moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Digital Transformation in Infertility Treatment: Effectiveness and Future Potential of a Comprehensive Mobile Web Application (Infertilitech)
Trial ID: NCT06960551
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-06-20
Completion date: 2025-09-20
Sponsor: Inonu University
Conditions: Infertility; Artificial Intelligence (AI)
Interventions: ınfertility group; control group
Enrollment: 148
Age range: 18 Years - 45 Years
Sex: ALL

Summary:
The primary objective of this project is to increase the treatment compliance and success of infertile couples based on the Knowledge-Motivation-Behavioral Skills model.

To develop a mobile web application and evaluate its effectiveness with scientific methods.

The secondary aim was to evaluate the effect of using a mobile application on Infertility Affect Status, Self-Efficacy, Infertility Adjustment and State-Trait Anxiety.

effect of the impact of the study.

Eligibility criteria:
Inclusion Criteria:

for women;

* To be between the ages of 18-45,
* Being diagnosed with primary infertility,
* To be able to read, write and understand Turkish,
* Not having a disability to use a smartphone,
* Being at the stage of agreeing to start infertility treatment (initial period of treatment),
* Using the mobile app at least 4 times a week for 15 minutes each for 3 months for partner
* Volunteer to participate in the project,
* Not having a disability to use a smartphone,
* Using the mobile app at least 1 time/15 minutes per week for 3 months

Exclusion Criteria:

for women

* Incomplete answers to forms,
* Not using the mobile app at all for a week,
* Wanting to leave the study,
* Voluntary discontinuation of infertility treatment for partner
* Incomplete completion of the forms (uMARS and State-Trait Anxiety Inventory),
* Not using the mobile app at all for two weeks

Outcomes:
Infertility Adjustment Scale
An Investigation Into the Oral-motor and Speech Functions of Post-stroke Individuals Via an AI-powered Mobile Health Application
Trial ID: NCT06635187
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-05-01
Completion date: 2027-03-31
Sponsor: The Hong Kong Polytechnic University
Collaborators: The University of Hong Kong
Conditions: Stroke; Oral Motor Functions
Interventions: Speech and swallowing assessment
Locations: Speech Therapy Unit, Room 701, Block EF, The Hong Kong Polytechnic Unviersity
Countries: Hong Kong
Enrollment: 200
Age range: 20 Years - 85 Years
Sex: ALL

Summary:
The goal of this study is to set up a database of oral-motor and speech functions of post-stroke patients in Hong Kong via an artificial intelligence-powered mobile-health application. The main study objectives are:

1. To establish an image database of oral-facial movements of neurologically healthy adults and adult stroke patients in Hong Kong.
2. To evaluate the oral-motor functions of neurologically healthy adults and stroke patients via instrumental and behavioral assessment.
3. To investigate the correlation between image-based analysis of oral-facial functions and instrumental evaluations and subjective judgment by the speech-language therapist.

Eligibility criteria:
For Neurologically healthy adults,

Inclusion Criteria:

1. age between 20 and 85 years old;
2. no severe systemic diseases such as uncontrolled diabetes, cerebrovascular diseases, head and neck tumors, or neurological disorders;
3. not undergoing radiation therapy or chemotherapy;
4. normal or corrected-to-normal vision and hearing; and
5. fluent Cantonese speakers

Exclusion Criteria:

1\) participants who are unable to sign the "Informed Consent Form" due to cognitive impairments

For stroke patients,

Inclusion Criteria:

1. age between 20 and 85 years old;
2. a history of stroke;
3. not undergoing radiation therapy or chemotherapy;
4. normal or corrected-to-normal vision and hearing;
5. premorbid fluent Cantonese speakers; and
6. able to follow simple commands.

