moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Automated Detection and Triage of Large Vessel Occlusions Using Artificial Intelligence for Early and Rapid Treatment (ALERT)
Trial ID: NCT04142879
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2019-12-01
Completion date: 2021-12-31
Sponsor: Providence Little Company of Mary-Torrance
Collaborators: Viz.ai, Inc.
Conditions: Stroke Workflow and Clinical Outcome Parameters
Interventions: Viz Device Software
Locations: Providence Little Company of Mary Medical Center-Torrance
Countries: United States
Enrollment: 500
Age range: 18 Years - None
Sex: ALL

Summary:
Post Market, Prospective, Randomized, Controlled, multi center data collection study to evaluate the use of Viz versus the standard of care in stroke workflow and clinical outcome parameters.

Eligibility criteria:
Inclusion Criteria:

A subject must meet all of the following inclusion criteria to be included in the study:

1. Age of 18 years or greater.
2. Signs and symptoms consistent with the diagnosis of a stroke.
3. Stroke protocol imaging (CTA) with a confirmed presence of anterior LVO via Viz.

Exclusion Criteria:

A subject may not be included in the study if any of the following exclusion criteria are met:

1. Poor or incomplete CTA imaging data.
2. Disagreement between neuroradiologist and stroke specialist or neurointerventionalist relative to presence of a stroke.
3. Other serious, advanced, or terminal illness (investigator judgment) requiring treatment that significantly delays initiating transfer and/or treatment for stroke.

Outcomes:
Non-Interventional Centers; Interventional Centers
Research on Early Warning and Solution System of Difficult Airway in Perioperative Period Based on Artificial Intelligence
Trial ID: NCT04458220
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2020-07-30
Completion date: 2025-05-30
Sponsor: Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University
Conditions: Difficult Airway
Interventions: photo and voice obtained
Locations: The Ninth People's Hospital of Shanghai Jiaotong University School of Medicine
Countries: China
Enrollment: 16000
Age range: None - None
Sex: ALL

Summary:
The study is performed in Shanghai Ninth People's Hospital Affiliated with Shanghai Jiao Tong University School of Medicine . According to inclusion and exclusion criteria ,the investigators are expected to enroll 16000 participants who performed endotracheal intubation under general anesthesia. All enrolled participants must sign a written informed consent.In the preset studio,The 3D face scanner and camera is used to obtain 3D or 2-dimensional portrait images of patients in different positions and from different angles.The Hi-Fi Recorder is used to obtain sound samples of patients in different word.Then,Statistical experts use quantitative software to quantify the data.The investigators will put all the data and images into a confidential database in order to build a large database of difficult airways.

Anesthesiologist will give every patient an endotracheal intubation as planned. After that the anesthesiologist will be asked to fill out the questionnaire immediately.This questionnaire allows obtaining a ground truth for the intubation difficulty.All data will be used for AI deep learning and intelligent analysis,Several of the most relevant landmarks will be selected to build an early warning model.The overall study did not involve any intervention in the process of anaesthesia and operation of the patients, only the three-dimensional facial images of the patients were obtained, without any trauma or injury.

Eligibility criteria:
Inclusion Criteria:

* Patients to be intubated under general anesthesia.

Exclusion Criteria:

* Patients with deaf-mutism or communication disorders.
* Patients with language deficiency or non-native language.
* Patients with mental or central nervous system disease.
* Patients with stupefaction or disturbance of consciousness.
* Patients with terrible injury.
* Patients cannot follow instructions to perform standard actions.
* Patients participated in other relevant clinical investigation in the past 3 months.

Outcomes:
Diagnosed as a difficult airway
Molecular Prediction of Development, Progression or Complications of Kidney, Immune or Transplantation-related Diseases
Trial ID: NCT05318196
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2022-09-05
Completion date: 2032-09-01
Sponsor: University Hospital, Toulouse
Conditions: Acute Kidney Injury; Chronic Kidney Diseases; Solid-organ Transplantation; Cancer; Metabolic Disease; Immune Diseases
Interventions: Biological samples collection
Locations: Rangueil University Hospital
Countries: France
Enrollment: 5000
Age range: 18 Years - 99 Years
Sex: ALL

Summary:
Managing patients with renal failure requires an understanding of the molecular mechanisms that lead to its occurrence (i.e. upstream of the disease), its worsening and its persistence (i.e. downstream), while also specifying the risk of worsening renal failure (risk stratification, intolerance to the treatment or complications (infectious, metabolic, cardiovascular, cancer…). Nephrogene 2.0 aims to study these different components of kidney, immune and solid organ transplantation (SOT)-related diseases.

