moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Comparing an Electronic Approach to the Human Massage Therapist.
Trial ID: NCT07040397
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-01-30
Completion date: 2026-10-01
Sponsor: Mayo Clinic
Conditions: Lower Back Pain
Interventions: EMMA massage therapy; Clinical massage treatment
Locations: Mayo Clinic in Rochester
Countries: United States
Enrollment: 40
Age range: 18 Years - None
Sex: ALL

Summary:
The purpose of this study is to gather information about patient experience receiving a massage through a robotic EMMA (Expert Manipulative Massage Automation) compared to receiving a massage from a massage therapist.

Eligibility criteria:
Inclusion Criteria:

* be 18 years of age or older.
* report a primary pain point in the lower back.
* Pain intensity reported at baseline
* Women of childbearing potential who self-report not being pregnant and agree to employ an effective method of birth control (surgical sterilization or oral contraceptives, barrier method with spermicides, intrauterine device, etc.) during the study period
* be able to participate fully in all aspects of the study.
* have understood and signed study informed consent.

Exclusion Criteria:

* Pregnancy or breastfeeding
* Patients with the inability to stay in a prone position
* Patients with bleeding disorders
* Patients with allergies and/or local skin affectations
* have used pain medications or participated in a pain treatment within three days of study enrollment.
* have an implanted device (including a lap band) in the targeted area of massage therapy.
* have used an investigational drug within 30 days of study enrollment.
* have a history of any major cardiovascular events including heart valve disease, ongoing angina, cardiac arrhythmias, congestive heart failure, acute coronary syndrome, stroke, transient ischemic attack, or peripheral vascular disease.
* have clinically significant acute or chronic progressive or unstable neurologic, hepatic, renal, cardiovascular, lymphatic, respiratory, metabolic disease (such as uncontrolled diabetes type 2) active cancer, actively receiving treatment for cancer or within 1 year of cancer remission.
* surgical intervention for pain within 1 month prior to enrollment.
* active infection, wound or other external trauma to the areas to be treated with massage therapy.
* have a known history of any condition or factor judged by the investigator to preclude participation in the study or which might hinder adherence.

Outcomes:
Number of patients enrolled
Pain, Physical Activity, Posture and Quality of Life in Individuals With Idiopathic Scoliosis Who Have Had COVID-19
Trial ID: NCT06006897
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-06-16
Completion date: 2024-12-30
Sponsor: Izmir Democracy University
Conditions: COVID-19 Respiratory Infection; Idiopathic Scoliosis
Interventions: Physical Evaluations of post-COVID-19 individuals; Physical evaluations of non-post-COVID-19 individuals
Locations: Izmir Democracy University
Countries: Turkey (Türkiye)
Enrollment: 41
Age range: 18 Years - None
Sex: ALL

Summary:
It is not known to what extent the COVID-19 virus affects individuals with scoliosis during the prolonged COVID-19 pandemic. Therefore, in this study it was aimed to comparatively investigate pain severity, posture disorders that can be assessed by artificial intelligence, physical activity levels and quality of life in individuals with idiopathic scoliosis with and without COVID-19.

Eligibility criteria:
Inclusion Criteria for individuals with idiopathic scoliosis who have experienced COVID-19:

* being 18 years of age or older
* Volunteering to participate in the study
* Having been diagnosed with idiopathic scoliosis
* Being able to walk independently
* Having been diagnosed with COVID-19 and having recovered and been discharged.

Inclusion criteria for individuals with idiopathic scoliosis who have not had COVID-19;

* being 18 years of age or older
* Volunteering to participate in the study
* Being diagnosed with idiopathic scoliosis

Exclusion criteria for individuals with idiopathic scoliosis who have had COVID-19;

* Having been diagnosed with COVID-19 in the last 15 days
* Being suspected of COVID-19
* Having mental problems that may affect cooperation
* Having an acute or chronic infection that may affect the evaluations to be made within the scope of the study.
* Participating in any professional sports activity
* Having any surgery to prevent walking, having a chronic disease, orthopedic/neurological/cardiopulmonary disease, physical or mental disability and/or cognitive impairment
* Using analgesics and other interactive drugs that will affect assessments
* Being pregnant

Exclusion criteria for individuals with idiopathic scoliosis who have not had COVID-19;

