moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
A Cohort Study to Evaluate an Artificial Intelligence Model for Assisting Medical Decision-Making Using Real-Time Hospital-Wide Electronic Health Record Data
Trial ID: NCT06846229
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-02-24
Completion date: 2026-06
Sponsor: The Eye Hospital of Wenzhou Medical University
Conditions: Real-world Study
Interventions: AI-associated strategy
Locations: First Affiliated Hospital of Wenzhou Medical University; Second Affiliated Hospital of Wenzhou Medical University
Countries: China; China
Enrollment: 50000000
Age range: None - None
Sex: ALL

Summary:
The study builds and applies an AI model to help doctors predict patient diagnoses and outcomes, such as survival or hospital stay. Real-time, multimodal data (labs, vital signs, history, imaging) from hospital records will be used. Patients will be tracked to compare the AI's performance with standard care. The goal is to improve diagnosis and treatment accuracy in a real-world, prospective study.

Eligibility criteria:
Inclusion Criteria:

1. Patients admitted to any department of the hospital (e.g., ICU, general wards, emergency, outpatient services) during the study period.
2. Patients with available real-time electronic health record (EHR) data, including at least two of the following: laboratory results, vital signs, medical history, and imaging data.

Exclusion Criteria:

Patients currently enrolled in another clinical trial that could interfere with data collection or outcomes of this study.

Outcomes:
Area Under the Curve (AUC); Overall Hospital Resource Utilization Improvement; Population-Level Diagnostic Accuracy Enhancement; System-Wide Reduction in Adverse Event Rates
Health Electronic Assessment of Risks and Trends Using Biometric Equipment and Technology: HEARTBEAT
Trial ID: NCT06753045
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-10-08
Completion date: 2027-10
Sponsor: Tulane University
Collaborators: Samsung Electronics
Conditions: Heart Diseases
Interventions: Smartwatch
Locations: East Jefferson General Hospital; Tulane University Medical Group Clinics; University Medical Center
Countries: United States; United States; United States
Enrollment: 10000
Age range: 18 Years - 120 Years
Sex: ALL

Summary:
This is a phase 0, non-interventional, longitudinal, electronic data capture (EDC) study to facilitate the HEARTBEAT Study project has set out to explore the potential use of smartwatches in collecting and analyzing biometric data to improve the detection, identification, and understanding of cardiovascular diseases and related conditions by SAMSUNG and Tulane. The study will include up to ten thousand adult subjects tasked with wearing a smartwatch to collect digital biomarker data over a 1 year period. Concurrent to smartwatch data collection, subjects will be instructed to complete questionnaires via the Huma Decentralized Clinical Trials (HUMADCT) platform. There are no investigational drugs or interventions administered as part of this study.

Eligibility criteria:
Inclusion Criteria:

* Patients with at least one cardiovascular disease (heart failure, cardiac arrhythmias, chronic kidney disease, coronary artery disease, history of stroke/Transient Ischemic Attack (TIA), or diabetes mellitus).
* Patients visiting a primary care provider for any reason without any of the previously mentioned cardiovascular diseases. (Healthy cohort, limited up to 500 patients throughout the study).

Exclusion Criteria:

* Participants who cannot read, speak, and/or understand English.
* Participants with cognitive impairments who are unable to give informed consent or sign their HIPAA form.
* Participants with cognitive impairments affecting their ability to be compliant with wearing and maintaining wearable devices.
* Participants who are pregnant.
* Participants with tattoos, scars, or skin adhesions that would not allow for Photoplethysmography (PPG) recordings to be collected from a wrist-worn device.
* Participants with neurological disorders that may interfere with device signal quality (e.g., hand tremors).
* Participants with a pacemaker.
* Participants with allergies to watch and/or wristband materials.
* Participants with known plans to permanently leave the state of Louisiana within the observational period.
* Participants who have no known medical history with any of the enrolling institutions.
* Patients without a compatible smartphone (iOS 13 \& Android 11 at minimum).

