moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
AI Recognition of Important Structures in Otolaryngological Surgery
Trial ID: NCT06726551
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2020-01-01
Completion date: 2026-08-30
Sponsor: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Conditions: Otolaryngological Diseases Requiring Mastoidectomy; Middle Ear Surgery, Endolymphatic Sac Surgery, Cochlear Implant, Labyrinthectomy, Petrous Apex Lesions, Facial Nerve Surgery, and Lateral Skull Base Tumors
Locations: Sun Yat-sen Memorial Hospital
Countries: China
Enrollment: 1000
Age range: 6 Months - 100 Years
Sex: ALL

Summary:
Developing a system for artificial intelligence to recognize anatomical landmarks in otolaryngological surgery, enabling real-time tracking of critical temporal bone structures during surgery.

Eligibility criteria:
Inclusion Criteria:

1. Performing otolaryngological surgeries such as cochlear implantation, endolymphatic sac decompression, semicircular canal plugging, and acoustic neuroma surgery using a microscope.
2. Microscopic exposure provides a comprehensive and clear view of the surgical area within the temporal bone.

Exclusion Criteria:

1. No surgical video recording available.
2. Unclear visualization of the surgical area during microscopic otolaryngological procedures.
3. Incomplete visualization of the entire surgical process.
4. Patients who did not undergo high-resolution temporal bone CT at our hospital or Shenzhen Deep Bay Hospital.

Outcomes:
otolaryngological diseases requiring mastoidectomy; Relevant indicators to evaluate the accuracy of the model
Mental Health, Intellectual and Neurodevelopmental Disorder Detection With Artificial Intelligence Models: Testing Speech-Based Machine Learning Algorithms for Clinical Assessment and Risk Stratification in Mental Health Presentations
Trial ID: NCT06792175
Status: ENROLLING_BY_INVITATION
Study type: OBSERVATIONAL
Start date: 2025-02-04
Completion date: 2026-07
Sponsor: Psyrin Inc.
Collaborators: Allwell Behavioral Health Services; The Brookline Center
Conditions: Autism Spectrum Disorder; Depression - Major Depressive Disorder; Anxiety, Generalized; Bipolar Disorder (BD); Attention Deficit Hyperactivity Disorder (ADHD); Schizophrenia Spectrum &Amp; Other Psychotic Disorders; Post Traumatic Stress Disorder; Obsessive Compulsive Disorder (OCD)
Interventions: Solicue Machine Learning Models; Mercuria Machine Learning Models
Locations: The Brookline Center; Allwell Behavioral Health Services
Countries: United States; United States
Enrollment: 500
Age range: 13 Years - 60 Years
Sex: ALL

Summary:
This study investigates whether AI-driven analysis of speech can accurately predict clinical diagnoses and assess risk for various mental or behavioral health conditions, including attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder, bipolar disorder, generalized anxiety disorder, major depressive disorder, obsessive compulsive disorder (OCD), post-traumatic stress disorder (PTSD), and schizophrenia. We aim to develop tools that can support clinicians in making more accurate and efficient diagnoses.

Eligibility criteria:
Inclusion Criteria

1. Participants aged between 16 and 60 years.
2. Individuals currently undergoing or referred for clinical assessment of mental or behavioral health conditions (including but not limited to ADHD, ASD, BPAD, GAD, MDD, OCD, PTSD, SSD)
3. Fluent in English
4. Capable of providing informed consent, or in the case of minors, having a parent or legal guardian who can provide consent on their behalf.
5. Access to a device (smartphone, tablet, or computer) with a microphone and stable internet connectivity, necessary for completing the speech tasks.

