moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Evaluating the Usefulness of an Artificial Intelligence (A.I.) Mental Health Chatbot in a Healthcare Setting
Trial ID: NCT04620668
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-10-01
Completion date: 2022-10-31
Sponsor: Luke MacNeill
Collaborators: New Brunswick Innovation Foundation; Touchkin eServices Pvt. Ltd.
Conditions: Arthritis; Diabetes; Depression; Anxiety; Stress
Interventions: Wysa
Locations: Centre for Research in Integrated Care, University of New Brunswick
Countries: Canada
Enrollment: 79
Age range: 18 Years - 65 Years
Sex: ALL

Summary:
Past research has shown that patients suffering from chronic health conditions tend to experience high levels of negative mental health symptoms (e.g., depression). The purpose of the current study is to evaluate whether an artificial intelligence (A.I.) mental health chatbot can be used to reduce negative mental health symptoms within this population. A minimum of 60 individuals with a chronic health condition (diabetes or arthritis) will be recruited. Participants will be randomly assigned to either a treatment group or a control group. Those assigned to the treatment group will use the mental health chatbot Wysa (Touchkin eServices, Bangalore) over a period of four weeks. Those assigned to the control group will receive no chatbot. Participants will complete measures of depression, anxiety, stress, and life satisfaction via Qualtrics at the outset of the study, two weeks into the study, and four weeks into the study (i.e., the final assessment point). Results from the treatment and control groups will be compared using ANOVA models. Participants in the treatment group will also be asked to complete some open-ended questions about their experiences with the chatbot program. A subset of participants from the treatment group may be asked to complete optional phone or video interviews to gain a better understanding of their experiences. Results will provide insight into the usefulness of chatbot programs for reducing negative mental health symptoms among patients with a chronic health condition. Results may also be used to inform policy decisions about the use of these programs for healthcare delivery, and to provide practical insight into how these programs can be best integrated into healthcare settings.

Eligibility criteria:
Inclusion Criteria:

* Participants must have a diagnosis of diabetes (type 1 or type 2 diabetes) or arthritis (osteoarthritis, rheumatoid arthritis, or another type of arthritis).
* Participants must have a phone with an active Internet connection.

Exclusion Criteria:

* Participants must not be receiving ongoing treatment from a mental health professional.
* Participants must not be using a mental health chatbot (i.e., prior to the study).
* Participants must not have started or experienced a dosage change in a psychopharmacological drug within the previous month.

Outcomes:
Changes in Depression as Measured by the Patient Health Questionnaire (PHQ-9); Changes in Anxiety as Measured by the Generalized Anxiety Disorder Scale (GAD-7); Changes in Stress as Measured by the Perceived Stress Scale (PSS-10)
Use of Machine Learning Algorithms for Automated Detection of Fetal Anomalies
Trial ID: NCT04897178
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-06-01
Completion date: 2023-12-01
Sponsor: Assiut University
Collaborators: Middle-East Obstetrics and Gynecology Graduate Education (MOGGE) Foundation
Conditions: Fetal Anomaly
Interventions: Ultrasound
Locations: Aswan Faculty of Medicine; Assiut Faculty of Medicine - Women Health Hospital
Countries: Egypt; Egypt
Enrollment: 1000
Age range: 18 Years - 45 Years
Sex: FEMALE

Summary:
MARS is an artificial intelligence-powered system that aims at detecting common fetal anomalies during real-time obstetrics ultrasound. The current study comprises 2 stages: (1) The stage of model creation which will include retrospective collection of images from fetal anatomy scans with known diagnoses to train these model and test their diagnostic accuracy. (2) The stage of model validation through prospective application of this model to collected videos with known normal and abnormal diagnoses

Eligibility criteria:
Inclusion Criteria:

* Pregnant women between 18 and 45 years
* Available ultrasound image with clear findings
* postnatal confirmation of diagnosis

Exclusion Criteria:

* Absence of research authorization on medical records

Outcomes:
Diagnostic accuracy
Effects of Film-Based Music Therapy With LLM-Generated Culturally Adapted Verbal Suggestions on Anxiety, Affect, and Heart Rate Among Chinese Young Adults: A Randomized Controlled Trial
Trial ID: NCT07359950
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-10-01
Completion date: 2026-01-30
Sponsor: Wen Li
Conditions: Anxiety; Psychological Stress; Emotional Regulation
Interventions: Music Listening With AI-Generated Eastern Imagery Verbal Guidance; Music Listening With Standard Verbal Guidance; Music-Only Relaxation
Locations: Aba Teachers College
Countries: China
Enrollment: 123
Age range: 18 Years - 25 Years
Sex: ALL

Summary:
This study aims to examine whether music-based relaxation combined with different types of verbal guidance can help reduce anxiety and improve emotional well-being in young adults.

University students often experience high levels of stress related to academic demands and daily life. Music listening is commonly used as a simple and safe method to promote relaxation. In addition to music itself, verbal guidance during music listening may influence how individuals imagine, interpret, and emotionally respond to the music experience.

In this study, participants are randomly assigned to one of three groups. One group listens to music accompanied by verbal guidance generated by a large language model and designed to reflect Eastern aesthetic imagery. A second group listens to music with standard relaxation guidance commonly used in music therapy. A third group listens to relaxing music without any verbal guidance. Each participant takes part in a single music listening session lasting approximately 25-30 minutes.

Levels of anxiety, positive and negative emotions, and heart rate are measured before and after the music session. By comparing the results across the three groups, this study seeks to better understand whether culturally adapted verbal guidance can enhance the effects of music-based relaxation for young adults.

Eligibility criteria:
Inclusion Criteria:

* Age between 18 and 25 years at the time of enrollment.
* Currently enrolled as a university student.
* Able to understand the study procedures and provide written informed consent.
* Willing to participate in a single music listening session lasting approximately 25-30 minutes.
* Able to complete self-report questionnaires before and after the intervention.

Exclusion Criteria:

* Self-reported history of diagnosed psychiatric disorders (e.g., major depressive disorder, anxiety disorders, psychotic disorders).
* Current use of psychotropic medications that may significantly affect mood or anxiety.
* Self-reported hearing impairment that could interfere with music listening.
* Self-reported cardiovascular conditions or other medical conditions that could affect heart rate measurements.
* Participation in other psychological or behavioral intervention studies within the past 30 days.

Outcomes:
Change in State Anxiety Score
Research on the Diagnostic Value of Machine Learning Model Based on Clinical Data in Patients With Coronary Heart Disease
Trial ID: NCT05018715
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-08-22
Completion date: 2023-12-31
Sponsor: Xiang Ma
Collaborators: Shihezi University
Conditions: Coronary Heart Disease; Acute Myocardial Infarction; Angina
Interventions: Machine learning model diagnosis
Locations: The first affiliated Hospital of Xinjiang Medical University
Countries: China
Enrollment: 600
Age range: 18 Years - 100 Years
Sex: ALL

Summary:
Based on the clinical data of patients, a machine learning model for coronary heart disease diagnosis was established to evaluate whether the model could improve the accuracy of coronary heart disease diagnosis, and to evaluate its authenticity, reliability and benefits.

Eligibility criteria:
Inclusion Criteria:

* Patients who meet the diagnostic criteria for CHD set by the World Health Organization

Exclusion Criteria:

* Exclude serious valvular disease, congenital heart disease, respiratory system and other diseases.

Outcomes:
make a definite diagnosis of CHD
Wearable Device for Characterizing AMI Patients After Primary PCI and Predicting the Clinical Outcomes
Trial ID: NCT06995495
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-04-01
Completion date: 2025-10-01
Sponsor: RenJi Hospital
Conditions: Acute Myocardial Infarction
Locations: Renji Hospital
Countries: China
Enrollment: 400
Age range: 18 Years - 75 Years
Sex: ALL

Summary:
The study will explore the use of wearable technology to consistently monitor patients with acute myocardial infarction (AMI) receiving primary percutaneous coronary intervention (PCI). Parameters such as heart rate, sleep time, physical activity, and blood pressure will be collected and analyzed to characterize the patients and predict clinical outcomes.