* Having had at least one COVID-19
* Being suspected of COVID-19
* Having any mental or physical disability
* Having any acute or chronic illness
* Having analgesic drug use that will affect the evaluations . Being pregnant

Outcomes:
Daily average step count
Effects of Artificial Intelligence-based Diabetic Retinopathy Screening on Timely Access to Treatment in Individuals With Diabetes
Trial ID: NCT07236879
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-08-08
Completion date: 2026-12
Sponsor: Hospital de Clinicas de Porto Alegre
Collaborators: Fundação de Amparo à Pesquisa do Estado de São Paulo; Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul, Brazil; TelessaúdeRS / UFRGS
Conditions: Diabetic Retinopathy
Interventions: Mobile retinography interpreted by artificial intelligence; Mobile retinography interpreted by ophthalmologists
Locations: Hospital de Clínicas de Porto Alegre
Countries: Brazil
Enrollment: 922
Age range: 18 Years - None
Sex: ALL

Summary:
This is a clinical trial to evaluate the effects of universal screening for diabetic retinopathy (DR) and diabetic macular edema (DME) using artificial intelligence (AI) in the interpretation of fundus photographs obtained by trained nursing assistant using a portable fundus camera in a primary care setting, compared with images obtained by the same method, but interpreted by ophthalmologists.

Eligibility criteria:
Inclusion Criteria:

* Adults (\> 18 years old) diagnosed with diabetes mellitus who agree to participate in the study.

Exclusion Criteria:

* Any contraindication for pharmacological mydriasis (such as knowledge of having closed-angle glaucoma, pregnancy).
* Life expectancy less than 6 months

Outcomes:
Number of appropriate referrals to ophthalmologist
A Prospective Clinical Study of Radiomics-Based Artificial Intelligence for Predicting Para-Aortic Lymph Node Metastasis in Patients With Gastric Cancer
Trial ID: NCT06947096
Status: ENROLLING_BY_INVITATION
Study type: OBSERVATIONAL
Start date: 2025-01-01
Completion date: 2025-06-30
Sponsor: Qun Zhao
Collaborators: First Hospital of Shijiazhuang City; Baoding First Central Hospital; Hengshui People's Hospital
Conditions: Gastric Cancer; Para-Aortic Lymph Node Metastasis; Lymphatic Metastasis; Preoperative Imaging Assessment; Radiomics; Artificial Intelligence
Interventions: Radiomics-Based AI Imaging Analysis
Locations: the Fourth Hospital of Hebei Medical University
Countries: China
Enrollment: 120
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
This study aims to develop and validate an artificial intelligence (AI) model based on radiomics features extracted from preoperative CT images to predict para-aortic lymph node (PALN) metastasis in patients with gastric cancer. Accurately identifying PALN metastasis before surgery can help doctors make better treatment decisions, such as whether to proceed with surgery, consider chemotherapy, or use other treatment strategies. The study will prospectively enroll patients who are diagnosed with gastric cancer and scheduled for surgery. All participants will undergo routine imaging tests, and their data will be analyzed using advanced AI techniques. The results of this study may improve the precision of preoperative staging and support personalized treatment planning for gastric cancer patients.

Eligibility criteria:
Inclusion Criteria:

1. Adults aged 18-80 years.
2. Histologically confirmed gastric adenocarcinoma.
3. Planned to undergo radical gastrectomy with or without para-aortic lymph node dissection.
4. Preoperative contrast-enhanced abdominal CT scan available within 3 weeks before surgery.
5. No evidence of distant metastasis on imaging.
6. ECOG performance status 0-2.
7. Provided written informed consent.

Exclusion Criteria:

1. History of other malignant tumors within the past 5 years.
2. Received neoadjuvant chemotherapy or radiotherapy prior to CT imaging.
3. Poor-quality or incomplete CT images not suitable for radiomics analysis.
4. Severe comorbidities that may affect prognosis or surgical decision-making.
5. Pregnancy or breastfeeding.
6. Inability to provide informed consent or comply with study procedures.