Exclusion Criteria

1. Individuals experiencing acute mental health crises or severe symptoms that would preclude meaningful participation in the study, including acute intoxication.
2. Severe cognitive impairment or intellectual disability that would prevent understanding of the study procedures or completion of the speech tasks.
3. Lack of fluency in English.
4. Technical limitations: Inability to access a suitable device or internet connection for completing the speech tasks

Outcomes:
Speech Battery ("PSY-10") audio; Clinical diagnosis; Performance of AI models
Leveraging Social Determinants Via Artificial Intelligence and Peer Coaching to Address Racial Disparities in Primary Care Among People Who Use Opioids
Trial ID: NCT06569667
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-03-28
Completion date: 2026-08-31
Sponsor: Friends Research Institute, Inc.
Collaborators: Nathan Kline Institute for Psychiatric Research; NYU Langone Health; Weill Medical College of Cornell University
Conditions: Opioid Use Disorder
Interventions: PRC supported text + AI driven SDH-enhanced text; AI driven SDH-enhanced text only; Treatment as Usual (TAU)
Locations: NYC Health + Hospitals
Countries: United States
Enrollment: 292
Age range: 18 Years - 99 Years
Sex: ALL

Summary:
Black and Latinx people who use opioids are disproportionately impacted by opioid overdose deaths. The proposed study assesses the efficacy of an open source, multimodal artificial intelligence-driven texting tool combined with peer recovery coach-supported text contact that delivers social services, stigma reduction, health habitus, and patient navigation content addressing social determinants of health to enhance receipt of buprenorphine in primary care among emergency department-enrolled Black / Latinx people who use opioids.

Eligibility criteria:
Inclusion Criteria:

1. ≥18 years of age;
2. fluent in English and/or Spanish;
3. self-reported non-prescription opioid use \<30 days prior to consent;
4. provision of informed consent;
5. planned stay in NYC ≥12 month;
6. self-identified Black and/or Latinx race/ethnicity;
7. positive urine toxicology for opioids per EMR records;
8. diagnosis of OUD per the Diagnostic and Statistical Manual of Mental Disorders-5
9. self-reported interest in initiating buprenorphine in primary care
10. must have a mobile phone data plan.

Exclusion Criteria:

1. inability to comprehend text content written at a 3rd grade reading level;
2. physical or visual disability preventing mobile phone use;
3. self-reported receipt of medications for OUD in past 30 days.

Outcomes:
Rate of self-reported linkage to OUD services
Strategy for Decreasing Inadequate Bowel Cleansing in Colonoscopy Based on an Artificial Intelligence System: A Randomized and Controlled Study
Trial ID: NCT05871814
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-09-15
Completion date: 2024-08-31
Sponsor: University of La Laguna
Conditions: Cleansing Quality of the Colon
Interventions: Colon preparation guided by an artificial intelligence device
Locations: Hospital Universitario de Canarias
Countries: Spain
Enrollment: 774
Age range: 18 Years - None
Sex: ALL

Summary:
The main purpose of the study is to assess if a strategy based on a mobile application linked to a neural network is useful for guiding colon cleansing in a more personalized way is better than the usual care defined as regular oral and written instructions. The secondary aim will be the acceptance of this artificial intelligence device defined as the proportion of patients assigned to the intervention group that actually used the device.

Eligibility criteria:
Inclusion Criteria:

* Age ≥ 18 years.
* Patients referred for outpatient colonoscopy
* Sign informed consent

Exclusion Criteria:

* Incomplete colonoscopy (except for poor bowel preparation)
* Contraindication for colonoscopy
* Allergies.
* Refusal to participate in the study or impairment to sign the informed consent.
* Colectomy (more than 1 segment)
* Dementia with difficulty in the intake of the preparation.
* Inability to use the smartphone application

Outcomes:
Quality of bowel cleansing assessed by the Boston Bowel Preparation Scale
Development and Testing of an AI-Assisted Smart Interactive Healthcare Robot Program
Trial ID: NCT06972043
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-04-30
Completion date: 2026-03-27
Sponsor: National Taiwan University Hospital
Conditions: Nursing; Artificial Intelligence (AI); Deep Learning; Mechine Learning
Interventions: Intervention Group
Locations: National Taiwan University Hospital
Countries: Taiwan
Enrollment: 160
Age range: 18 Years - None
Sex: ALL

Summary:
To address workforce shortages and increasing workloads in nursing, technological solutions and AI-powered robots for ward navigation have been introduced. However, limitations remain in their application to clinical care. This study aims to develop and test a programming framework for an AI-assisted nursing care robot ("E-Nursing Assistant") to reduce nurses' workload and improve the efficiency and quality of care.

Eligibility criteria:
1. Nurse

   Inclusion Criteria:
   * Aged 18 years or older.
   * Currently employed as a ward nurse.