Outcomes:
The incidence of cardiovascular events indicative of progression of pre-existing cardiovascular conditions; The incidence of new cardiovascular diseases events
Using Artificial Intelligence to Optimize Delivery of Weight Loss Treatment
Trial ID: NCT05231824
Status: ACTIVE_NOT_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-03-22
Completion date: 2026-03-31
Sponsor: Drexel University
Conditions: Obesity; Weight Loss
Interventions: Standard Behavioral Weight Loss Treatment; AI-optimized Behavioral Weight Loss Treatment
Locations: Drexel University Center for Weight, Eating and Lifestyle Science
Countries: United States
Enrollment: 336
Age range: 18 Years - 70 Years
Sex: ALL

Summary:
Project ReLearn is testing the efficacy and cost-effectiveness of an Artificial Intelligence system for optimizing weight loss coaching. Participants are randomized to a 1-year weekly gold standard behavioral weight loss remote (video) group treatment or the AI-optimized treatment, which is made up of a combination of remote group treatment, short video call and automated message. In the AI-optimized condition, the system monitors outcomes (via wireless scale, mobile phone app, and wristworn tracker) and, each week, assigns each participant the treatments they have responding to the best, within certain time constraints.

Eligibility criteria:
Inclusion Criteria:

* Individuals must be of overweight or obese BMI (27-50 kg/m)
* Individuals must be adults (aged 18-70)
* Able and willing to engage in the remote program
* Able to engage in physical activity (defined as walking two city blocks without stopping)
* Individuals must also provide consent for the research team to contact their personal physician if necessary, to provide clearance or to consult about rapid weight loss
* Access and willingness to use an Apple or Android smartphone
* Satisfactory completion of all enrollment procedures

Exclusion Criteria:

* Medical condition (e.g., cancer, type I diabetes, psychosis, full-threshold eating disorder) that may pose a risk to the participant during intervention or cause a change in weight
* Currently pregnant, breastfeeding, or planning to become pregnant in the next 12 months
* Recently began or changed the dosage of medication that can cause significant change in weight
* History of bariatric surgery
* Weight loss of \> 5% in the previous 3 months

Outcomes:
Weight change; Costs
Identifying Imaging Biomarkers Predictive of Disability Progression in Alzheimer's Disease: Pilot Study
Trial ID: NCT05939362
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-10-02
Completion date: 2028-01-15
Sponsor: Poitiers University Hospital
Conditions: Alzheimer Disease; Magnetic Resonance Spectroscopy; Ultra High Field 7T; Progression of Disease; MR Biomarkers
Interventions: MRI follow-up
Locations: Chu Poitiers
Countries: France
Enrollment: 80
Age range: 60 Years - 90 Years
Sex: ALL

Summary:
The pathophysiology of AD is complex. In addition to amyloid plaques and neurofibrillary degeneration, there is a metabolic alteration of the energy pathways, oxidative phosphorylation and glycolysis, which are involved in brain function. Several authors have shown a series of early metabolic dysregulations via an increase in phosphorylation at the origin of neuronal death.

Ultra-high field imaging (7T MRI) may allow, with its better spatial resolution and advanced imaging techniques, to shed light on the mechanisms of progression of Alzheimer's disease. A Magnetic Resonance Spectroscopy (MRS) examination can be coupled to brain MRI without additional risk for the patient. Multinuclear 1H-31P metabolic imaging is a promising tool that can provide information on the metabolic evolutionary profile of AD. Thus, we propose a longitudinal study in patients with early-stage AD on 7T MRI-MRS.

Eligibility criteria:
Inclusion Criteria:

* French-speaking patients aged 60 to 90 years,
* Patient in the context of Alzheimer's disease \* for which imaging after MRI is prescribed as part of the usual diagnostic process,

  \*Alzheimer's disease is diagnosed by the doctor of the memory consultation and is defined by :Evidence of a storage disorder in verbal episodic memory at LR/RI defined by a sum of LR \< 17/48 and sum of RT \< 40/48 +/- Impairment of executive functions possible (BREF, TMT grefex, verbal fluencies) +/- Impairment of instrumental functions possible (Grémots noun naming, Rey's figure, Mahieux's Battery).
* MMSE score ≥18,
* Written informed consent after the patient has been informed,
* Progressive decline for at least 6 months.

Exclusion Criteria:

--Partially or completely illiterate patient unable to read and write,

* Patient with an absolute contraindication to 7T MRI
* Severe psychiatric pathology not balanced,
* Non-degenerative neurological disease (stroke, multiple sclerosis ...),
* Patient with tumor or inflammatory pathology, or vascular leukopathy visualized in MRI (Fazekas score \> 3)