Eligibility criteria:
Inclusion Criteria:

* Patients (\> 18 year of age) with kidney disease or at risk to develop a kidney disease,
* Patients followed by a practitioner of the Department of Nephrology and Organ Transplantations of the University Hospital of Toulouse (France)

Exclusion Criteria:

* consent deny
* inability of the patient or its family to give consent.

Outcomes:
Identification of the molecular mechanisms underlying kidney, immune and solid organ transplantation-related diseases.
New Strategies Based on Artificial Intelligence in Breast Cancer Screening Programs in Córdoba With Digital Mammography and Digital Breast Tomosynthesis. A Prospective Evaluation.
Trial ID: NCT04949776
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-03-15
Completion date: 2024-01-11
Sponsor: Maimónides Biomedical Research Institute of Córdoba
Conditions: Breast Cancer; Screening
Interventions: Mammograms
Locations: Hospital Universitario Reina Sofia
Countries: Spain
Enrollment: 31301
Age range: 50 Years - 71 Years
Sex: FEMALE

Summary:
The use of artificial intelligence software in breast screening (Transpara®) makes it possible to identify studies with a very low probability of cancer.

The hypothesis raised in this work is that reading strategies based on artificial intelligence (single or double reading only of cases with a score\> 7 with Transpara®), allow reducing the workload of a screening program by more than 50 % with respect to the standard reading of the program (double reading of all cases without Transpara®), without presenting inferiority in terms of detection rates and recalls of the program, both with the use of 2D digital mammography and with the use of tomosynthesis or 3D mammogram.

Eligibility criteria:
Inclusion criteria:

All women between 50 and 71 years of age (including women who reach that age in the year of appointment), in the Reina Sofía University Hospital district, invited to participate in the Breast Cancer Early Detection Program, that have been randomly assigned in the Hologic equipment (DM or DBT), and who agree to participate in the study by signing the informed consent form.

1. Women studied in the program during the established period and who have previously participated.
2. Women studied in the program for the first time in the established period.

Exclusion Criteria:

1. Women invited to the program who do not agree to participate in the research study by signing the informed consent form.
2. Women with breast prostheses.
3. Women with signs or symptoms of suspected breast cancer.

Outcomes:
Assessment of Workload of each strategy; Assessment of Workload of each strategy; Detection rate; Detection rate; Recall or referral rate; Recall or referral rate
Evaluation of the Agreement Between ChatGPT-5 and Anesthesiologists' Predictions and Actual Outcomes in Predicting Postoperative Intensive Care Unit Requirement Based on Preoperative Data
Trial ID: NCT07395791
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-12-07
Completion date: 2026-06-01
Sponsor: Beste Mutlu Dağlıoğlu
Conditions: Postoperative Care
Interventions: No intervention (observational study)
Locations: Antalya City Hospital
Countries: Turkey (Türkiye)
Enrollment: 938
Age range: 18 Years - None
Sex: ALL

Summary:
Accurate prediction of postoperative intensive care unit (ICU) requirement is essential for patient safety and efficient use of limited ICU resources. In routine clinical practice, decisions regarding postoperative ICU admission are primarily based on anesthesiologists' preoperative clinical judgment, which may vary among clinicians.

This prospective, observational study aims to evaluate the agreement between predictions made by ChatGPT-5(Chat Generative Pre-trained Transformer) and anesthesiologists regarding postoperative ICU requirement using routinely collected preoperative patient data, and to compare these predictions with actual postoperative ICU admission outcomes.

The study does not involve any intervention, treatment modification, or additional procedures beyond standard clinical care. All patient data are anonymized, and clinical management is not influenced by the model's predictions

Eligibility criteria:
Inclusion Criteria:

* Adult patients aged 18 years or older.
* Patients undergoing elective surgical procedures requiring preoperative anesthesiology evaluation.
* Availability of complete preoperative clinical data required for postoperative intensive care unit (ICU) need prediction.
* Patients evaluated preoperatively by an anesthesiology specialist.