   Exclusion Criteria:
   * Nurses who do not actively participate in ward care, such as nursing unit supervisors.
2. Patients/Caregivers

Inclusion Criteria:

* Aged 18 years or older.
* Willing to accept the "E-Nursing Assistant" intervention for care tasks that do not involve patient safety.

Exclusion Criteria:

* Patients or caregivers who are unconscious or unable to communicate verbally or in writing to complete the research interview.

Outcomes:
Workload-related stress in nursing staff
Automatic Diagnosis of ST-segment Elevated Myocardial Infarction by Artificial Intelligence-based Electrocardiographic System:The ALERT-Pilot Study
Trial ID: NCT03317691
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2017-10-20
Completion date: 2018-11-01
Sponsor: Shanghai 10th People's Hospital
Conditions: Myocardial Infarction, Acute; Artificial Intelligence; ECG Pattern
Interventions: electrocardiogram diagnosed by artificial intelligence
Locations: Shanghai Tenth People's Hospital
Countries: China
Enrollment: 2000
Age range: 18 Years - None
Sex: ALL

Summary:
the algorithm of artificial intelligent to diagnose myocardial infarction through prior surgery Electrocardiogram was established. The accuracy of using artificial intelligent to diagnose acute ST-segment elevation myocardial infarction and judge criminal vascular was evaluated.

Eligibility criteria:
Inclusion Criteria:

1. age≥18 years old;
2. the admitting doctor's diagnosis is ST-segment Elevation Myocardial Infarction.

Exclusion Criteria:

1. the admitting diagnosis is non-ST-segment Elevation Myocardial Infarction.
2. default data;
3. pregnancy, mental disorder, kidney failure;
4. except for criteria mention above, including some improper condition considered by investigators.

Outcomes:
the algorithm of artificial intelligent to diagnose myocardial infarction through prior surgery Electrocardiogram; The accuracy of using artificial intelligent to diagnose acute ST-segment elevation myocardial infarction and judge criminal vascular
A Prospective Evaluation of Impact of AI Assistance on Workload Associated With Preparation of Rare Tumor Case Reports
Trial ID: NCT06026098
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-05
Completion date: 2026-12
Sponsor: UNC Lineberger Comprehensive Cancer Center
Conditions: Cognitive Burden; Cognitive Symptom
Interventions: Artificial Intelligence
Locations: Lineberger Comprehensive Cancer Center
Countries: United States
Enrollment: 10
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this study is to explore cognitive burden perceptions among physicians in relation to case report writing. Furthermore, this study evaluates the use of artificial intelligence (AI) assistance as a tool to reduce cognitive burden among providers preparing and submitting case reports. If an AI-tool is helpful in this setting, it may potentially help increase reporting of rare medical events and thereby improve the evidence base for care of these patient populations. This study will occur at a single time point which is expected to last approximately 2 hours. This session will include reviewing two rare tumor cases and then writing a clinical vignette with and without AI assistance.

Eligibility criteria:
Inclusion Criteria:

1\. The subject is a physician, medical student, or postdoctoral student.

Exclusion Criteria:

Outcomes:
Subjective Cognitive workload (CWL); Perceived cognitive load
Bubble: A New VR-AI Way of Treatment for Hot Flashes in Women With Breast Cancer
Trial ID: NCT04414033
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2018-02-01
Completion date: 2019-02-28
Sponsor: Bar-Ilan University, Israel
Collaborators: Hematology and Oncology Specialists
Conditions: Hot Flashes; Breast Cancer
Interventions: Bubble: A New VR-AI Way of Treatment for Hot Flashes
Locations: Bar Ilan University
Countries: Israel
Enrollment: 37
Age range: 28 Years - 55 Years
Sex: FEMALE

Summary:
This study evaluates the potential of a unique artificial intelligence-virtual reality intervention called Bubble, to reduce the number and intensity of hot flashes and their accompanying psychological symptoms in women, aged 28-55, with breast cancer. A sample of 37 women with breast cancer were selected to the study. Participants were asked to answer a survey before and after using Bubble. The treatment period was 24 days. The findings are positive and show that the use of Bubble help to reduce several psychological symptoms connected to hot flashes.

