moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Emotional Assessment and Tiered Individualized Psychological Support for Newly Diagnosed Postoperative Gynecologic Patients: Development and Testing of the E-MOTE Artificial Intelligence Robot
Trial ID: NCT07365761
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-10-01
Completion date: 2026-07-31
Sponsor: National Taiwan University Hospital
Conditions: Emotional Assessment; Nursing; Gynecological Cancers
Interventions: E-MOTE group
Locations: National Taiwan University
Countries: Taiwan
Enrollment: 130
Age range: 18 Years - None
Sex: ALL

Summary:
To develop the E-MOTE robot and evaluate its effectiveness in addressing psychological distress, physical symptom, stress index, and quality of life, as well as to explore its clinical applicability.

Eligibility criteria:
Stage 1 (For patients and nurse) \*\*\*Patients\*\*\*

Inclusion Criteria:

* Aged 18 years or older.
* Diagnosed with gynecologic cancer and undergoing surgery.
* Willing to receive E-MOTE robot and wear a smart wearable device.
* Willing to complete pre-test and post-test questionnaires.

Exclusion Criteria:

* Unable to communicate clearly.
* Unwilling or unable to participate in the study in its entirety.

\*\*\*Nurse\*\*\*

Inclusion Criteria:

* Aged 18 years or older.
* As a clinical nurse and has cared for patients who received the E-MOTE robot

Exclusion Criteria:

\- Unwilling to participate in the qualitative interview.

Stage 2 (only for patients) \*\*\*Intervention group\*\*\*

Inclusion Criteria:

* Aged 18 years or older.
* Diagnosed with gynecologic cancer and scheduled to undergo surgery.
* Willing to receiveE-MOTE robot and to wear a smart wearable device.
* Willing to complete pre-test and post-test questionnaires.

Exclusion Criteria:

* Unable to communicate clearly.
* Unwilling or unable to participate in the study in its entirety.

\*\*\*Control group\*\*\*

Inclusion Criteria:

* Aged 18 years or older.
* Diagnosed with gynecologic cancer and scheduled to undergo surgery.
* Willing to complete pre-test and post-test questionnaires.

Exclusion Criteria:

* Unable to communicate clearly.
* Unwilling or unable to participate in the study in its entirety.

Outcomes:
Distress; Depression
Investigation of Posture, Core Stabilization, Respiratory Muscle Strength and Function, and Quality of Life in Women With Urinary Incontinence
Trial ID: NCT06988683
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-01-05
Completion date: 2024-06-24
Sponsor: Izmir Democracy University
Conditions: Urinary Incontinence (UI)
Locations: Izmir Democracy University
Countries: Turkey (Türkiye)
Enrollment: 62
Age range: 18 Years - None
Sex: FEMALE

Summary:
When the literature was examined, it was observed that there were studies on parameters such as quality of life, pelvic floor muscle strength in women with urinary incontinence, but there were not enough studies on posture, respiratory function, constipation and cough strength. Therefore, in this study, the researchers aimed to investigate posture, constipation, core muscle stabilisation, respiratory muscle strength and function, cough strength and quality of life of women with urinary incontinence and to compare these parameters with women without urinary incontinence.

Eligibility criteria:
Inclusion Criteria:

1. Inclusion Criteria for Women with Urinary Incontinence

   * Being diagnosed with urinary incontinence
   * Volunteer to participate in the study
   * To be over 18 years of age
   * To be able to communicate verbally and in written Turkish
2. Inclusion Criteria for Women without Urinary Incontinence

   * No symptoms of urinary incontinence
   * Volunteer to participate in the study
   * To be over 18 years of age
   * Establish written and verbal communication in Turkish

Exclusion Criteria:

* The presence of any pathology that may affect pelvic floor function
* Trauma to the lumbopelvic, abdominal, thoracic or lower extremities within the last six months
* Urinary tract infection
* Grade III or higher urogenital prolapse
* Neurological or psychiatric illness
* Being pregnant

