moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
An EHR-embedded Pragmatic Stepped-wedge Individual Randomized Clinical Trial of Ambient Artificial Intelligence to Improve Provider Well-Being
Trial ID: NCT06517082
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-08-15
Completion date: 2025-03-27
Sponsor: University of Wisconsin, Madison
Conditions: Healthcare Provider Well-being
Interventions: Artificial Intelligence
Locations: University of Wisconsin
Countries: United States
Enrollment: 66
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this clinical trial is to learn whether using Ambient Artificial Intelligence for provider documentation will enhance provider well-being and improve documentation quality.

Participants will complete their documentation using the Ambient AI software.

Eligibility criteria:
Inclusion Criteria:

* Healthcare Provider in the UW Health Outpatient Clinic Setting
* Willingness to engage and use ambient technology
* Owns an Apple mobile device, as the software is accessible only on this platform
* Adult (greater than 18 years)
* English or Spanish speaking
* Completed the training and in-servicing required to use the tool
* Providing outpatient care to no less than 20 encounters on a weekly average

Exclusion Criteria:

* Planned leave in 6 weeks following randomization
* Not registered onto Epic's mobile Haiku system for access
* Enrolled in a virtual scribe program and not willing to leave the program

Outcomes:
Change in provider fulfillment index; Change in provider burnout
Artificial Intelligence in a Mobile Intervention for Depression and Anxiety (AIM)
Trial ID: NCT02176226
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2015-03
Completion date: 2016-06
Sponsor: Northwestern University
Conditions: Major Depressive Disorder; Anxiety Disorders
Interventions: IntelliCare
Locations: Northwestern University
Countries: United States
Enrollment: 105
Age range: 19 Years - None
Sex: ALL

Summary:
This study will evaluate a Smartphone intervention for depression and anxiety that uses machine learning to tailor treatment for patients. The intervention, referred to as IntelliCare, delivers patient-specific treatment material and motivational messaging via a mobile phone, to help individuals with depression and/or anxiety. Information and data received from the patient will inform the tailored treatment approach through machine learning. The purpose of this study is to obtain preliminary information on the feasibility and effectiveness of IntelliCare in improving symptoms of depression and anxiety.

Eligibility criteria:
Inclusion Criteria:

* Meets criteria for clinically significant symptoms of depression or anxiety using self-report measures used in screening for depression, as well as interviewer administered measures to ensure symptoms are not transitory and have been present for at least two weeks.
* Is familiar with the use of mobile phones
* Has an Android phone and is willing download the IntelliCare apps on their own device
* Is able to speak and read English
* Is at least 19 years of age
* Is able to give informed consent

Exclusion Criteria:

* Has visual, hearing, voice, or motor impairment that would prevent completion of study and treatment procedures
* Has any psychiatric condition for which participation in a clinical trial of psychotherapy may be either inappropriate or dangerous (e.g., psychotic disorders, bipolar disorders, dissociative disorders, obsessive compulsive disorder, severe post traumatic stress disorder, etc.).
* Exhibits severe suicidality, including ideation, plan, and intent.
* Currently receiving psychotherapy or planning to receive psychotherapy during the 8 weeks of the study.
* Participants who have been on an antidepressant or anxiolytic medication with no dose changes for 2 weeks and do not intend to change the dose will be eligible. Those who have been on the antidepressant or anxiolytic for less than 2 weeks or intend to have their medication optimized will be asked to wait for screening until they meet the 2-week criterion.