Outcomes:
To identify Magnetic Resonance Imaging biomarkers concentration (mmol/l) at baseline that are predictive of disability progression in individuals with Mild Alzheimer's disease as assessed by the Clinical Dementia Rating (CDR) scale
Will Embryo Selection Through Use of Artificial Intelligence (iDA) Perform Equally Compared to Day 5 Morphology?
Trial ID: NCT04969822
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2020-02-20
Completion date: 2023-11-01
Sponsor: Vitrolife
Conditions: IVF; Infertility
Interventions: iDAScore®
Locations: IVFAustralia Greenwich; IVFAustralia Alexandria; IVFAustralia, Westmead; Queensland Fertility Group (QFG); Melbourne IVF; Universitetshospitalet; Aagaard Fertilitetsklinik; Regionshospitalet; Livio Gothenburg; Reproductive medicine, Sahlgrenska University Hospital; Thames Valley Fertility (TFP); Nurture Fertility (TFP); Oxford Fertility (TFP); Wessex Fertility (TFP)
Countries: Australia; Australia; Australia; Australia; Australia; Denmark; Denmark; Denmark; Sweden; Sweden; United Kingdom; United Kingdom; United Kingdom; United Kingdom
Enrollment: 1066
Age range: None - None
Sex: ALL

Summary:
A non-inferiority, prospective parallel group, multi-center, randomized controlled trial to investigate whether selection of a single blastocyst for transfer using the deep learning tool, iDA, results in non-inferior clinical pregnancy rate compared to trained embryologists using standard morphology criteria.

Eligibility criteria:
Inclusion Criteria:

1. Women undergoing IVF or ICSI with controlled ovarian stimulation with gonadotrophins and the intention to treat by either transfer of a single fresh embryo on day 5 or in case of a freeze all cycle, the first rewarmed embryo.
2. Age: Up to and including the 42nd completed birthday on the day of randomization.
3. Has at least two early blastocysts on day 5.

Exclusion Criteria:

1. Treatment involving donated eggs
2. Intention to perform any form of preimplantation genetic testing
3. The use of IMSI or polarized light in the ICSI process
4. The use of assisted hatching prior to randomization
5. Previous participation in this RCT
6. Where the cycle is carried out for fertility preservation
7. If a day 2-4 transfer is planned
8. Has a reduced likelihood of obtaining two early blastocysts on day 5 as evidenced by either: an AMH level of \<3pmol/L or AFC \<5 (if available)

Outcomes:
Clinical Pregnancy Rate
Research and Validation of a Big Data-Driven Intelligent Decision-Making System for Hemodialysis
Trial ID: NCT07466329
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2011-01-01
Completion date: 2025-09-30
Sponsor: Huashan Hospital
Conditions: ESRD (End Stage Renal Disease)
Locations: Huashan Hospital, Fudan University
Countries: China
Enrollment: 778
Age range: 18 Years - 90 Years
Sex: ALL

Summary:
Objectives and Scope:This observational study aims to leverage real-world data from Huashan Hospital to develop an AI-driven intelligent decision-making system for assessing dialysis adequacy in maintenance hemodialysis (MHD) patients, and to analyze early warning factors contributing to inadequate dialysis.

Core Research Question:Can an AI-based early warning and diagnostic model, built on multidimensional big data, identify the risk of inadequate hemodialysis at an ultra-early stage and accurately diagnose composite complications such as cardiovascular and cerebrovascular diseases? Methodology:The study will conduct a retrospective analysis of adult MHD patients treated at Huashan Hospital between January 2011 and September 2025. The dataset encompasses multidimensional variables, including sociodemographics, treatment parameters, laboratory indicators, metabolomics, and physical functions. Utilizing Dynamic Network Biomarkers (DNB) technology to screen for early warning markers, combined with artificial intelligence algorithms such as Neural Networks and Support Vector Machines (SVM), the study will construct two primary models: "Ultra-early Warning" and "Disease State Diagnosis." These models are designed to provide clinical decision support for precise interventions.

Eligibility criteria:
Inclusion Criteria:

* Patients undergoing long-term maintenance hemodialysis with a dialysis vintage of at least 3 months.
* Aged between 18 and 90 years.
* Possess relatively comprehensive hemodialysis records maintained within this center.

Exclusion Criteria:

* Patients with significantly incomplete dialysis-related data.
* Patients with poor compliance during dialysis or those receiving palliative dialysis.
* Other conditions deemed unsuitable by the investigator.