Exclusion Criteria:

* Patients younger than 18 years of age.
* Emergency surgical procedures.
* Patients with incomplete or missing preoperative clinical data.
* Patients who decline the use of their clinical data for research purposes.

Outcomes:
Agreement Between Predicted and Actual Postoperative ICU Requirement
Predicting Outcome of Cytoreduction in Advanced Ovarian Cancer, Using a Machine Learning Algorithm and Patterns of Disease Distribution at Laparoscopy (PREDAtOOR)
Trial ID: NCT06017557
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-01-02
Completion date: 2026-09-01
Sponsor: Fondazione Policlinico Universitario Agostino Gemelli IRCCS
Conditions: Ovarian Cancer Stage III; Ovarian Cancer Stage IV
Interventions: Artificial Intelligence
Locations: Fondazione Policlinico Universitario A. Gemelli IRCCS, UOC Ginecologia Oncologica
Countries: Italy
Enrollment: 151
Age range: 18 Years - None
Sex: FEMALE

Summary:
PREDAtOOR is a pilot study and this study aims at improving the selection of the best treatment strategy for patients with advanced ovarian cancer by using Camera Vision (CV) to predict outcomes of cyto reduction at the time of Diagnostic laparoscopy.

Eligibility criteria:
Inclusion Criteria:

* Patients treated at Fondazione Policlinico Gemelli Hospital, Rome Italy, Trillium -Credit Valley Hospital, Mississauga, Ontario and Princess Margaret Cancer Centre, Toronto, Canada
* Patients fit for cytoreductive surgery
* Patients with a primary diagnosis of suspect Stage III-IV ovarian cancer
* Patients selected for interval cytoreductive surgery after NACT

Exclusion Criteria:

* Patients with pre-operative Stage I-II disease confined to the pelvis
* Patients unfit for surgery
* Lack of information about patients' surgical outcomes and clinicopathological characteristics
* LGSOC, Clear cell and mucinous, non-epithelial histologic subtypes (if available)

Outcomes:
a) Number of Participants with Treatment Diagnostic Laparoscopy assessed by Predictive Index Value.; b)Number of Participants with Treatment Diagnostic Laparoscopy assessed by utilizing machine learning and computer vision models to analyze images and videos
Attitudes and Perceptions of Corresponding Authors From Top International Medical Journals Regarding the Use of Artificial Intelligence in the Scientific Process
Trial ID: NCT07326501
Status: ENROLLING_BY_INVITATION
Study type: OBSERVATIONAL
Start date: 2025-12-10
Completion date: 2026-06-30
Sponsor: Centre Hospitalier Universitaire de Nice
Conditions: Artificial Intelligence; Biomedical Research; Scientific Publishing; Surveys and Questionnaires
Interventions: observational survey
Locations: CHU de NICE
Countries: France
Enrollment: 500
Age range: None - None
Sex: ALL

Summary:
"Artificial intelligence (AI), including large language models and conversational tools, is increasingly being used in medical research. These tools may assist researchers at different stages of the scientific process, such as generating research ideas, reviewing the literature, analyzing data, writing manuscripts, and preparing articles for publication. While interest in AI is growing rapidly, there is still limited information on how these tools are actually perceived and used by leading medical researchers. This study aims to better understand the attitudes, perceptions, and self-reported uses of artificial intelligence among corresponding authors who have published in six major international medical journals. These authors play a key role in shaping scientific standards and editorial practices, and their views are essential to understanding how AI may influence the future of medical research. Participants are invited to complete an anonymous online questionnaire that asks about their familiarity with AI tools, how and when they use or plan to use them in the research process, the potential benefits they perceive, and the concerns or limitations they identify. The survey also explores participants' expectations regarding transparency, ethical guidance, and journal policies related to the use of artificial intelligence in scientific work.The study is observational and does not involve any medical intervention or collection of personal or health-related data. Participation is voluntary, and responses are fully anonymous.

Eligibility criteria:
Inclusion Criteria:

* Participants without any disease, condition, or related symptoms are permitted to participate. This study is a non-clinical, observational survey focusing on attitudes and practices related to artificial intelligence in scientific research, not on health conditions.

Exclusion Criteria:

* Authors who are not corresponding authors.
* Students, residents, interns, or trainees without corresponding author status.
* Individuals not involved in biomedical or medical research activities.
* Individuals who decline to participate or do not complete the online questionnaire.