Outcomes:
Maximal inspiratory pressure (MIP); Maximal expiratory pressure (MEP)
A DeepSeek-Powered AI System for Automated Chest Radiograph Interpretation in Clinical Practice
Trial ID: NCT07117266
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-08-01
Completion date: 2025-08-12
Sponsor: Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
Collaborators: The First Affiliated Hospital of Zhengzhou University; The First Affiliated Hospital of Henan University of Science and Technology
Conditions: X-Ray; AI (Artificial Intelligence); Radiology
Interventions: radiologists reference AI reports
Locations: The First Affiliated Hospital of Henan University of science and technology; Union Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology; The First Affiliated Hospital of Zhengzhou University
Countries: China; China; China
Enrollment: 296
Age range: None - None
Sex: ALL

Summary:
There's a global shortage of radiologists. Radiology AI's automatic reporting is key for boosting efficiency and meeting patient needs, especially in resource-poor areas. Multimodal large models enable medical image auto-reporting systems. ChatGPT 4o can diagnose medical images but has issues like being closed-source and "hallucinations." The new open-source Janus Pro 1B-with strong performance, "any-to-any" capability, low cost, and open access-shows potential for medical imaging tasks with training. But little research explores its use here; most models are general, lacking field-specific optimization and systematic evaluation. This study will develop Janus Pro 1B-CXR (a medical image-specific model) via public data, test its value in diagnosis and reporting, and build an efficient automated system.

Eligibility criteria:
Inclusion Criteria:

1. clinically suspected thoracic diseases (such as pneumonia, tuberculosis, or lung cancer) requiring CXR-assisted diagnosis;
2. patients providing written informed consent for research data use;
3. complete clinical records (including chief complaints, medical history, and laboratory test results);
4. patients with no historical chest X-ray images and no need for comparison with previous chest X-ray images;
5. Patients who underwent only posteroanterior (PA) chest X-rays without lateral chest X-rays.

Exclusion Criteria:

1. substandard CXR image quality (including severe motion artifacts, over-/underexposure, or missing anatomical structures);
2. pregnant or lactating women.

Outcomes:
Report quality scores in the prospective study; Agreement evaluation in the prospective study; Pairwise preference tests in the prospective study; Reading Time in the prospective study
DECIDE Registry: AI DErived Plaque Quantification: CCTA and AI-QCPA for Determining Effective CAD Management
Trial ID: NCT06376851
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-03-27
Completion date: 2030-03-27
Sponsor: HeartFlow, Inc.
Conditions: Coronary Artery Disease
Interventions: AI-enabled quantitative coronary plaque analysis (AI-QCPA)
Locations: Moses Cone Memorial Hospital
Countries: United States
Enrollment: 20000
Age range: 18 Years - None
Sex: ALL

Summary:
The objective of HeartFlow's DECIDE Registry is to collect observational data about the management of patients before and after HeartFlow Artificial Intelligence-Quantitative Coronary Plaque Analysis (AI-QCPA).

Eligibility criteria:
Inclusion Criteria (all must be present) :

1. Groups 1, 2,4,5 and 6: CCTA and plaque detected
2. Group 3: Any patient with a stress test (stress echocardiogram or nuclear imaging including SPECT or PET) and no CCTA within the 90 days prior to site activation
3. Groups 1,2,3 and 4: Clinically stable, symptomatic
4. Group 6: Clinically stable without symptoms suggestive of CAD at the time of the CCTA

Exclusion Criteria (all must be absent) :

1. ED presentation (at the time of the CCTA or stress test)
2. Groups 1,2,3,4, and 6: Previous history of CAD (prior to the CCTA or stress test with history of revascularization)
3. Group 5: Prior history of PCI in multiple vessel territories, PCI in the Left Main, CABG, and/or any other conditions or limitations which would prevent successful CCTA processing by HeartFlow
4. Acute chest pain (in patients who have not been ruled out for ACS)
5. Previously evaluated with and/or in a study which includes AI-QCPA

Outcomes:
Primary Endpoint
Evidently Workflow Study: Randomized Evaluation of a Clinician-Facing AI Documentation Support Tool in Outpatient Specialty Practice
Trial ID: NCT07498582
Status: ENROLLING_BY_INVITATION
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-03-30
Completion date: 2026-06
Sponsor: University of North Carolina, Chapel Hill
Conditions: Burnout, Healthcare Workers; Clinical Workflow Optimization; Health Information Technology; Electronic Health Records
Interventions: AI Clinical Summarization Tool (Evidently)
Locations: UNC Health
Countries: United States
Enrollment: 150
Age range: None - None
Sex: ALL

Summary:
This study looks at whether a clinician-facing artificial intelligence (AI) tool can help outpatient doctors spend less time reviewing medical records and documenting care. The tool creates brief summaries of existing medical record information to support routine clinical work. This study examines how use of this tool affects clinicians' workload, time spent in the electronic health record, and overall experience with documentation. The goal is to better understand whether AI documentation support tools can improve efficiency and reduce burden for clinicians in outpatient specialty practice.

