI supported detection of bladder cancer
Locations: Department of Urology, Aarhus University Hospital
Countries: Denmark
Enrollment: 64
Age range: 18 Years - None
Sex: ALL

Summary:
This study is being conducted to investigate if an artificial intelligence support tool is non-inferior in detecting bladder cancer compared to the traditional method, standard white light cystoscopy (WLC). The researchers will compare how well the artificial intelligence tool and WLC perform in detecting bladder cancer through a controlled, organized testing process.

Eligibility criteria:
Inclusion Criteria:

* Men and women adults, age \>18 years old

Suspicion of primary or recurrent bladder cancer

Willingness to sign the Informed Consent Form (ICF) for the CI

Ability to comprehend the oral and written Patient Information Leaflet (PIL)

Exclusion Criteria:

* Not able or willing to sign the Informed Consent Form

Outcomes:
Sensitivity of standard WLC compared to WLC assisted by the AI model evaluated with a non-inferiority margin of 5%.
Prospective Evaluation of a Point-of-Care Artificial Intelligence Model in Critical Care Outcomes
Trial ID: NCT07293078
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: PHASE1; PHASE2
Start date: 2026-01-01
Completion date: 2029-06-30
Sponsor: MetroWest Artificial Intelligence Research Workgroup
Conditions: Critical Illness; Sepsis; Acute Respiratory Failure (ARF); Multi-organ Failure; Acute Kidney Injury; Delirium Confusional State; Shock
Interventions: Point-of-care large language model decision support (ChatGPT-5)
Locations: Framingham Union Hospital/MetroWest Medical Center
Countries: United States
Enrollment: 1000
Age range: 18 Years - None
Sex: ALL

Summary:
This is a prospective, unmasked, randomized, multicenter clinical trial evaluating the impact of point-of-care large language model (LLM)-based decision support on diagnostic accuracy and clinical outcomes in adult medical intensive care unit (MICU) patients.

Consecutive adult ICU admissions at participating community hospitals (initially MetroWest Medical Center and St. Vincent Hospital) will be screened for eligibility. Eligible patients will be randomized 1:1 to standard care or an AI-assisted group. In both arms, initial evaluation and management will follow usual practice. For patients randomized to AI assistance, de-identified admission data (history and physical, labs, imaging reports, and other relevant documentation) will be formatted and submitted to a state-of-the-art LLM (ChatGPT-5) at the time of admission. The AI-generated differential diagnosis and therapeutic recommendations will be provided to the admitting team for consideration. For the standard care arm, LLM output will be generated but not shared with clinicians.

After discharge, a masked chart review will determine the "ground truth" primary diagnosis and extract outcomes including: Primary Outcome - a composite of medical errors (from time of ICU admission through day 7 of ICU stay, or ICU discharge, whichever comes first); Secondary Outcomes - 90-day mortality, ICU and hospital length of stay, and ventilator-free days.

Eligibility criteria:
Inclusion Criteria:

1. Adult patients (≥ 18 years) admitted to the medical intensive care unit (MICU) at participating hospitals.
2. Direct admissions from the emergency department or transfers from medical wards to the MICU.
3. Critically ill patients meeting local ICU admission criteria.

Exclusion Criteria:

1. Transfers to the MICU from outside hospitals, operating room, or post-anesthesia care unit.
2. Age \< 18 years.
3. Incomplete or missing essential clinical information at admission (e.g., key labs or documentation not yet available).
4. Primary surgical or cardiac (e.g., STEMI) patients.
5. Pregnant or postpartum women.
6. Prisoners.

Outcomes:
Composite of Medical Errors