Outcomes:
Diagnostic Accuracy of the AI Radiomics Model for Predicting Para-Aortic Lymph Node Metastasis in Gastric Cancer
Multimodal Clinical-imaging-pathology-driven Artificial Intelligence Model for Predicting Postoperative Recurrence of Locally Advanced Gastric Cancer
Trial ID: NCT06690268
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2022-01-01
Completion date: 2024-10-31
Sponsor: Qun Zhao
Conditions: Gastric Adenocarcinoma
Interventions: Multimodal AI-driven predictive model
Locations: the Fourth Hospital of Hebei Medical University
Countries: China
Enrollment: 93
Age range: 18 Years - 75 Years
Sex: ALL

Summary:
This study focuses on developing an advanced model that combines clinical information, imaging, and pathology data to predict the likelihood of cancer returning after surgery in patients with locally advanced gastric cancer. By using artificial intelligence (AI), this model analyzes various data sources to create a more accurate prediction of recurrence risk, which can help doctors, patients, and families better understand the chances of recurrence. This AI-driven approach allows healthcare providers to make more informed decisions about personalized follow-up care and potential additional treatments to improve patient outcomes.

Eligibility criteria:
\*\*Inclusion Criteria:\*\*

* Patients diagnosed with locally advanced gastric cancer (Stage II or III).
* Patients who have undergone surgical resection for gastric cancer.
* Patients with complete clinical, imaging, and pathology data available for analysis.
* Age 18 years or older.
* Patients who provide informed consent to participate in the study.

\*\*Exclusion Criteria:\*\*

* Patients with distant metastasis (Stage IV) at the time of diagnosis.
* Patients with incomplete or missing clinical, imaging, or pathology data.
* Patients who have received prior treatment for gastric cancer other than surgical resection.
* Patients with other concurrent malignancies.
* Patients who are unable or unwilling to comply with the study follow-up requirements.

Outcomes:
Prediction accuracy of postoperative recurrence in locally advanced gastric cancer
How Are You Coping? Assessing the Psychological, Social, and Economic Impacts of an Emerging Pandemic
Trial ID: NCT04369690
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2020-04-03
Completion date: 2021-04-03
Sponsor: University of Ottawa
Collaborators: Centre Integre Universitaire de Sante et Services Sociaux du Nord de l'ile de Montreal; Sunnybrook Health Sciences Centre; Southlake Regional Health Centre; Ottawa Hospital Research Institute; Centre for Addiction and Mental Health; Cheo Research Institute; Ottawa Heart Institute Research Corporation; McGill University; Johns Hopkins University; Université de Montréal; The Royal's Institute of Mental Health Research
Conditions: COVID-19; Chronic Disease; Psychiatric Disorder; Cardiovascular Diseases
Locations: Southlake Regional Health Centre; CHEO Research Institute; The Ottawa Hospital Research Institute; The Centre for Addiction and Mental Health; The University of Ottawa Heart Institute; The Royal's Institute of Mental Health Research; Sunnybrook Health Sciences Centre; Hôpital en santé mentale Rivière-des-Prairies (CIUSSS du Nord-de-l'Île-de-Montréal); McGill University; University of Montréal
Countries: Canada; Canada; Canada; Canada; Canada; Canada; Canada; Canada; Canada; Canada
Enrollment: 1000
Age range: 12 Years - None
Sex: ALL

Summary:
A dynamic analytical tool is being implemented to monitor the health, psychosocial and economic impacts of the COVID-19 pandemic as the crisis unfolds. A longitudinal survey is distributed via a network of hospitals, provincial/national organizations and web platforms. The survey information can be linked to provincial health administrative data and metrics derived from social media activity based on artificial intelligence methods. Targeted questions are included for critical populations such as healthcare workers and people with chronic illnesses.

Eligibility criteria:
Inclusion Criteria:

* All individuals who are at least 12 years of age

Exclusion Criteria:

* None

Outcomes:
Mental health - Stress; Mental health - Anxiety; Mental health - Depression
Ultra-Fast Whole-Body Bone Tomoscintigraphies Achieved With a High-Sensitivity 360° CZT Camera and a Dedicated Deep Learning Noise Reduction Algorithm
Trial ID: NCT06281015
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-08-30
Completion date: 2023-10-30
Sponsor: Central Hospital, Nancy, France
Conditions: Bone Lesion
Interventions: Whole-body bone single photon emission tomography (SPECT) for detection or follow-up of bone metastasis
Locations: CHRU Nancy
Countries: France
Enrollment: 19
Age range: 18 Years - 95 Years
Sex: ALL

Summary:
This study aimed to determine whether the whole-body bone Single Photon Emission Computed Tomography (SPECT) recording times of around 10 minutes, routinely provided by a high-sensitivity 360 degrees cadmium and zinc telluride (CZT) camera, can be further reduced by a deep learning noise reduction (DLNR) algorithm.