Eligibility criteria:
Inclusion Criteria:

* Aged 18-75 years old
* Diagnosed with STEMI or NSTEMI (ACC/AHA guidelines)
* Half male and half female
* Undergo CAG and PCI treatment;
* Written informed consent.

Exclusion Criteria:

* People under the age of 18 years old;
* Patients who are pregnant/breastfeeding;
* Patients who are allergic to the metallic or plastic components of the wearable devices

Outcomes:
Heart Rate Pattern assessed artificial intelligence (AI)-assisted analysis
Artificial Intelligence on Molecular Imaging to Predict the Risks of Parkinson's Disease for Patients With Rapid Eye Movement Sleep Behavior Disorder
Trial ID: NCT06629207
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-10-07
Completion date: 2026-08-01
Sponsor: Insel Gruppe AG, University Hospital Bern
Conditions: Parkinson Disease; REM Sleep Behavior Disorder; Dementia, Lewy Body
Interventions: PET/CT with 18-FDG; SPECT : 123 I-FP-CIT (DATSCAN); MRI
Locations: Inselspital, University Clinic for Nuclear Medicine
Countries: Switzerland
Enrollment: 20
Age range: 18 Years - None
Sex: ALL

Summary:
The study aims to systematically document the course of REM sleep behavior disorder (RBD) and investigate possible clinical and imaging biomarkers for disease progression and conversion risk to Parkinson's disease (PD), dementia with Lewy bodies (DLB), and multiple system atrophy (MSA). The study will use artificial intelligence to analyze imaging and develop a reliable method to predict and stratify patients approaching conversion to overt a-synucleinopathy. Participants will be clinically evaluated and 2 imaging procedures will be done.

Eligibility criteria:
Inclusion Criteria:

1. Confirmed clinical iRBD diagnosis by movement disorder specialists according to the International Classification of Sleep Disorders
2. Written informed consent

Exclusion Criteria:

1. Known diagnosis of PD or other neurodegenerative disorder
2. Unequivocal signs of parkinsonism on examination
3. Narcolepsy or other known causes of RBD
4. Moderate to severe obstructive sleep apnea
5. Abnormal neurological or MRI examination

Outcomes:
Assessment of Deep Learning Model Accuracy in Predicting Neurodegenerative Conversion in isolated REM sleep behavior disorder (iRBD) through Early Biomarker Detection
Effect and Safety of Smart Bra of Peking Union Medical College & Hospital
Trial ID: NCT04761211
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2020-08-01
Completion date: 2021-10-01
Sponsor: Peking Union Medical College Hospital
Conditions: Effects of the Elements
Interventions: smart bra
Locations: Breast Surgery Department of PUMCH
Countries: China
Enrollment: 2141
Age range: 18 Years - 80 Years
Sex: FEMALE

Summary:
This is a multicenter, prospective clinical study was conducted to evaluate the safety and effectiveness of Xiehe smart bra for breast disease screening in outpatients with breast surgery. According to the patients' wishes and written informed consent, they were randomly assigned to the study group. A total of 2000 patients were expected to be enrolled. The training group: validation group = 1:1.

Eligibility criteria:
Inclusion Criteria:

1. Patients who are going to receive breast ultrasound and molybdenum target in breast surgery clinic;
2. Female patients (18-80 years old);
3. Signed written informed consent approved by the relevant institutional review board (IRB) or independent ethics committee (IEC)

Exclusion Criteria:

1. The subjects were pregnant or lactating;
2. Patients with nipple discharge;
3. Known allergy to bra materials;
4. The patients who had received breast surgery or breast puncture within half a year;
5. Patients with skin diseases.

Outcomes:
Sensitivity
Automatic Evaluation of the Severity of Gastric Intestinal Metaplasia With Pathology Artificial Intelligence Diagnosis System: a Diagnostic Test
Trial ID: NCT05447221
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-08-01
Completion date: 2023-12-31
Sponsor: Shandong University
Conditions: Gastric Intestinal Metaplasia; Artificial Intelligence; Pathology; Gastric Cancer
Interventions: The diagnosis of Artificial Intelligence and pathologists
Locations: Department of Gastroenterology, Qilu Hospital, Shandong University
Countries: China
Enrollment: 150
Age range: 40 Years - 75 Years
Sex: ALL

Summary:
The OLGIM staging system is highly recommended for a comprehensive assessment of GIM severity to evaluate patients' gastric cancer risk. However, its need to take at least 4 biopsies is not clinically feasible due to a serious shortage of pathologists compared with the large number of gastric cancer screening population.

