moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Evaluation of a Vaccine Chatbot on HPV Vaccine Confidence and Hesitancy: a Randomized Controlled Trial and Implementation Science Study
Trial ID: NCT07200570
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-09-26
Completion date: 2026-05
Sponsor: Fudan University
Collaborators: Merck Sharp & Dohme LLC
Conditions: HPV Vaccine
Interventions: LLM-Powered HPV Vaccine Chatbot
Enrollment: 1800
Age range: 15 Years - 26 Years
Sex: FEMALE

Summary:
The goal of this clinical trial is to learn if a chatbot powered by artificial intelligence works to improve HPV vaccination among females aged 15 to 26 in China. A randomized controlled trial and implementation science study will be conducted targeting females as participants. The main questions it aims to answer are:

1. Does the vaccine chatbot influence women's confidence, literacy, hesitancy, and uptake of the HPV vaccine.
2. What are the public acceptance of chatbot and the facilitators and barriers to its implementation in a real-world setting.

Researchers will compare a group of women who use the chatbot with a group who do not use it to see if the chatbot is effective at helping women feel confident and willing to get vaccinated against HPV.

Participants will:

1. Be recruited and randomly allocated into one of two groups. One group will be invited to use the HPV vaccine chatbot and the other group will not get access to the vaccine chatbot until the end of the trial.
2. Complete a questionnaire survey on their confidence, literacy, and hesitancy on the HPV vaccine.
3. Have their vaccination status checked at the end of trial.

Eligibility criteria:
Inclusion Criteria:

* Female, aged 15 to 26 years, inclusive.
* Not previously vaccinated against HPV.
* Reports no contraindications to HPV vaccination.
* Has no mental, visual, or reading impairments that would preclude cooperation with study activities.
* Is willing and able to provide informed consent.

Exclusion Criteria:

* Male.
* Age under 15 or over 26 years.
* Has a history of prior HPV vaccination.
* Has a known contraindication to HPV vaccination.
* Unable to comply with study procedures.
* Is unwilling or unable to provide informed consent.

Outcomes:
HPV Vaccine Uptake
The Effect of Wound Care Education Given to Patients Undergoing Prosthetic Surgery on Wound Care Knowledge, Self-Care Self-Efficacy, and Quality of Recovery
Trial ID: NCT07264712
Status: ACTIVE_NOT_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-10-10
Completion date: 2026-04-01
Sponsor: TC Erciyes University
Conditions: Prosthetic Treatment; Wound Care; Education
Interventions: artificial intelligence-supported education
Locations: Erciyes University; Health training and research hospital
Countries: Turkey (Türkiye); Turkey (Türkiye)
Enrollment: 50
Age range: 18 Years - None
Sex: ALL

Summary:
This study will be conducted as a randomized controlled trial to determine the impact of artificial intelligence-assisted wound care training provided to patients undergoing prosthetic surgery on their wound care knowledge, quality of healing, and self-care self-efficacy. It was planned to include 22 individuals in the experimental group and 22 in the control group. However, considering the potential for data loss, the study was planned to be completed with a total of 50 individuals: 25 in the experimental group and 25 in the control group. Patient Identification Form, Patient Follow-up Form, Knowledge Level Questionnaire, Self-Care Self-Efficacy Scale and Quality of Recovery (QoR-15) Scale were used to collect data.

Eligibility criteria:
Inclusion Criteria:

* Those undergoing total hip or knee replacement surgery
* Those who volunteer to participate in the study
* Those over 18 years of age
* Those who have no communication problems (language, hearing impairment, etc.)

Exclusion Criteria:

* Patients who have developed a surgical site infection
* Illiterate
* Unable to self-care
* Previously had prosthetic surgery will not be included.

Outcomes:
Knowledge Level; Self-Care Self-Efficacy; Quality of Recovery
A Pilot Study: Exploring The Role Of an Artificial Intelligence Powered Mental Health Support Tool For Mental Health Wellness For Physicians In Training
Trial ID: NCT07087119
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-07-21
Completion date: 2026-12
Sponsor: Cedars-Sinai Medical Center
Conditions: Wellness, Psychological; Mental Health Wellness 1
Interventions: utilization of artificial intelligence powered mental health support session through a web browser
Locations: Cedars Sinai Medical Center
Countries: United States
Enrollment: 20
Age range: 18 Years - None
Sex: ALL

Summary:
The purpose of this pilot study is to evaluate the acceptability of an artificial intelligence powered mental health support tool for mental health wellness amongst physicians in training.