Outcomes:
Cardiovascular and Cerebrovascular Diseases (CCVD)
Precision Retrospective Integrative Study for Metastatic Lymph Nodes in Breast Cancer
Trial ID: NCT06738459
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-01-01
Completion date: 2026-07-31
Sponsor: Centro di Riferimento Oncologico - Aviano
Conditions: Breast Cancer
Locations: Landeskrankenhaus Villach; KI4LIFE, Fraunhofer Austria Research; Klinikum Klagenfurt am Wörthersee Klagenfurt am Wörthersee; Centro di Riferimento Oncologico
Countries: Austria; Italy; Italy; Italy
Enrollment: 1500
Age range: 18 Years - None
Sex: ALL

Summary:
Accurate assessment of axillary lymph nodes in patients with breast cancer is essential for prognosis and treatment planning. Staging and surgical management have evolved from axillary lymph node dissection to sentinel lymph node biopsy to minimize morbidity. However, sentinel lymph node biopsy has non-negligible morbidity, and more than 70% of biopsies are negative, calling into question its routine use. Magnetic resonance imaging (MRI) can be used to detect and stage lymph node metastases in situ, but its sensitivity and specificity are moderate to poor. Few studies have employed artificial intelligence to detect lymph node metastases on MRI images, and none have used an integrative multidata approach (IMA), defined as modeling the combination of clinical and laboratory data with multiparametric MRI.

The primary objective of this retrospective observational study is to improve the accuracy of detecting lymph node involvement in breast cancer using IMA. The secondary objective is to allow longitudinal monitoring of the effects of neoadjuvant therapy on lymph node involvement

Eligibility criteria:
Inclusion Criteria:

* Patients diagnosed with breast cancer who underwent MRI and biopsy or surgery of axillary lymph nodes as part of their diagnosis and treatment
* Patients of all genders, ages, and stage I-III of breast cancer
* Patients who underwent neoadjuvant therapy and have longitudinal imaging data available for analysis (for secondary outcome analysis)

Exclusion Criteria:

* Patients whose MRI images were of insufficient quality for analysis
* Patients who had a previous history of breast cancer
* Patients with a history of axillary surgery or lymph node dissection prior to the current diagnosis of breast cancer
* Patients who received neoadjuvant therapy at another institution

Outcomes:
The accuracy of detecting lymph node involvement using multiparametric MRI, clinical characteristics, and laboratory data, and to compare it to the accuracy of detecting lymph node involvement using MRI alone
Computer Assisted Quality-monitoring for Magnet-controlled Capsule Endoscopy: a Prospective Case-control Study.
Trial ID: NCT04278196
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2020-01-01
Completion date: 2020-11-30
Sponsor: Shandong University
Conditions: Magnet Controlled Capsule Endoscopy
Interventions: AI-Box
Locations: Department of Gastroenterology, Qilu Hospital, Shandong University
Countries: China
Enrollment: 300
Age range: 18 Years - 75 Years
Sex: ALL

Summary:
During the past several years，MCE（magnet controlled endoscopy）has made great process in detecting lesions of gastric as well as screening. Compared with conventional endoscopy, magnetically controlled capsule endoscopy can be further optimized in gastric examination. So we make an AI for operators to help improve the quality of MCE and record the mucosa visibilty of gastirc.

Eligibility criteria:
Inclusion Criteria:

* patients between 18 and75
* patients who can sign informed consent

Exclusion Criteria:

* patients who are unsuitable to accept the check

Outcomes:
The integrity of the magnet controlled capsule endoscopy; The visibilty of the gastric; The time of operation in gastric
Artificial Intelligence Versus Human-controlled Doctor in Virtual Reality Simulation for Sepsis Team Training: Randomized Controlled Trial
Trial ID: NCT05953441
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-08-08
Completion date: 2022-11-13
Sponsor: National University of Singapore
Collaborators: Ministry of Education, Singapore
Conditions: Sepsis
Interventions: AI-powered doctor; Human-controlled doctor avatar
Locations: Alice Lee Centre for Nursing Studies
Countries: Singapore
Enrollment: 65
Age range: 20 Years - None
Sex: ALL

Summary:
The purpose of the study is to compare the effectiveness of Artificial Intelligence virtual doctor with human-controlled virtual doctor avatars on nursing students' sepsis care and interprofessional communication.

Eligibility criteria:
Inclusion Criteria:

1. All Year 3 NUS nursing students in Academic Year 2022
2. Individuals 20 years old and above; and
3. Consent to be video and/or audio-recorded

Exclusion Criteria:

1. Year 1, 2 and 4 NUS nursing students;
2. Incapable of giving informed consent;
3. Unable to understand and/or speak in English language;
4. Have visual, speech, and/or hearing impairment; and
5. Do not agree to be video and/or audio-recorded.