Outcomes:
Self-reported attitudes regarding the use of artificial intelligence in the scientific research process; Perceptions regarding the use of artificial intelligence in the scientific research process
Feasibility of Using Wearable Sensors and Artificial Intelligence for Carbohydrate Counting in Chinese Americans With Type 2 Diabetes
Trial ID: NCT05335889
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2022-07-18
Completion date: 2023-09-22
Sponsor: NYU Langone Health
Conditions: Type 2 Diabetes
Interventions: eButton; Continuous Glucose Monitoring (CGM)
Locations: NYU Langone Health
Countries: United States
Enrollment: 12
Age range: 18 Years - 99 Years
Sex: ALL

Summary:
This is a one-group pilot study where Chinese immigrants who are English speaking with T2D from NYU Langone Health and NYU Brooklyn Family Health Center (Sunset Park) will be recruited.

Eligibility criteria:
Inclusion Criteria:

* ≥18 years old
* Diagnosed with T2D at least one year prior
* Self-identified as first- or second-generation Chinese immigrants
* Feel comfortable communicating in English, the reason is that the questionnaires/surveys are validated in English
* Have a computer and internet connection

Exclusion Criteria:

* Plan frequent travel or vacations or plan to relocate in the next five weeks
* Have serious diabetes-related complications, physical illness, or mental illness (e.g., schizophrenia, bipolar disorder, or substance abuse) that would preclude participation
* Have severe cognitive impairments (e.g., dementia, intellectual disability)

Outcomes:
Accuracy of Carbohydrate Counting using eButton (absolute error); Accuracy of Carbohydrate Counting using eButton (relative error); Proportion of participants who are fully compliant with eButton use
Artificial Intelligence Validation Trial for Polyp Detection: Pilot Study
Trial ID: NCT04378660
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2019-10-30
Completion date: 2020-04-18
Sponsor: Universitaire Ziekenhuizen KU Leuven
Collaborators: Nuovo Regina Margherita Hospital, Rome, Italy; Universitäts Medizin, Mainz, Germany
Conditions: Polyp of Colon
Interventions: Artificial intelligence for polyp detection
Locations: University Hospitals Leuven
Countries: Belgium
Enrollment: 357
Age range: 16 Years - None
Sex: ALL

Summary:
Interventional prospective multicenter study: Polyp detection by an automated endoscopic tool as second observer during routine diagnostic colonoscopy

Eligibility criteria:
Inclusion Criteria:

* \> 18 years
* Diagnostic or screening colonoscopy
* Therapeutic colonoscopy

Exclusion Criteria:

* Inability to give informed consent by the patient or legal representative
* \< 18 years old
* Any contraindication for colonoscopy or biopsies of the colon
* Uncontrolled coagulopathy
* Confirmed diagnosis of inflammatory bowel disease
* Short bowel or ileostomy
* Pregnancy

Outcomes:
Diagnostic accuracy of the novel AI system compared to endoscopic diagnosis as gold standard
Precision Medicine for L/GCMN and Melanoma 1 (Precis-mel 1)
Trial ID: NCT06608420
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-03-01
Completion date: 2026-11-30
Sponsor: Fundacion Clinic per a la Recerca Biomédica
Collaborators: Hospital Clinic of Barcelona
Conditions: Melanoma (Skin Cancer); Nevi and Melanomas
Interventions: Gradient Boosting Survival Analysis (GBSA),; Concordance index
Locations: Hospital Clínic de Barcelona (Dermatology service)
Countries: Spain
Enrollment: 6000
Age range: None - None
Sex: ALL

Summary:
The primary objective of this study is to create a highly multidimensional and multicentric database for melanoma that encompasses cohorts of children, adolescent and young adults. This database will be used to perform survival analysis and evaluate sentinel lymph node (SLNB) positivity in CAYA. The secondary objectives to be met are the following:

* Adaptation and optimization of algorithms: work on optimizing existing precision medicine algorithms, which are currently being used in adult patient care, for their application within pediatric and young adult populations.
* Implementation of transfer learning: given the limitations associated with pediatric and young adult data, the investigators intend to utilize transfer learning techniques. The study will employ a sequential waterfall methodology, whereby machine learning models trained on adult patient data will be fine-tuned using the more limited data from younger cohorts.
* Integration of expert medical opinion: to integrate physician's scientific domain knowledge into the decision support system. This will be facilitated through the comprehensive examination of existing literature, as well as the evaluation of variable risk contributions within each patient group.
* AI-based prognostic models: to develop artificial intelligence-based models for the quantitative prognosis of melanoma across the three age groups: adults, young adults, and children.