We plan to develop a Digital Pathology artificial intelligence diagnosis system (DPAIDS), to automatically identify tumor areas in whole slide images(WSI) and quickly and accurately quantify the severity of intestinal metaplasia according to the proportion of intestinal metaplasia areas.

Eligibility criteria:
Inclusion Criteria:

* patients aged 40-75 years who undergo the gastroscopy examination and biopsy

Exclusion Criteria:

* patients with severe cardiac, cerebral, pulmonary or renal dysfunction or psychiatric disorders who cannot participate in gastroscopy
* patients with previous surgical procedures on the stomach
* patients with contraindications to biopsy
* patients who refuse to sign the informed consent form

Outcomes:
The diagnostic performance of AI model to assess the severity of intestinal metaplasia
A Pilot Study of an Artificial Intelligence System as a Diagnostic Aid to Improve Skin Cancer Management (04.17 SMARTI)
Trial ID: NCT04040114
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2019-10-01
Completion date: 2021-05-30
Sponsor: Melanoma and Skin Cancer Trials Limited
Collaborators: Monash University
Conditions: Skin Cancer; Melanoma (Skin)
Interventions: Molemap Skin Cancer Triage Artificial Intelligence Device
Locations: The Alfred- Victorian Melanoma Service; Skin Health Institute
Countries: Australia; Australia
Enrollment: 200
Age range: 18 Years - None
Sex: ALL

Summary:
The study is designed to be able to prove if the Molemap Artificial Intelligence (AI) algorithm can be used as a diagnostic aid in a clinical setting. This study will determine whether the diagnostic accuracy of the Molemap AI algorithm is comparable to a specialist dermatologist, teledermatologist and registrar (as a surrogate for a general practitioner). The study patient population will be adult patients who require skin cancer assessment.

The use of AI as a diagnostic aid may assist primary care physicians who have variable skill in skin cancer diagnosis and lead to more appropriate referrals (rapid referral for lesions requiring treatment and fewer referrals for benign lesions), thereby improving access and reducing waiting times for specialist care.

Eligibility criteria:
Inclusion Criteria:

1. Patients attending the specialist dermatology clinics for skin cancer assessment or surveillance.
2. Patients may or may not have a lesion of concern.
3. Patients must have at least two lesions imaged during full skin examination by a dermatologist.
4. Age greater than 18 years.
5. Participant is willing and able to undertake investigation of suspicious lesion (e.g. skin biopsy).

Exclusion Criteria:

1. Patient does not give informed consent.
2. Patient is unable or unwilling to have a full skin examination
3. Patient has a known past or current diagnosis of cognitive impairment

Outcomes:
Diagnostic accuracy of the device when compared prospectively to a teledermatologist assesment
Machine Learning Models for Predicting Long-Term Clinical Outcomes in Chinese Female Breast Cancer Patients Receiving Neoadjuvant Chemotherapy
Trial ID: NCT06856616
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-05-13
Completion date: 2026-06-01
Sponsor: The Third Affiliated Hospital of Harbin Medical University
Conditions: Breast Neoplasms
Locations: Ming Niu
Countries: China
Enrollment: 6000
Age range: 18 Years - 80 Years
Sex: FEMALE

Summary:
At present, the majority of studies on neoadjuvant chemotherapy (NAC) in patients with breast cancer (BC) use pathological complete response (pCR) as a surrogate marker for patient prognosis, with significant improvements in pCR indicating better long-term survival. However, there is still a lack of non-invasive tools for accurately predicting the prognosis and pCR of BC patients undergoing NAC. Recent research has introduced emerging artificial intelligence machine learning (ML) and deep learning (DL) algorithms such as Bayesian methods, K-nearest neighbors (KNN), decision trees, support vector machines (SVM), XGBoost, ResNet, convolutional neural networks, and Transformer models, which have brought new avenues of exploration for cancer researchers.