Eligibility criteria:
Inclusion Criteria:

* patients referred to fast whole-body bone single photon emission tomography for detection or follow-up of bone metastasis

Outcomes:
Assess a dedicated deep learning noise reduction algorithm
Evaluation of an Artificial Intelligence Algorithm Reducing Noise on Fast Whole-body Bone Tomoscintigraphy Acquisitions Recorded by a 360 Degree Cadmium-Zinc-Tellurid Camera
Trial ID: NCT06782438
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-02-27
Completion date: 2025-03-30
Sponsor: Central Hospital, Nancy, France
Conditions: Bone Scan
Interventions: artificial intelligence algorithm
Locations: Nuclear Medicine Department
Countries: France
Enrollment: 20
Age range: 18 Years - 99 Years
Sex: ALL

Summary:
Recently, artificial intelligence algorithms reducing noise by deep learning have been developed with application to SPECT and PET images.

Many studies have reported the possibility of reducing the recording time in bone scintigraphy by applying artificial intelligence algorithms reducing noise

Eligibility criteria:
Inclusion Criteria:

Patients who had a whole-body thee dimensions bone scan for rheumatological or oncological indications.

Exclusion Criteria:

Patients opposed to the use of their data

Outcomes:
To compare imaging treated by the intelligence artificial algorithm with imaging treated with the traditionnal filter artificial algorithm
Use of Multiscale Omics to Develop a Cohort Database and Study Platform in Breast Cancer Survivors
Trial ID: NCT04999826
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2021-04-01
Completion date: 2022-12-30
Sponsor: Mayo Clinic
Conditions: Anatomic Stage 0 Breast Cancer AJCC v8; Anatomic Stage I Breast Cancer AJCC v8; Anatomic Stage IA Breast Cancer AJCC v8; Anatomic Stage IB Breast Cancer AJCC v8; Anatomic Stage II Breast Cancer AJCC v8; Anatomic Stage IIA Breast Cancer AJCC v8; Anatomic Stage IIB Breast Cancer AJCC v8; Anatomic Stage III Breast Cancer AJCC v8; Anatomic Stage IIIA Breast Cancer AJCC v8; Anatomic Stage IIIB Breast Cancer AJCC v8; Anatomic Stage IIIC Breast Cancer AJCC v8; Prognostic Stage 0 Breast Cancer AJCC v8; Prognostic Stage I Breast Cancer AJCC v8; Prognostic Stage IA Breast Cancer AJCC v8; Prognostic Stage IB Breast Cancer AJCC v8; Prognostic Stage II Breast Cancer AJCC v8; Prognostic Stage IIA Breast Cancer AJCC v8; Prognostic Stage IIB Breast Cancer AJCC v8; Prognostic Stage III Breast Cancer AJCC v8; Prognostic Stage IIIA Breast Cancer AJCC v8; Prognostic Stage IIIB Breast Cancer AJCC v8; Prognostic Stage IIIC Breast Cancer AJCC v8
Interventions: Biospecimen Collection; Quality-of-Life Assessment; Questionnaire Administration
Locations: Mayo Clinic in Rochester
Countries: United States
Enrollment: 105
Age range: 18 Years - 75 Years
Sex: FEMALE

Summary:
This study gathers information to create a database to improve the ability to diagnose cancer, predict long term health of breast cancer patients, and help develop future treatment products. This study will provide a foundational platform for therapeutic development and intervention studies in the future and may for therapeutic development and intervention studies in the future, and may advance researchers understanding of the contribution gut bacteria to altered circulating estrogens in breast cancer survivors.