Eligible UNC outpatient specialists may be invited to complete surveys and, if they qualify, are randomly assigned either to receive access to the tool (Evidently) at the beginning of the study period or to continue their usual workflow for eight weeks before receiving access. The study does not recruit patients and does not change medical care for patients.

Eligibility criteria:
Inclusion Criteria:

Participants must meet all of the following criteria:

* Attending outpatient specialists affiliated with UNCFP or UNCMG.
* At least two half-days (≥3.5 hours per session) of scheduled outpatient clinic per week.
* Primary specialty in an outpatient, patient-facing specialty practice within UNCFP or UNCMG.

Anesthesiologists will be enrolled under the same protocol but analyzed separately due to distinct workflow characteristics.

Exclusion Criteria:

Clinicians meeting any of the following criteria will be excluded:

* Trainees (e.g., residents or fellows).
* Advanced practice providers (APPs).
* Attendings whose outpatient workflows are predominantly supervisory or shared with trainees or APPs, defined as clinicians who self-report that 25% or more of outpatient clinic sessions are conducted jointly with trainees or APPs.
* Clinicians who participated in a prior Evidently pilot study.
* Clinicians with planned time away exceeding one week during the study period. Exclusion criteria will be assessed using self-report in the baseline survey, supplemented by administrative records where available (e.g., prior pilot participation or clinic volume). Clinicians meeting any exclusion criterion will not be randomized.

Outcomes:
Change in Clinician Cognitive Load
Artificial Intelligence Augmented Training in Skin Cancer Diagnostics for Skin Cancer Specialists
Trial ID: NCT04758988
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-09-15
Completion date: 2024-04-30
Sponsor: Herlev Hospital
Conditions: Melanoma; Skin Cancer
Interventions: DermLoop Learn
Locations: Herlev Hospital; Gentofte Hospital
Countries: Denmark; Denmark
Enrollment: 1500
Age range: None - None
Sex: ALL

Summary:
Background:

The worldwide incidence of skin cancer has been rising for 50 years, in particular the incidence of malignant melanoma has increased approx. 2-7% annually and is the most common cancer amongst Danes aged 15-34. Currently there is a significant amount of misdiagnosis of skin cancer and mole cancer, and most excised skin lesions are benign.

Previous studies have shown that there is no significant increase in doctors diagnostic accuracy during the first 6 years of clinical work.

The resources spend on healthy people could be put to better use, if the Benign-Malignant Ratio could be lowered. This could potentially be done by better educating the doctors during their everyday clinical practice.

Aim:

The aim of this study is to investigate the dose/response effect of an AI augmented training and clinical feedback on the diagnostic accuracy of skin cancer and clinical decisions among doctors from specialized skin cancer centers.

Research question: How much specialized doctors need to train before their diagnostic accuracy and clinical decisions change?

Eligibility criteria:
Inclusion Criteria:

* Doctors are required to work at a specialized skin department (dermatology or plastic surgery or the like).
* Doctors must be registered authorized health personnel

Exclusion Criteria:

* Doctors that have previously received access to the DermLoop Learn educational intervention
* Doctors with less than 2 months left of their affiliation with their current department of employment

Outcomes:
Dose/Response
Application Research of B-ultrasound Assisted by Artificial Intelligence in Breast Cancer Screening
Trial ID: NCT06521788
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2020-09-01
Completion date: 2023-12-31
Sponsor: Fudan University
Conditions: Breast Cancer; Screening
Interventions: AI-assisted breast ultrasound; Routine breast ultrasound
Locations: Maternal and Infant Healthy Centre of Hongkou District, Shanghai; Fudan University Shanghai Cancer Center; Shanghai Pudong New Area Healthcare Hospital For Women & Children
Countries: China; China; China
Enrollment: 21790
Age range: 35 Years - 70 Years
Sex: FEMALE

Summary:
AIBU is a randomized, open-label study assessing the effectiveness of an articificial intelligence-assisted breast ultrasound breast cancer screening strategy compared to standard screening (according to the current local guidelines in each participating area) in detecting late-stage breast cancers.