Exclusion Criteria:

1. individuals currently on non-oral feeding; and
2. participants who are unable to sign the "Informed Consent Form" due to cognitive impairments

Outcomes:
Diadochokinetic (DDK) rate of alternating and sequential labial, alveolar and velar plosive sounds; Tongue strength endurance measured by tongue pressure sensor (model: TPM-02, JMS, Hiroshima, Japan); Mastication efficiency as measured via glucose sensor (model: GS-II, GC Corporation, Toyko, Japan); Occlusal force as measured via the occulsal pressure measurement film (model: Dental Prescale II, GC Corporation, Toyko, Japan); Maximum tongue strength measured by tongue pressure sensor (model: TPM-02, JMS, Hiroshima, Japan)
Continuous Glucose Monitoring in Prediabetes With Health Education Videos
Trial ID: NCT06883812
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-03-05
Completion date: 2025-11-03
Sponsor: David S Black, PhD
Collaborators: National Institute on Minority Health and Health Disparities (NIMHD)
Conditions: Prediabetes
Interventions: CGM
Locations: Southern California Center for Latino Health SCCLH
Countries: United States
Enrollment: 30
Age range: 18 Years - None
Sex: ALL

Summary:
The objective of this project is to deliver a behavior health education video series that combines wearable continuous glucose monitoring (CGM) with smartphone feedback and video clips generated by artificial intelligence (AI) software to improve glycemic control among individuals with prediabetes. The goal is to prevent transition to type 2 diabetes and advanced metabolic complications.

Eligibility criteria:
Inclusion Criteria:

* Prediabetes by finger prick blood A1C%
* Centers for Disease Control and Prevention (CDC) prediabetes risk test score of 5 or higher
* Willingness to wear CGM sensor
* Community health worker

Exclusion Criteria:

* Currently pregnant
* Less than 18 years of age, which is adult in California
* Diagnosed with any disorder that interferes with glucose
* Influential medical disorder/event affecting ability to participate in study
* Incompatible smartphone device not pairing with Dexcom G6 app

Outcomes:
Glucose levels
The Role of Chat GPT in the Diagnosis and Treatment of Lymphedema
Trial ID: NCT07485465
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-03-15
Completion date: 2026-04-01
Sponsor: Fatih Sultan Mehmet Training and Research Hospital
Conditions: Lymphedema; Artificial Intelligence (AI)
Interventions: a domain-specific, custom-trained large language model for the differential diagnosis and treatment planning of lymphedema, lipedema, and venous insufficiency
Locations: Istanbul Fatih Sultan Mehmet Training and Research Hospital
Countries: Turkey (Türkiye)
Enrollment: 25
Age range: 18 Years - None
Sex: ALL

Summary:
A domain-specific, custom-trained large language model for the differential diagnosis and treatment planning of lymphedema, lipedema, and venous insufficiency.

Eligibility criteria:
Inclusion Criteria:

* Patients over the age of 18
* Clinical diagnosis of lymphoedema
* Clinical diagnosis of lipoedema
* Clinical diagnosis of venous insufficiency

Exclusion Criteria:

* Lack of medical history
* Lack of demographic data
* Lack of clinical data and
* Lack of imaging methods

Outcomes:
Diagnostic accuracy rate; Treatment adequacy rate; Average criterion score
Bone Enhanced Ultrasound (BEUS) Data Library Development Project
Trial ID: NCT07036653
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-09-01
Completion date: 2025-12-31
Sponsor: Queen's University
Conditions: Lower Back Pain; Facet Joint Pain; Low Back Pain; Osteoarthritis (OA)
Interventions: spinal ultrasound
Enrollment: 100
Age range: 18 Years - None
Sex: ALL

Summary:
A common treatment for low back pain involves fluoroscopy-guided spinal facet joint injections and/or medial branch nerve blocks. Unfortunately, fluoroscopy requires expensive equipment and personnel and exposes patients and healthcare providers to ionizing radiation. Ultrasound offers a safer, lower-cost alternative, but the traditional 2-dimensional (2D) ultrasound systems are limited due to poor image quality, particularly in patients with higher body mass index (BMI).

As an alternative, a novel Bone Enhanced Ultrasound (BEUS) technology uses artificial intelligence (AI) to create real-time 3-dimensional (3D) images of the spine to guide needle placement for these injections. The AI software is trained by overlaying computed tomography (CT) and ultrasound images from a patient dataset to recognize anatomical landmarks. BEUS aims to ultimately replace fluoroscopy for spinal injections, reducing radiation exposure, lowering healthcare costs, and improving accessibility, especially in rural settings where CT and fluoroscopy are unavailable.

A key limitation, however, is that the current AI system is trained based primarily on patients (mostly pediatric) undergoing perioperative assessment of scoliosis. To address this, the current study aims to develop a new, more clinically relevant training AI dataset by collecting spinal ultrasounds from up to 100 adult participants (most/all of whom are followed at the local chronic pain clinic for low back pain) with existing spinal CT or magnetic resonance imaging (MRI) scans. This dataset will be used to retrain the current AI model to enhance the accuracy of 3D spinal reconstructions, thereby improving the clinical relevance of the BEUS system.