Eligibility criteria:
Inclusion Criteria:

\- Melanoma patients of any age with histopathological confirmed melanoma

Exclusion Criteria:

* Not having a melanoma diagnosis
* Not having signed the informed consent
* Records prior to the year 2012 (as data might not accurately reflect current practices and treatment outcomes)

Outcomes:
Patient prognosis curves
Relation Between Artificial Intelligence (AI)-Assisted Quantitative Coronary Angiography and Positron Emission Tomography-Derived Myocardial Blood Flow
Trial ID: NCT06397820
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2021-09-01
Completion date: 2024-12-31
Sponsor: Chonnam National University Hospital
Conditions: Coronary Artery Disease; Coronary Artery Stenosis
Interventions: Percutaneous coronary intervention (PCI)
Locations: Chonnam National University Hospital
Countries: South Korea
Enrollment: 168
Age range: 18 Years - None
Sex: ALL

Summary:
The aim of the study is to evaluate the clinical implications of artificial Intelligence (AI)-assisted quantitative coronary angiography (QCA) and positron emission tomography (PET)-derived myocardial blood flow in clinically indicated patients.

Eligibility criteria:
Inclusion criteria

1. Subject must be ≥18 years
2. Patients suspected with CAD or ischemic heart disease
3. Patients undergoing CAG and cardiac PET for evaluation of severity of coronary artery disease

Exclusion criteria

1. Poor imaging quality of CAG and PET which were not available for core-lab analysis
2. Chronic total occlusion
3. Time interval was more than \>3 months between CAG and PET
4. History of coronary artery bypass grafting
5. History of acute myocardial infarction or recent myocardial infarction
6. Heart failure (left ventricular ejection fraction \<40%)

Outcomes:
Correlation between diameter stenosis by AI-QCA and PET-driven RFR; Correlation between diameter stenosis by AI-QCA and PET-driven stress MBF
Prospective Multicenter Validation of a Home-Based Artificial Intelligence Enabled Single-Lead Electrocardiogram for Detecting Low Ejection Fraction and Structural Heart Disease
Trial ID: NCT07488052
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-03-01
Completion date: 2026-09-30
Sponsor: National Defense Medical Center, Taiwan
Collaborators: Taipei Medical University WanFang Hospital; Far Eastern Memorial Hospital; Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation; Chi Mei Medical Hospital; China Medical University Hospital
Conditions: Heart Failure
Interventions: Single-Lead ECG Acquisition [Apple Watch and QOCA ECG102D]
Locations: Taipei Medical University WanFang Hospital; Far Eastern Memorial Hospital; Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation; Chi Mei Medical Hospital; and China Medical University Hospital
Countries: Taiwan
Enrollment: 5000
Age range: 20 Years - 90 Years
Sex: ALL

Summary:
This prospective multicenter observational study will validate the accuracy of a previously developed artificial intelligence-enabled single-lead electrocardiogram (AI-ECG) model for identifying low ejection fraction and other structural heart disease phenotypes. Adult participants receiving a model-compatible single-lead electrocardiogram (ECG) (Apple Watch and QOCA ECG102D) and transthoracic echocardiography at five hospitals in Taiwan will be enrolled between March 1, 2026 and June 30, 2026. Model predictions will be compared with echocardiographic reference standards obtained within 30 days after the index ECG.

Eligibility criteria:
Inclusion Criteria:

* Age 20 years or older
* Single-lead ECG recorded using a study-compatible device
* Ability to comply with study procedures and, when applicable, provide informed consent according to local institutional review board (IRB) requirements
* No transthoracic echocardiography performed within 90 days before the index ECG

Exclusion Criteria:

* ECG signal quality insufficient for prespecified AI analysis
* No transthoracic echocardiography available within 30 days after the index ECG
* Echocardiography unavailable or technically inadequate for determining left ventricular ejection fraction (EF)

Outcomes:
Area Under the Receiver Operating Characteristic Curve for Detection of Low Left Ventricular Ejection Fraction; Sensitivity, Specificity, Positive Predictive Value, and Negative Predictive Value for Low EF Detection at the Pre-specified Operating Threshold