Eligibility criteria:
Inclusion Criteria:

* Women aged 35-70 who participated in breast cancer screening (Shanghai Rural Women "Women two cancer screening project", national major public health service project "Rural women breast cancer screening Project" and "Gynecological diseases and breast diseases screening project for retired and living difficult women") in Shanghai district

Exclusion Criteria:

* 1\. Personal history of breast carcinoma, either invasive or ductal carcinoma in situ (DCIS); 2. Personal history of any types of malignant neoplasms; 3. Known condition or suspicion of severe cardiopulmonary insufficiency, liver and kidney insufficiency and other systemic diseases; 4. Known condition or suspicion of serious comorbidities and average life expectancy less than 5 years; 5. History of lateral or bilateral mastectomy; 6. Disagree to participate in the study and follow-up interviews.

Outcomes:
Incidence rate of early breast cancer in each arm
Automatic Diagnosis of Spinal Stenosis on CT With Deep Learning
Trial ID: NCT03746561
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2018-11
Completion date: 2019-05
Sponsor: Shanghai 10th People's Hospital
Collaborators: Brigham and Women's Hospital; Shanghai East Hospital; Shanghai Tongji Hospital, Tongji University School of Medicine
Conditions: Spinal Stenosis
Interventions: deep learning
Enrollment: 500
Age range: 18 Years - None
Sex: ALL

Summary:
MRI is a common tool for radiographic diagnosis of spinal stenosis, but it is expensive and requires long scanning time. CT is also a useful tool to diagnose spinal stenosis, yet interpretation can be time-consuming with high inter-reader variability even among the most specialized radiologists. In this study, the investigators aim to develop a deep-learning algorithm to automatically detect and classify lumbar spinal stenosis.

Eligibility criteria:
Inclusion Criteria:

* Age \>18 years
* with radiologists' CT reports on cervical, thoracic and lumbar stenosis

Exclusion Criteria:

* not applicable (only specific levels with extensive infections, fractures, tumor, high-grade spondylolisthesis would be excluded for analysis).

Outcomes:
diagnostic accuracy of deep learning
Feasibility Analysis of LCD-SLA 3D Printing Technology for Overall Surgical Planning of Liver Malignant Tumors
Trial ID: NCT06526754
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2019-01-01
Completion date: 2024-01-01
Sponsor: Yang Jihong
Conditions: Hepatobiliary Surgery; Liver Diseases
Interventions: 3D-Printed Liver Model; Digital Simulation-Based Surgical Planning
Locations: Affiliated Hospital of Hebei University
Countries: China
Enrollment: 64
Age range: 18 Years - 75 Years
Sex: ALL

Summary:
This study aims to evaluate the effectiveness of 3D-printed liver models in hepatobiliary surgery planning compared to traditional digital simulations. It is conducted in three phases:

1. Development and validation of 35 3D-printed liver models, focusing on timeliness, cost, precision, and alignment with digital planning tools.
2. Optimization of the 3D reconstruction process using deep learning to enhance model accuracy and efficiency.
3. A retrospective comparative analysis of surgical outcomes in 64 patients, with one group using 3D-printed models and the other using digital simulations for surgical planning.

Eligibility criteria:
Inclusion Criteria:

* Age: Patients aged 18-75 years
* Gender: Both male and female patients
* Diagnosis: Confirmed diagnosis of hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (iCCA), or perihilar cholangiocarcinoma (pCCA)
* Surgical Candidates: Patients who are candidates for hepatectomy
* Liver Function: Patients with adequate liver function (Child-Pugh A or B)
* Informed Consent: Patients who provide written informed consent

Exclusion Criteria:

* Non-Surgical Candidates: Patients not eligible for surgery due to advanced disease or comorbidities
* Pregnancy: Pregnant or breastfeeding women
* Severe Comorbidities: Patients with severe cardiovascular, respiratory, renal, or other systemic diseases
* Previous Liver Surgery: Patients with a history of previous liver resection or transplantation
* Uncontrolled Infections: Patients with uncontrolled active infections
* Inability to Comply: Patients unable to comply with study procedures or follow-up