Eligibility criteria:
Inclusion Criteria:

* Individuals who have had previous CT or MRI scans of their spine in the past 5 years

Exclusion Criteria:

* \< 18 years old

Outcomes:
Accuracy of 3D ultrasound spine reconstruction in adults with chronic low back pain
A Prospective Multicenter Study on the Effect of Preoperative Automatic Reminder System on Colonoscopy
Trial ID: NCT04996888
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-08-04
Completion date: 2022-01-31
Sponsor: Renmin Hospital of Wuhan University
Conditions: Colonoscopy
Interventions: AI based preoperative automatic reminder system
Locations: Renmin hospital of Wuhan University
Countries: China
Enrollment: 829
Age range: 50 Years - 75 Years
Sex: ALL

Summary:
In this study, the investigators proposed an artificial intelligence-based preoperative automatic reminder system on colonocopy, which can improve the quality of bowel preparation and the rate of polyps and adenomas detection.

Eligibility criteria:
Inclusion Criteria:

1. Male or female between 50-75 years old;
2. Able to read, understand and sign informed consent
3. The investigator believes that the subjects can understand the process of the clinical study, are willing and able to complete all study procedures and follow-up visits, and cooperate with the study procedures
4. Outpatients requiring colonoscopy

Exclusion Criteria:

1. colorectal surgical history
2. Severe colonic stricture or obstructive tumor
3. Obvious gastroparesis or gastric outlet obstruction or intestinal infarction
4. Pregnant or lactating women
5. Dysphagia or impaired swallowing reflex or mental state
6. Intestinal obstruction or perforation is known
7. Severe chronic renal failure（CCR\< 30ml/min）
8. Severe congestive heart failure（NYHA ⅢorⅣ）
9. Uncontrolled hypertension( systolic pressure\>170mmHg，diastolic pressure\>100mmHg)
10. Dehydration or electrolyte disorder
11. Toxic colitis or megacolon
12. Inable to give informed consent
13. Hemodynamic instability

Outcomes:
the rate of adequate bowel preparation
Mirror Speech Entrainment: A Novel Technique for Voice Personalized Speech Entrainment for Nonfluent Aphasia
Trial ID: NCT06829420
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-05-01
Completion date: 2027-08-01
Sponsor: University of South Florida
Conditions: Aphasia Non Fluent; Stroke; Aphasia
Interventions: Mirror speech entrainment; Traditional speech entrainment: auditory-only; Traditional speech entrainment: auditory-visual
Locations: University of South Florida; University of South Florida
Countries: United States; United States
Enrollment: 20
Age range: 18 Years - 70 Years
Sex: ALL

Summary:
The goal of this clinical trial is to test the use of voice personalization through artificial intelligence (AI) voice cloning on speech entrainment tasks to improve language production of persons with aphasia (PWA). The main question the study aims to answer is:

\- What is the impact of personalized voice on speech entrainment in PWA compared to traditional speech entrainment?

Speech entrainment is a technique used by speech-language pathologists to improve the speech production of PWA. Traditionally, speech therapists act as the model for participants to speak along with to improve their speech production. This study proposes the use of one's own voice (digitally altered) to improve speech production.

The study uses a mobile health approach to administer speech entrainment treatment through a mobile app.

* Smartphones with the mobile app pre-installed will be mailed to participants at no cost.
* Participants will complete treatment in the comfort of their homes.
* The experimental treatments involve: mirror speech entrainment (speaking along to one's own voice) and traditional speech entrainment (speaking along to someone else's voice).

Eligibility criteria:
Inclusion Criteria:

* Adults between the ages of 18-70 years old
* Dominant English-speaking
* Aphasia due to left hemisphere stroke

Exclusion Criteria:

* Individuals with aphasia due to other causes such as dementia, Alzheimer's disease and Parkinsonism
* Non-dominant English adult speakers
* Persons with significant difficulties with language comprehension
* Persons with concomitant cognitive disability
* Persons with uncorrected sensory deficits.