Eligibility criteria:
Inclusion Criteria:

* Females between the ages of 18 and 60
* Having an existing diagnosis of breast or ovarian cancer
* Receiving standard-of-care chemotherapy, endocrine therapy or estrogen blocking anti-cancer treatment
* The woman reported experiencing hot flashes at least once per day for a duration of at least 7 days prior to the beginning of the study and hot flashes for at least 30 days prior to the beginning of the study
* No chronic migraine headaches, seizure disorders, serious vestibular disorders, or pathological vertigo
* No serious psychiatric disorders
* Not pregnant
* No current substance abuse
* Not participating in other forms of therapy
* Not using medical marijuana.

Exclusion Criteria:

\* women aged 28-55 with breast cancer suffering from hot flashes

Outcomes:
Demographic and background (disease & treatment-elated) information; HFRDIS, The Hot Flash Related Daily Interference Scale; HFRDIS, The Hot Flash Related Daily Interference Scale; HFRS, The Hot Flash Rating Scale; HFRS, The Hot Flash Rating Scale; PSS, The Perceived Stress Scale; PSS, The Perceived Stress Scale; K10, The Kessler Psychological Distress Scale; K10, The Kessler Psychological Distress Scale; B-IPQ, The Brief Illness Perception Questionnaire; B-IPQ, The Brief Illness Perception Questionnaire; PSQI, The Pittsburgh Sleep Quality Index; PSQI, The Pittsburgh Sleep Quality Index; WHOQOL-BREF, The World Health Organization Quality of Life Scale; WHOQOL-BREF, The World Health Organization Quality of Life Scale
An Artificial Intelligence-Based Screening Tool to Detect Psychological Distress
Trial ID: NCT07092085
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-03-01
Completion date: 2025-04-01
Sponsor: The Eye Hospital of Wenzhou Medical University
Conditions: Mental Disease
Interventions: AI model
Locations: Peking Union Medical College
Countries: China
Enrollment: 17386
Age range: 14 Years - 40 Years
Sex: ALL

Summary:
The goal of this observational study is to test an artificial intelligence (AI) tool that can help screen for mental health risks . The main questions it aims to answer are:

Can an AI model that analyzes a person's voice, facial expressions, and language accurately identify students who may be at high risk for mental health conditions, such as depression or OCD?

How accurate is the AI model when compared to results from standard mental health questionnaires?

Participants will be asked to:

Complete a standard mental health questionnaire.

Provide consent for their data to be used in the research.

Participate in a recorded session to collect video and audio data for the AI model to analyze.

Eligibility criteria:
Inclusion Criteria:

* Enrolled as a student at a participating university.
* Age between 14 and 40 years, inclusive.
* Willing and able to provide written informed consent.
* Fluent in the language required for the study.

Exclusion Criteria:

* Inability to provide video or audio data of sufficient quality for analysis.

Outcomes:
Sensitivity; AUROC; Specificity of the AI Model for Mental Health Screening
Screening Program for Structural Heart Disease Among Primary and Secondary School Students in Ruyang County
Trial ID: NCT07194785
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2025-10-27
Completion date: 2026-01-05
Sponsor: Heart Health Research Center
Conditions: Structural Heart Disease
Interventions: AI-Assisted Cardiac Auscultation using the HearTech Smart Stethoscope
Locations: Ruyang County People's Hospital
Countries: China
Enrollment: 6614
Age range: 10 Years - 18 Years
Sex: ALL

Summary:
The goal of this observational diagnostic study is to evaluate whether an artificial intelligence (AI)-enabled smart stethoscope can accurately detect structural heart disease in school-aged children and adolescents (10-18 years) in Ruyang County, China.

The main questions it aims to answer are:

Can the smart stethoscope reliably identify students with cardiac murmurs that indicate possible structural heart disease? How well do the sensitivity, specificity, and predictive values of the smart stethoscope compare with standard echocardiography?

Researchers will compare AI-assisted stethoscope screening results with echocardiography (gold standard) to see if the device can be used as an effective early screening tool.

Participants will:

Undergo a heart sound screening using the AI-enabled smart stethoscope (3-5 minutes).

If screening is positive, receive a free echocardiogram at Ruyang County People's Hospital.

A small sample of students with negative screening results will also receive echocardiography to check for missed cases.