Outcomes:
Intraoperative Blood Loss; Blood Transfusion; Operation Duration; Surgical Margin Status; Postoperative Hospital Stay; Postoperative Complications
Optimizing Behavioral Healthcare Delivery Through Technology
Trial ID: NCT05745103
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-09-01
Completion date: 2023-04-30
Sponsor: Eleos Health
Collaborators: Missouri Department of Mental Health
Conditions: Mood Disorders; Anxiety Disorders
Interventions: Cognitive behavioral therapy with AI; Cognitive behavioral therapy
Locations: The Ozark Center
Countries: United States
Enrollment: 46
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this randomized controlled clinical trial is to determine the feasibility, acceptability, and preliminary efficacy of an artificial intelligence platform )שׁ( for behavioral health in facilitating better clinical outcomes for adult patients receiving outpatient therapy.

The main question\[s\] it aims to answer are:

* whether an AI platform designed for behavioral healthcare would be feasible and acceptable to patients and therapists.
* whether the depression and anxiety outcomes of adults receiving outpatient cognitive-behavioral therapy (CBT) in a community-based clinic would be superior among patients whose therapists used an AI-based platform to support clinical decision making and administrative tasks compared to patients receiving treatment-as-usual (TAU).

Participants will receive CBT for depression or anxiety and complete standardized assessments. Participants will be followed for the first two months of therapy.

Eligibility criteria:
Inclusion Criteria:

1. Older than 18 years old.
2. Presenting for an outpatient CBT for a depressive or anxiety disorder.

Exclusion Criteria:

1. Age \<18.
2. individuals who are medically unstable for outpatient treatment.
3. a severe mental health condition that might interfere with treatment compliance (e.g., psychotic depression, psychosis, active substance dependence).

Outcomes:
Patient Health Questionnaire-9 (PHQ-9); Patient Health Questionnaire-9 (PHQ-9); Patient Health Questionnaire-9 (PHQ-9); Generalized Anxiety Disorder-7 (GAD-7); Generalized Anxiety Disorder-7 (GAD-7); Generalized Anxiety Disorder-7 (GAD-7)
Comprehensive Interpretation of the Importance of Skeletal Muscle Mass on Prognosis and Recovery in Cardiac Arrest Survivors: From Machine Learning to Clinical Trial
Trial ID: NCT07034170
Status: ENROLLING_BY_INVITATION
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-06-01
Completion date: 2028-12-31
Sponsor: National Taiwan University Hospital
Conditions: Out of Hospital Cardiac Arrest; Inhospital Cardiac Arrest
Interventions: Enhanced Rehabilitation Program
Locations: Department of Emergency Medicine, National Taiwan University Hospital
Countries: Taiwan
Enrollment: 90
Age range: 18 Years - 99 Years
Sex: ALL

Summary:
The objective of this research project is to develop an automated muscle mass measurement system based on computed tomography (CT) images using deep learning and artificial intelligence, and to construct a prognostic prediction model for patients who have experienced cardiac arrest and undergone resuscitation. The model will be validated using multicenter registry data and medical imaging. Furthermore, a clinical trial involving core muscle rehabilitation for post-resuscitation patients will be conducted to evaluate both short-term and long-term outcomes, aiming to comprehensively understand the impact of muscle mass on the prognosis of patients after cardiac arrest.

Eligibility criteria:
Inclusion Criteria:

* Non-traumatic adult patients who experienced sudden cardiac arrest
* Achieved sustained return of spontaneous circulation (ROSC) for more than five minutes following successful resuscitation
* Admitted to the ICU
* Survived beyond 72 hours without successful weaning from mechanical ventilation

Exclusion Criteria:

* Age under 18 years
* Pregnancy
* Expected to be weaned from mechanical ventilation within 48 hours
* Terminal-stage cancer or unwillingness to receive aggressive treatment
* Unavailability of ICU care due to overcrowding in the emergency intensive care unit
* Refusal to participate in the study by the patient or family members
* Presence of an implanted pacemaker or defibrillator
* Open wounds, severe edema, or deep vein thrombosis in the lower limbs
* Pre-arrest status limited to wheelchair use or bedridden, unable to ambulate

Outcomes:
skeletal muscle mass