Outcomes:
Speaking rate
Randomized, Controlled, Open Label, Phase 2 Clinical Trial of Interferon-β-1a (IFNβ-1a) in COVID-19 Patients
Trial ID: NCT04449380
Status: TERMINATED
Study type: INTERVENTIONAL
Phase: PHASE2
Start date: 2020-11-02
Completion date: 2021-03-30
Sponsor: Emanuele Bosi
Conditions: COVID-19 Virus Infection
Interventions: Interferon-ß-1a; Standard of Care (SOC)
Locations: IRCCS Ospedale San Raffaele
Countries: Italy
Enrollment: 56
Age range: 18 Years - None
Sex: ALL

Summary:
Pharmacological therapies of proven efficacy in coronavirus disease 2019 (COVID-19) are still lacking.

Since two clinical stages of COVID-19 are emerging, an early one with typical clinical characteristics of a viral infection (fever, malaise, cough) and a later one with pneumonia leading to progressive respiratory failure, associated with heavy, cytokine-mediated, inflammation, an intervention by a compound possessing both antiviral activity and immunomodulatory effects would be most effective at the earliest possible stage.

The purpose of this clinical trial is to test the efficacy of Interferon-β-1a (IFNβ-1a), in COVID-19 patients in an open label, randomized clinical trial.

The design of the study is to test IFNβ-1a in addition to standard of care compared with standard of care alone.

The primary outcome is the time to negative conversion of Severe Acute Respiratory Syndrome-CoronaVirus-2 (SARS-CoV-2) nasopharyngeal swabs.

Eligibility criteria:
Inclusion Criteria:

1. Informed consent signed
2. Patients hospitalized with confirmed swab RT-PCR detection of SARS-CoV-2
3. X-ray and/or CT diagnosed pneumonia
4. Age \>=18 years
5. Clinical status defined as 3, 4 or 5 on the 7-point ordinal scale

Exclusion Criteria:

1. Known allergy or hypersensitivity to IFNß-1a or IFNß-1b
2. Presence of severe concomitant illnesses/medical conditions that in the physician opinion do not allow participation to the study
3. Pregnant or lactating females
4. History of major depression disorder or suicidal attempt or suicidal ideation
5. Spontaneous blood alanine aminotransferase/aspartate aminotransferase (ALT/AST) levels \> 5 times the upper limit of normal
6. Clinical status defined as 1, 2, or 6 on the 7-point ordinal scale

Outcomes:
Time to negative conversion of SARS-CoV-2 nasopharyngeal swab
Creation of a Clinical Database of Lumbar Spine MRI
Trial ID: NCT06248827
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-11
Completion date: 2025-04
Sponsor: Caerus Medical
Collaborators: Centre hospitalier de l'Université de Montréal (CHUM)
Conditions: Low Back Pain
Interventions: Data Collection
Enrollment: 500
Age range: 18 Years - 99 Years
Sex: ALL

Summary:
The creation of a clinical database including patients who suffer from low back pain and underwent a lumbar spine MRI Exam.

This database will allow us to :

* Collect patients symptoms, medical history, and MRI exams
* Launch the annotation of the MRI exams by expert radiologists
* Link and relate information between the exams and the diagnostic done by the experts
* Train and develop a diagnostic platform for th spinal pathologies based on artificial intelligence.

Eligibility criteria:
Inclusion Criteria:

* Adult patient aged ≥ 18, no age limit;
* Suffering of low back pain ;
* Patient exam performed at the Centre Hospiralier Universitaire de Montreal (CHUM), Canada

Exclusion Criteria:

* Patients with orthopedic material
* Patients with traumatic cases (ex : accidents)

Outcomes:
Establish a database of patients who underwent a MRI exam of the lumbar spine at the Centre Hospitalier Universitaire de Montreal (CHUM) in Canada.
AI for All: Harnessing the Power of Artificial Intelligence in Mental Health Case Management
Trial ID: NCT06280170
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-02-19
Completion date: 2025-12-31
Sponsor: Eleos Health
Collaborators: Centerstone Research Institute
Conditions: Depressive Disorder; Anxiety Disorders; Substance Use Disorders; Post Traumatic Stress Disorder
Interventions: Artificial Intelligence platform for case managers
Locations: Centerstone
Countries: United States
Enrollment: 280
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this clinical trial is to assess the effectiveness of an artificial intelligence (AI) platform for case managers in a nonprofit health system specializing in mental health and substance use disorder. The main questions it aims to answer are:

1. Is the AI platform acceptable and feasible for case managers?
2. Does the AI platform improve providers' productivity and reported interventions? Participants will be approximately 30 case managers and their 250 adult clients receiving case management services. Researchers will compare the provider productivity and work satisfaction prior to the implementation of the AI platform to following its implementation.