Outcomes:
Change from Baseline in Sepsis Knowledge on the 18-item sepsis knowledge test immediately after interventions.
Automated Anesthesia Systems for Painless Abortion (AAS-PA): a Multi-center, Randomized Controlled Trial
Trial ID: NCT07132151
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-09-08
Completion date: 2027-11-08
Sponsor: Boston Intelligent Medical Research Center, Shenzhen United Scheme Technology Co., Ltd.
Collaborators: Fujian Provincial Hospital; Shishi Hospital; Sichuan Provincial People's Hospital; First Affiliated Hospital of the Chinese People's Liberation Army Naval Medical University; The People's Hospital of Yubei District of Chongqing city; Peking University Shenzhen Hospital; People's Hospital of Zhengzhou University; Jinjiang Municipal Hospital (Shanghai Sixth People's Hospital Fujian)
Conditions: Painless Abortion; Artificial Intelligence (AI); Ciprofol
Interventions: AI Controlled General Anesthesia; General Anesthesia
Locations: Fujian Provincial Hospital; Jinjiang Municipal Hospital (Shanghai Sixth People's Hospital Fujian); Shishi Hospital; Peking University Shenzhen Hospital; Zhengzhou Central Hospital Affiliated to Zhengzhou University; Sichuan Provincial People's Hospital; The First Affiliated Hospital of Army Medical University; The People's Hospital of Yubei District of Chongqing city
Countries: China; China; China; China; China; China; China; China
Enrollment: 167
Age range: 18 Years - 45 Years
Sex: FEMALE

Summary:
This study compares the automated anesthesia systems for painless abortion (AAS-PA) with traditional anesthesia in painless abortions. It assesses intraoperative indicators (hypoxemia, injection pain, hypotension, hemodynamics, respiratory depression) and postoperative outcomes (nausea, dizziness, recovery time, satisfaction). The AAS-PA group uses AI-adjusted intravenous ciprofol based on real-time vital signs and sedation depth; the traditional group relies on manual anesthesia. The goal is to verify if AAS-PA safely manages sedation, meets painless standards, reduces adverse events, and improves experience, aiding optimized automated protocols.

Eligibility criteria:
Inclusion Criteria:

* Age between 18-45 years
* ASA status Ⅰ to Ⅱ
* Diagnosed with early intrauterine pregnancy
* Body mass index (BMI) between 18 to 28 kg/m2

Exclusion Criteria:

* History of cardiac, hepatic, renal, or metabolic disorders (e.g., diabetes, hypertension, sleep apnea)
* Known allergy to opioids or ciprofloxacin components
* Cachexia
* History of substance or alcohol abuse
* ASA class III or higher
* Participation in any other drug clinical trial within the past 3 months
* Long-term use of sedative or analgesic medications
* History of neurological disorders and convulsions

Outcomes:
Incidence of Hypoxemia
Efficacy of a Chatbot to Deliver Cognitive-behavioral Therapy for Insomnia in Youth: a Randomized Controlled Trial
Trial ID: NCT05769231
Status: WITHDRAWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-03-06
Completion date: 2025-01-31
Sponsor: Chinese University of Hong Kong
Conditions: Insomnia
Interventions: Cognitive Behavioral Therapy for Insomnia in Chatbot
Enrollment: 0
Age range: 18 Years - 35 Years
Sex: ALL

Summary:
Insomnia is prevalent in youth, and it associates with depression and other psychiatric disorders, leading to increased mental health burden. Cognitive Behavioral Therapy for Insomnia (CBT-I) is recommended as first-line treatment for insomnia. Digital tools have been employed to automate mental health interventions, in order to address deterrents such as clinician shortage, limited appointment availability, high cost, and stigma of seeking help. Digital CBT-I is shown to be effective in treating insomnia. Future digital intervention will incorporate patient-centered design, input from key stakeholders, and new understandings of behavior change. Artificial Intelligence (AI)-powered chatbots are utilized in different industries for better customer experience. AI chatbot is also utilized in the mental health industry to extend the boundary of digital interventions from accommodating didactic and informational content to providing interactive, intelligent, and most importantly, patient-centered conversational agents. Some famous AI mental health chatbots in Western societies were developed to give tailored feedback, respond to emotions that a user expresses, and encourage users to complete an intervention. This study will investigate the effect of a CBT-I chatbot on insomnia to provide further evidence on mental health chatbot.

Eligibility criteria:
Inclusion Criteria:

* score 8 or higher on the Insomnia Severity Index (ISI)
* between 18 and 35 years old
* read and type Chinese
* own a smartphone
* have consistent internet access to their smartphone

Exclusion Criteria:

* have a self-reported diagnosis of any psychiatric disorder(s)
* have been on regular medication(s) in the past month
* do not have instant messaging app(s) will be excluded.

Outcomes:
The Insomnia Severity Index (ISI)