The integration of AI with imaging, pathology, genomics, and other multi-omics has non-invasively improved preoperative diagnosis of breast cancer and, when combined with clinical factors, can assess postoperative survival. Moreover, current research data is limited, and reliable predictive models require extensive data for training. Therefore, establishing a multi-center database is essential.

Eligibility criteria:
Inclusion Criteria:

1. Women with invasive breast cancer who received neoadjuvant chemotherapy (NAC) treatment in various hospitals from 2008 to 2019 (follow-up endpoint December 31, 2024)
2. Women with primary breast cancer (Stage II-III) confirmed by pre-NAC needle biopsy, along with recorded clinical, pathological, and prognostic information
3. With MR images before the first cycle of NAC and before surgery
4. With pathological HE staining images, including biopsy pathology and postoperative major pathology

Exclusion Criteria:

1. Any form of treatment was received before NAC, including endocrine therapy, radiotherapy, and chemotherapy
2. Disease metastasis occurred during NAC
3. Breast cancer patients with secondary malignancies from other cancers
4. Patients did not complete surgery and were lost to follow-up

Outcomes:
5-10 year survival rate
Precision Phenotyping of Behavioral Risk and Response to Electromagnetic and Psychedelic Therapies
Trial ID: NCT07484906
Status: WITHDRAWN
Study type: INTERVENTIONAL
Phase: PHASE2
Start date: 2026-05-01
Completion date: 2029-08-31
Sponsor: University of New Mexico
Collaborators: The Mind Research Network; Georgia State University; New Mexico VA Healthcare System; Washington University School of Medicine
Conditions: Suicidal Ideation
Interventions: fMRI Neurofeedback; Accelerated theta burst stimulation; Psilocybin assisted therapy
Enrollment: 0
Age range: 18 Years - 69 Years
Sex: ALL

Summary:
PRE-EMPT will assemble a study group of 150 civilian and Veteran participants from three populations (low risk, intermediate risk, and high risk for self-harm). The investigators will obtain clinical assessments, MRI, and blood levels for circular RNA (circRNA). The teams will then administer three interventions (neurofeedback, transcranial magnetic stimulation, and psilocybin assisted therapy), and repeat the tests above. A team with expertise in artificial intelligence will then use our data to try to find patterns that identify who is at high risk versus low risk with a high degree of accuracy.

Eligibility criteria:
Inclusion Criteria:

1. 18-69 years old
2. Have been on a stable psychiatric medication regimen for at least four weeks prior to study participation.

Exclusion Criteria:

1. A prior history of other central nervous system disease or any history of seizures;
2. history of psychotic disorders (e.g., schizophrenia, schizoaffective disorder, bipolar disorder type I);
3. history of current or recent (within two years) substance/alcohol use disorder, with the exception of tobacco use disorder;
4. meet criteria for Very High risk of suicide, or require inpatient hospital-level care for psychiatric reasons at time of consent, to reduce exacerbation of risk of harm to self during study;
5. presence of any implanted metal or electrical device (e.g. pacemaker);
6. recent medical hospitalization (within three weeks);
7. any condition that would prevent the participant from completing the protocol, such as significant agitation;
8. appointment of a legal representative or treatment guardian;
9. any ongoing litigation related to a health condition;
10. any other contraindication to exposure to strong magnetic fields or MRI, such as severe claustrophobia;
11. pregnancy or lactation;
12. a family history of schizophrenia or schizoaffective disorder (first or second degree relatives), or bipolar disorder type 1 (first degree relatives), to reduce risk of exacerbation of an undiagnosed psychotic condition;
13. other medical conditions that would preclude safe participation in the trial (e.g., decompensated heart failure);
14. starting or planning to start psychotherapy or changing the frequency or intensity of existing psychotherapy during the trial (current psychotherapy can be continued provided the frequency and intensity has been stable for ≥2 months prior to screening);
15. membership in a vulnerable population (minors, prisoners);
16. any contraindication for blood draws.

Outcomes:
Scale of Suicidal Ideation