Eligibility criteria:
FOR PROVIDERS:

Participating providers must be Centerstone case managers.

FOR CLIENTS:

Inclusion Criteria:

1. Participants must be adults.
2. Participants must be receiving case management services from a Centerstone provider

Exclusion Criteria:

1. Participants currently involved in any other concurrent research study will be excluded to avoid potential confounding factors.
2. Participants with any medical conditions or medications that may significantly interfere with the study outcomes will be excluded.

Outcomes:
Case manager satisfaction; Case manager productivity; Case manager note completion time
Leveraging Artificial Intelligence and Multi-Omics Data to Predict Opioid Addiction
Trial ID: NCT06540105
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-08-26
Completion date: 2027-09-30
Sponsor: University of California, San Diego
Collaborators: VA Palo Alto Health Care System; San Diego Veterans Healthcare System
Conditions: Opioid Use Disorder; Addiction, Opioid
Locations: University of California, San Diego
Countries: United States
Enrollment: 300
Age range: 18 Years - None
Sex: ALL

Summary:
The primary goal of this proposal is to validate a novel genomic and microbiome predictive model that may be used to assess a person's risk of developing opioid use disorder (OUD). The following will be tested: (1) MODUS (Measuring risk for Opioid use Disorder Using SNPs), which is a genomic panel consisting of a set number of proven single nucleotide polymorphisms (SNP) that utilizes machine learning to determine an individual's risk; and (2) MICROUD (MICRObiome for Opioid Use Disorder), which will be a novel microbiome prediction panel for OUD risk. MODUS and MICROUD will be developed using existing public datasets with genomic and microbiome data (e.g., All of Us, Human Microbiome Project). During development of these predictive models, in parallel, an external prospective validation cohort will be recruited consisting of subjects from the University of California, San Diego, Veteran Affairs of San Diego, and Veteran Affairs of Palo Alto (each site with separate IRB). The hypothesis is that MODUS and MICROUD will have high predictive potential for identifying high risk patients for OUD.

Eligibility criteria:
Inclusion Criteria:

* diagnosis of OUD (active or in remission) defined by the DSM-5 criteria
* age ≥ 18 years old

Exclusion Criteria:

* inability to participate independently with the study (i.e. dementia)
* chronic opioid use that is not consistent with a diagnosis of OUD
* patients that are pregnant
* children
* institutionalized individuals
* non-English speaking subjects as there are several surveys without appropriate translation and with sensitive information (e.g., questions about mental health and history of drug use) that is required to complete the study.

Outcomes:
opioid use disorder at baseline based on the DSM-5 criteria
Artificial Intelligence Assessment of Trainee Communication Compared to Human Assessment: A Prospective Cohort Study
Trial ID: NCT07107880
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-11-10
Completion date: 2026-11-14
Sponsor: Stanford University
Conditions: Communication Skills
Interventions: Communication Assessment through AI and Human
Locations: Lucile Packard Children's Hospital Stanford
Countries: United States
Enrollment: 45
Age range: 18 Years - 99 Years
Sex: ALL

Summary:
This study will evaluate whether a web-based artificial intelligence platform (AI) (Clinical Mind AI \[CMAI\] Stanford, CA), can assess communication skills in anesthesiology trainees, including residents and fellows, in the setting of disclosing medical errors. All participants will participate in an AI-generated simulation by using the platform remotely, and CMAI will assess trainee performance immediately after the simulation.

Eligibility criteria:
Inclusion Criteria:

* Participant's must be 18 years of age or older
* Graduate medical trainess such as residents and fellows at all levels of training excluding PGY1 trainees at Department of Anesthesiology, Perioperative, and Pain Medicine at Stanford University
* Able to speak and understand English
* Willing and able to provide consent to participate in research
* Able to participate in Artificial Intelligence communication simulation

Exclusion Criteria:

* Non-English speaker
* unable to provide consent to participate in research
* individual with no access to the necessary technology (internet, computer/smart device) required for participating in the digital, voice-based interaction with the AI platform

Outcomes:
Reliability of AI Conversational Performance Assessment