Eligibility criteria:
Inclusion Criteria:

1. School students aged 10-18 years.
2. Able to cooperate with cardiac auscultation.
3. Student and parent/guardian provide written informed consent.

Exclusion Criteria:

1. Student or parent/guardian declines participation.
2. Inability or unwillingness to follow screening procedures or cooperate with cardiac auscultation.
3. Refusal to undergo standard transthoracic echocardiography or cardiology evaluation.
4. Previously diagnosed structural heart disease.
5. Chest wall deformities or skin conditions that may interfere with auscultation.
6. Fever ≥37.5 °C on the day of examination, or severe developmental delay or other conditions preventing cooperation with the examination.

Outcomes:
Specificity of AI-enabled smart stethoscope; Positive Predictive Value (PPV) of AI-enabled smart stethoscope; Negative Predictive Value (NPV) of AI-enabled smart stethoscope
AI-based Low Dose CBCT Reconstruction for Lung Puncture Guidance: A Multicenter Randomized Controlled Study
Trial ID: NCT06831617
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-03-01
Completion date: 2025-11-30
Sponsor: Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
Conditions: Lung Nodule
Interventions: AI-based low dose CBCT reconstructed images system; Conventional CBCT images system
Locations: Wuhan Union Hospital; Wuhan Union Jinyinhu Hospital; Wuhan Union West Hospital
Countries: China; China; China
Enrollment: 400
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
The goal of this clinical trial is to learn if AI-based low dose CBCT reconstructed images can guide lung puncture effectively. The main questions it aims to answer are:

1. Does the AI-based low dose CBCT reconstruction model reconstruct high quality images?
2. Is it possible that low-dose CBCT reconstructed images can guide lung puncture procedures without compromising the efficiency of the procedure? Researchers will compare AI-based low dose CBCT reconstructed images to a placebo (conventional CBCT images) to see if AI-based low dose CBCT reconstructed image can guide lung puncture procedures without compromising the efficiency of the procedure.

Participants will:

1. Undergo lung puncture under AI-based low dose CBCT reconstructed images guidance or under conventional CBCT images
2. Be followed up for 1 week postoperative to obtain patient complications

Eligibility criteria:
Inclusion Criteria:

* Participants who require CBCT-guided precutaneous lung puncture (PLP) and meet the clinical indications for the procedure.
* Participants whose physical condition is suitable for PLP.
* Participants are willing to sign informed consent.

Exclusion Criteria:

* Participants have metallic implants in the body, which severely affects the image quality.
* Participants are pregnant or breastfeeding.
* Participants are unwilling or unable to sign informed consent.

Outcomes:
Number of puncture needles of participants undergoing percutaneous lung puncture procedures.
A-EYE: A Mixed Quantitative and Qualitative Study to Develop and Evaluate the Application of Artificial Intelligence (AI) Methods Using Retinal Imaging for the Identification of Adverse Retinal Changes Associated With Cancer Therapies.
Trial ID: NCT04901468
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-06-18
Completion date: 2022-12-31
Sponsor: University of Manchester
Collaborators: Institute of Cancer Research, United Kingdom
Conditions: All Comers
Interventions: No Intervention
Locations: Manchester Royal Eye Hospital
Countries: United Kingdom
Enrollment: 350
Age range: 18 Years - None
Sex: ALL

Summary:
This is a data collection study involving the gathering of clinical data and OCT (optical coherence tomography) scans from 350 patients.

The purpose of this study is to gather data to help develop an AI algorithm to detect eye abnormalities specifically those related to certain cancer treatments.

At the end of the study interviews will be held with expert ophthalmologists to assess the acceptability of implementing AI into clinical practice.

Eligibility criteria:
Inclusion Criteria:

Patients are eligible for the study if all inclusion criteria are met:

1. Voluntary informed consent.
2. Aged at least 18 years.
3. Fully registered patient attending the Manchester Royal Eye Hospital
4. Patients are having an optical diagnostic imaging as part of their standard of care.

Exclusion Criteria:

Patients are excluded from the study if any of the following criteria apply:

1\. Patient who are deemed clinically unable to be scanned by healthcare professional.

Outcomes:
Measure of the diagnostic accuracy of the AI algorithm against gold standard clinical assessment associated with cancer treatment.