Eligibility criteria:
Inclusion Criteria:

* BCS GROUP
* Female patients diagnosed with breast cancer age 18 - 75, stages 0 through 3 at time of diagnosis
* Who have completed active therapy (including surgery, radiation, and/or chemotherapy)
* Able to speak English and complete surveys and provide the specimen (willing to follow the sample collection instructions)
* Able to read, understand and sign inform consent
* CONTROL GROUP
* Healthy Volunteers age 18 - 75
* Able to speak and read English, complete consent, surveys, and provide specimens

Exclusion Criteria:

* Unable to give written consent
* Unable to fast before providing blood and urine
* Pregnant women (per participant report) and males
* Unwilling to travel to Mayo Clinic Rochester to provide bio specimens

Outcomes:
Utilization of multiscale omics to build a cohort database for breast cancer survivors
Barriers and Facilitators to Nursing Record With AI Technology Application: A Mixed Methods Study Using the Consolidated Framework for Implementation Research
Trial ID: NCT07532343
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-05-01
Completion date: 2027-04-30
Sponsor: National Yang Ming Chiao Tung University Hospital
Conditions: Implementation Research; Artificial Intelligence (AI); Nursing Documentation Burden
Interventions: AI-based technologies systems to assist with nursing record
Locations: National Yang Ming Chiao Tung University
Countries: Taiwan
Enrollment: 271
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this study is to examine the facilitators and barriers to the comprehensive implementation of AI technology in nursing documentation. The main questions it aims to answer are:

What are facilitators to the comprehensive implementation of AI technology in nursing documentation? What are barriers to the comprehensive implementation of AI technology in nursing documentation? What strategies can help to fully utilize artificial intelligence technology in nursing documentation?

Eligibility criteria:
Inclusion Criteria:

* Nurses assigned to ward

Exclusion Criteria:

* Nurses without record-keeping task
* Those assigned to intensive care units, emergency room, or operating rooms

Outcomes:
The pragmatic context assessment tool
Epic Generative Artificial Intelligence Chart Summarization Tool to Reduce Ambulatory Provider Cognitive Task Load: A Randomized Controlled Trial
Trial ID: NCT07438743
Status: ACTIVE_NOT_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-02-23
Completion date: 2026-05
Sponsor: University of California, Los Angeles
Conditions: Quality Improvement
Interventions: GenAI Chart Summarization
Locations: University of California, Los Angeles
Countries: United States
Enrollment: 284
Age range: None - None
Sex: ALL

Summary:
This is a RCT of 284 outpatient physicians at a large academic health system, randomized 1:1 to an electronic health record (EHR) produced generative AI outpatient chart summarization tool or a usual-care control group. The 90 day study will observe the effects of the tool prior to system-wide roll out of the tool.

Eligibility criteria:
Inclusion Criteria:

* Ambulatory care providers within the UCLA Health system including physicians and advanced practice providers (APPs), such as nurse practitioners and physician assistants with at least one half-day clinic session per week.
* Providers complete baseline pre-survey

Exclusion Criteria:

• Trainee providers (e.g., residents, medical students), and psychologists

Outcomes:
Change from Baseline Physician Task Load
Artificial Intelligence for Early Detection of Peripheral Artery Disease
Trial ID: NCT06505317
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-07-01
Completion date: 2028-06-30
Sponsor: University of California, San Diego
Collaborators: Stanford University; National Institute on Aging (NIA)
Conditions: Peripheral Arterial Disease
Interventions: AI-based PAD screening intervention
Enrollment: 7800
Age range: 50 Years - 85 Years
Sex: ALL

Summary:
The goal of this clinical trial is to test an AI-based screening tool that will help to identify patients at high risk of having undiagnosed peripheral artery disease. The primary outcome measure is overall rate of new PAD diagnoses. Secondary outcomes include rate of new secondary prevention measures initiated for PAD, which will include new prescriptions for antiplatelets, PAD-dosed rivaroxaban, statins, smoking cessation counseling or referrals, and/or supervised exercise therapy referrals also aggregated at a clinic and site level.

Eligibility criteria:
Inclusion Criteria:

* Aged 50-85 years
* Presenting to an outpatient appointment at UCSDH, SDVA, or SHC
* No previous diagnosis of PAD
* No prior PAD alert triggered for a previous visit

Exclusion Criteria:

* \<50 years of age or \> 85 years of age
* Prior diagnosis of PAD

Outcomes:
PAD Diagnosis Rate