The primary research procedures are:

The primary research procedure will be the utilization of artificial intelligence powered mental health support session through a web browser for physicians in training to address symptoms related to depression and anxiety. A pre and post intervention survey, evaluating depression symptoms with the standardized PHQ-9 questionnaire and anxiety symptoms with the standardized GAD-7 questionnaire will be conducted to assess changes in mental health wellness.

The study will enroll physicians in training, including Internal Medicine residents and Gastroenterology fellows.

The study includes at least 4 sessions. The total study duration for each subject is 3 months.

Eligibility criteria:
Inclusion Criteria:

* Individuals 18 years old or older are included.
* Training physicians in Internal Medicine residency and Gastroenterology fellowship at Cedars Sinai.
* Study is limited to Cedars-Sinai employees who are known to speak English.

Exclusion Criteria:

* Active treatment of severe and uncontrolled anxiety or depression with a health care professional
* Unwilling and or unable to participate
* Non-English speakers.

Outcomes:
Feasibility of using Artificial Intelligence sessions as a wellness resource
Effects of VR-Based Mindfulness Observational Meditation for Stress Reduction in Adults : Randomized Controlled Trial
Trial ID: NCT06704282
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-11-25
Completion date: 2025-02-23
Sponsor: Penn State University
Conditions: Stress; Anxiety; Depression
Interventions: VR Mindfulness Meditation Program
Locations: Penn State Univercity
Countries: United States
Enrollment: 34
Age range: 18 Years - None
Sex: ALL

Summary:
This study investigates the impact of a mindfulness-based intervention using virtual reality (VR) and artificial intelligence (AI) coaching on stress reduction. Participants will engage in VR-based observation mindfulness activities guided by an AI coach. The study aims to evaluate the effectiveness of this intervention compared to a standard relaxation process by measuring self-reported stress levels, physiological biomarkers (e.g., heart rate), and participant satisfaction. The research could inform the development of future digital mental health interventions.

Eligibility criteria:
Inclusion Criteria:

* Adults aged 18 years and older.

Exclusion Criteria:

* Those with visual or hearing impairments that prevent interaction with VR content.

Outcomes:
Reduction in Perceived Stress Levels; Changes in Emotional State; Participant Satisfaction with Intervention; Physiological Stress Response; Changes in Emotional State Using STAI
Developing, Administering, and Evaluating Online Psychotherapy and Psychoeducation Programs for Postsecondary Students Within a Regional Mental Health Hub in Ontario, Canada Using AI-assisted Online Interventions
Trial ID: NCT05754723
Status: WITHDRAWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-05-05
Completion date: 2023-04-06
Sponsor: Dr. Nazanin Alavi
Conditions: Mental Health Issue
Interventions: e-Psychotherapy/Psychoeducation
Locations: Queen's University, Hotel Dieu Hospital
Countries: Canada
Enrollment: 0
Age range: 18 Years - None
Sex: ALL

Summary:
The prevalence of mental health challenges and disorders in post-secondary students, demands accessible and efficacious care. Online psychotherapy and psychoeducation programs have shown significant effectiveness in mitigating the risk and clinical symptoms of various mental health disorders. Additionally, artificial intelligence (AI) has become an accessible, effective, and scalable tool supporting the delivery of healthcare. Therefore, the primary objective of this study is to develop an AI-driven online mental health care hub in the Kingston, Frontenac, Lennox \& Addington region for post-secondary students. . This hub will deliver equitable, efficacious, and cost-effective online psychoeducation and psychotherapy in the form of online diagnosis-specific cognitive behavioural therapy (e-CBT). The hub's virtual design aims to adequately address existing gaps in the mental healthcare of these individuals and alleviate the burden placed on mental health services in Canada. Using a rigorous implementation framework, the development of this hub is designed as a multiphase study with three phases. (1) Pre-adoption phase: will assess post-secondary students' current mental health landscape through surveys and focus groups. This information will be used in the development of our online psychoeducation and diagnosis-specific e-CBT programs. (2) Delivery phase: will determine the efficacy and cost-effectiveness of the online psychoeducation and diagnosis-specific e-CBT programs by comparing them to treatment as usual. (3) Post-adoption phase: the collected data from these programs will be analyzed and shared with key stakeholders to guide continuous program scaling and improvement.

Eligibility criteria:
Inclusion Criteria:

* post-secondary students enrolled in institutions of interest
* the ability to provide informed consent
* the ability to speak and read English
* consistent and reliable access to the internet

Exclusion Criteria:

\- N/A

Outcomes:
Change in anxiety severity (GAD-7); Change in depression severity (PHQ-9); Change in depression severity (QIDS); Change in stress severity (DASS-42); Change in quality of life (AQoL-8D); Change in resilience (RS-14); Change in social anxiety severity (LSAS); Change in ADHD severity (ASRS)
Image Features of LSCI and Thermography for Determining the Risk Factor (0,1,2 and 3) of Developing Diabetic Foot Ulcer
Trial ID: NCT05845242
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2023-08-01
Completion date: 2028-10-01
Sponsor: Ziekenhuisgroep Twente
Collaborators: University of Twente
Conditions: Diabetic Foot
Enrollment: 80
Age range: 18 Years - None
Sex: ALL

Summary:
Diabetic foot ulcers are one of the complications of diabetes mellitus. These wounds are often the result of diabetes-related neuropathy and/or an ischemic foot. Even with great care, recurrent ulcers are common. To mediate the damage and societal costs that come with DF there is a need for applications to detect ulcers before they come apparent. Two of these promising techniques are Laser Speckle Contrast imaging and thermography. This study is part of 4 specific clinical studies and is aimed at determining Laser Speckle Contrast imaging and thermography features, the provocations that are needed to optimize imaging and the correlation between these features and the IWGDF risk stratification category system for the diabetic foot.

Eligibility criteria:
Inclusion Criteria:

* The sample will include all the people who sign the informed consent.
* Patients above 18. Patients diagnosed with Diabetic feet.
* Patients with Risk Level 0, 1, 2 and 3 according to the International Working Group on the Diabetic Foot - IWGDF).

Exclusion Criteria:

* People who do not give their consent to participate in the study.
* Patients with active wounds on one or both feet.

Outcomes:
Perfusion units at various regions of interest using LSCI; Temperature at various regions of interest using thermography; The effect of provocations performed prior to measuring with LSCI; The effect of provocations performed prior to measuring with thermography; Correlation of the above features with the chance of developing ulcers (Risk 0, Risk 1, Risk 2 and Risk 3).
Feasibility and Effectiveness of Diabetic Retinopathy Screening Using Artificial Intelligence in Children With Type 1 Diabetes
Trial ID: NCT07149142
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-01-01
Completion date: 2029-12-31
Sponsor: University Hospital, Motol
Conditions: Type 1 Diabetes Mellitus
Interventions: Non-mydriatic fundus camera photography; Fundus ophtalmoscopy; Optic coherence tomography; Confocal microscopy of the retina
Locations: University Hospital Motol
Countries: Czechia
Enrollment: 400
Age range: 11 Years - 19 Years
Sex: ALL

Summary:
The proposed project is a clinical intervention trial testing the feasibility and effectiveness of diabetic retinopathy screening evaluated by artificial intelligence (AI) based software in children with type 1 diabetes (CwD). Another novel method, the confocal microscopy of the retina will be used to assess the early stages of diabetic neuropathy. In parallel, we aim to assess the prevalence of diabetic retinopathy and neuropathy in a well-controlled population of CwD at a tertiary diabetes care center.

Each participant will undergo an examination of diabetic retinopathy using the non-mydriatic fundus camera. The resulting photography will be evaluated by AI driven software. The participant will then follow this examinaton with fundus ophtalmoscopy in arteficial mydriasis as a standard method of diabetic retinopathy assessment. Another method, the optic coherence tomography (OCT), which is considered as the most sensitive method for diabetic retinopathy assessment, will be performed after that. The results of these methods will be compared to assess the sensitivity of each. The examination-satisfaction questionnaire will be given to the participants.

In subjects over 18 years, a confocal microscopy of the retina examination will be performed to assess the status of the corneal sub-basal nerve plexus and the presence of diabetic neuropathy will be noted.

The prevalence of diabetic retinopathy and neuropathy in this group of children with diabetes will be assessed based on the results.

Eligibility criteria:
Inclusion Criteria:

* Type 1 diabetes diagnosed according to the ADA criteria
* Age over 11.0 years or duration of diabetes over 2 years
* Signed written informed consent by both the CwD and their parent/caregiver

Exclusion Criteria:

* Unwillingness to sign a written informed consent by both the CwD and their parent/caregiver

Outcomes:
Feasibility of AI for retinopathy screening
ACCESS: AI-Driven Consent Simplification Study
Trial ID: NCT07303517
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-01-08
Completion date: 2026-12-01
Sponsor: University of California, Los Angeles
Conditions: Informed Consent; Comprehension; Communication; Disclosure
Interventions: Original ICF; AI Simplified ICF; Podcast - Original ICF; Podcast - Simplified ICF
Locations: UCLA
Countries: United States
Enrollment: 230
Age range: 18 Years - None
Sex: ALL

Summary:
The overarching goal of this pilot is to explore how generative artificial intelligence (genAI) can be used to improve the accessibility and understandability of informed consent materials in clinical research. The study will test the extent to which informed consent text can be improved by large language models (LLM; specifically, ChatGPT and NotebookLM) along with other AI tools (specifically, ElevenLabs) through qualitative and quantitative analyses.

Simplifying such forms using genAI may facilitate better comprehension, ensuring truly informed consent. Improving informed consent form (ICF) comprehension can lead to more informed and willing participation in clinical studies. This improved understanding may result in higher enrollment rates, better subject retention, and more accurate data collection as individuals will have a clearer understanding of study procedures and risks.

Eligibility criteria:
Inclusion Criteria:

Group 1:

* Age 60 years and above
* UCLA patient
* English speaking
* Ability to provide informed consent

Group 2:

* UCLA investigators who are health providers that perform clinical trials
* Must be clinical research investigatory and staff actively involved in research operations at UCLA
* English speaking

Group 3:

* Individuals involved in IRB activities (chairs, committee members, staff)
* Must be a director or staff at UCLA
* English speaking

Exclusion Criteria:

* Group 1: None
* Group 2: PI and co-PIs of the study "Donor Chimerism and Graft Survival Following Combined HLA-Identical Sibling Living Donor Kidney and Hematopoietic Stem Cell Transplantation Utilizing a Conditioning Regimen of Total Lymphoid Irradiation (TLI) and Rabbit Anti-Thymocyte Globulin (rATG)"
* Group 3: Individuals from the IRB who have reviewed the study submission for "Donor Chimerism and Graft Survival Following Combined HLA-Identical Sibling Living Donor Kidney and Hematopoietic Stem Cell Transplantation Utilizing a Conditioning Regimen of Total Lymphoid Irradiation (TLI) and Rabbit Anti-Thymocyte Globulin (rATG)"

Outcomes:
Quality of Informed Consent (QUIC)
Deep Learning for Histopathological Classification and Prognostication of Gynaecologic Smooth Muscle Tumours
Trial ID: NCT06540846
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2023-12-01
Completion date: 2026-12
Sponsor: Institut Bergonié
Conditions: Stump
Interventions: No intervention
Locations: Institut Bergonie
Countries: France
Enrollment: 392
Age range: None - None
Sex: FEMALE

Summary:
Smooth muscle tumors of the uterus that do not fit the diagnostic criteria of benignity (such as leiomyomas) or malignancy (such as leiomyosarcomas) are called STUMP (smooth muscle tumor of uncertain malignant potential). A potential solution to this problem could be the application of predictive models using artificial intelligence (AI) to aid in the histopathological classification and prognosis of gynecological smooth muscle tumors. Deep learning using convolutional neural networks represents a specific class of machine learning, in which predictive models are trained by considering small groups of pixels in digital images and iteratively identifying salient features. In this study, we aim to develop deep learning models capable of accurately subclassifying and predicting the prognosis of gynecological smooth muscle tumors, based on histopathological features of hematoxylin and eosin (H\&E) slides. The aim is to develop a diagnostic and prognostic algorithm to help pathologists better classify and diagnose uterine smooth muscle tumors and predict their clinical course.

Eligibility criteria:
Inclusion Criteria:

* Patients with a diagnosis of uterine smooth muscle tumors (leiomyomas, smooth muscle tumors of uncertain malignancy and leiomyosarcomas), registered in the RRePS database and/or treated at Institut Bergonié or one of the participating centers.
* Histopathological material available (kerosene blocks and/or slides).
* The follow-up (outcome) is required for each LMS/ STUMP.

Exclusion Criteria:

* na

Outcomes:
Develop deep learning models that can accurately subclassify gynaecologic smooth muscle tumours
Prediction of Sepsis in Patients Undergoing Abdominal Surgery: A Prospective, Observational Clinical Study
Trial ID: NCT06238180
Status: WITHDRAWN
Study type: OBSERVATIONAL
Start date: 2023-11-29
Completion date: 2024-06-30
Sponsor: Aisthesis Medical P.C.
Collaborators: Larissa University Hospital; Technical University of Crete
Conditions: Sepsis; Abdominal Sepsis; Infections; Clinical Deterioration; Hemodynamic Instability
Interventions: PRISM Tool
Locations: General University Hospital of Larissa
Countries: Greece
Enrollment: 0
Age range: 18 Years - 120 Years
Sex: ALL

Summary:
The goal of this prospective observational study is to develop and utilize an Artificial Intelligence (AI) model for the prediction of postoperative sepsis in patients undergoing abdominal surgery. The main questions it aims to answer are:

1. Can a remote AI-driven monitoring system accurately predict sepsis risk in postoperative patients?
2. How effectively can this system integrate and analyze multimodal data for early sepsis detection in the surgical ward?

Participants are equipped with non-invasive PPG-based wearable devices to continuously monitor vital signs and collect high-quality clinical data. This data, along with demographic and laboratory information from the Electronic Health Record (EHR) of the hospital, are used for AI model development and validation.

Eligibility criteria:
Inclusion Criteria:

* Patients undergoing elective abdominal surgery.
* Postoperative admission to the surgical ward.
* Age 18 years or older, who are able and willing to participate and have given written consent.
* On admission, the primary investigator assess their risk to deteriorate during the first 72 hours after admission as reasonably high.

Exclusion Criteria:

* \<18 years of age Known allergy or contraindication to the monitoring devices.
* Pre-existing conditions that could interfere with the study (e.g., chronic sepsis, immunodeficiency disorders).
* Day case surgery.
* Pregnancy.
* Immediate transfer to ICU postoperatively.
* Patient refusal or unable to give written consent.

Outcomes:
Accuracy of AI-Driven Sepsis Prediction in Postoperative Period
Using Reinforcement Learning to Personalize Electronic Health Record Tools to Facilitate Deprescribing
Trial ID: NCT06660979
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-08-11
Completion date: 2026-03-27
Sponsor: Brigham and Women's Hospital
Collaborators: National Institute on Aging (NIA); Atrius Health
Conditions: Aging
Interventions: Reinforcement learning
Locations: Atrius Health
Countries: United States
Enrollment: 1249
Age range: 18 Years - None
Sex: ALL

Summary:
The overall goal of the proposed research is to refine and adapt and perform efficacy testing of a novel reinforcement learning-based approach to personalizing EHR-based tools for PCPs on deprescribing of high-risk medications for older adults. The trial will be conducted at Atrius Health, an integrated delivery network in Massachusetts, and will intervene upon primary care providers. The investigators will conduct a cluster randomized trial using reinforcement learning to adapt electronic health record (EHR) tools for deprescribing high-risk medications versus usual care. 70 PCPs will be randomized (i.e., 35 each to the reinforcement learning intervention and usual care \[no EHR tool\] in each arm) to the trial and follow them for approximately 30 weeks. The primary outcome will be discontinuation or ordering a dose taper for the high-risk medications for eligible patients by included primary care providers, using EHR data at Atrius. The primary hypothesis is that the personalized intervention using reinforcement learning will improve deprescribing compared with usual care.

Eligibility criteria:
Inclusion Criteria:

The trial will intervene upon primary care providers (including physicians and PCP-designated nurse practitioners and physician assistants) at Atrius Health.

Patients of the PCPs will be included in the intervention and analysis if they are \>/=65 years of age and have been prescribed \>/= 90 pills of high-risk medications in the prior 180 days based on EHR data.

Exclusion Criteria:

• Not a primary care provider at Atrius Health

Outcomes:
Discontinuation or taper for high-risk medication