Outcomes:
Patient Health Questionnaire - 9 (PHQ-9) - Depression Severity Module; GAD-7 (Generalized Anxiety Disorder Scale-7)
Development and Demonstration of Intelligent Assessment Based on Multi-modal Information Fusion for Tumor Risk and Diagnosis and Treatment
Trial ID: NCT06653478
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2022-06-01
Completion date: 2026-10-01
Sponsor: Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
Conditions: Artificial Intelligence; Deep Learning; Lung Cancer; Lung; Node; Stomach Cancer; Colon Cancer; Cancer Risk; Cancer Screening; Cancer, Treatment-Related
Interventions: No Intervention
Locations: Wuhan Union Hospital
Countries: China
Enrollment: 3000
Age range: 18 Years - 75 Years
Sex: ALL

Summary:
To improve the accuracy of risk prediction, screening and treatment outcome of cancer, we aim to establish a medical database that includes standardized and structured clinical diagnosis and treatment information, image features, pathological features, and multi-omics information and to develop a multi-modal data fusion-based technology system using artificial intelligence technology based on database.

Eligibility criteria:
Inclusion Criteria:

1. Participants with the suspected of lung cancer/node, or stomach cancer/lesion, or colorectal cancer/leision
2. Participants that have signed informed consent.
3. Participants with detailed electronic medical records, image records, pathological records, multi-omics information, and other important clinical diagnostic information.
4. Healthy participants with no clinical diagnosis of lung cancer/node, or stomach cancer/lesion, or colorectal cancer/leision.

Exclusion Criteria:

1. Participants with primary clinical and pathological data missing.
2. Participants lost to follow-up.
3. Participants with too poor medical image quality to perform segment and mark ROI accurately

Outcomes:
The outcome of clinical diagnosis of suspected patients with lung cancer/pulmonary nodular (Benign/Malignant nodule); The outcome of clinical diagnosis of suspected patients with colorectal cancer or lesion (Benign/Malignant).; Treatment response of anti-cancer therapy at first evaluation in patients with lung/stomach/colorectal cancer (CR, PR, PD, SD).
Development and Effectiveness Verification of an AI Application for Pressure Injury Nursing Based on Clinical Judgment Model
Trial ID: NCT07132320
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-11-15
Completion date: 2024-12-10
Sponsor: Yonsei University
Conditions: Pressure Ulcer; Nursing Education
Interventions: Pressure Ulcer Nursing AI Application Training; Traditional Pressure Ulcer Nursing Education
Locations: Severance Hospital, Wound Ostomy Team
Countries: South Korea
Enrollment: 95
Age range: 20 Years - None
Sex: ALL

Summary:
This clinical trial aims to develop a pressure ulcer nursing AI mobile application based on a clinical judgment model and verify its effectiveness among clinical nurses. The study consists of algorithm development, application design, implementation, and a comparative evaluation of nurses using the AI app versus a control group not given the application. Outcomes include clinical judgment, knowledge in pressure ulcer nursing, educational satisfaction, and pressure ulcer incidence. The investigators expect the AI-driven intervention to improve practical nursing skills, reduce pressure ulcer occurrence, and contribute to patient-centered care in hospital settings.

Eligibility criteria:
Inclusion Criteria:

* Registered nurse working at a tertiary hospital, directly engaged in patient care
* ≥3 years experience (algorithm development phase) or ≥1 year experience (evaluation phase)
* Written informed consent

Exclusion Criteria:

* Communication difficulties for interviews (analysis phase)
* Unable to understand education for AI application (evaluation phase)

Outcomes:
Change in Clinical Judgment Competence Score
Comparison Between Artificial Intelligence Assisted Capsule Endoscopy and Standard Reading to Investigate Suspected Crohn Disease: the SCAI STUDY
Trial ID: NCT07111715
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-07-21
Completion date: 2028-12-31
Sponsor: Fondazione Poliambulanza Istituto Ospedaliero
Conditions: Crohn Disease (CD)
Interventions: Small bowel Video Capsule Endoscopy (VCE) using an Capsule System equipped with a Deep Neural Network based system called SmartScan (SC),
Locations: Fondazione Poliambulanza Istituto Ospedaliero
Countries: Italy
Enrollment: 180
Age range: 18 Years - 75 Years
Sex: ALL

Summary:
The diagnosis of Crohn's Disease (CD) is based on a combination of clinical, biochemical (serological and fecal), endoscopic, radiological, and histological investigations. In the absence of obstructive symptoms or known stenosis, European guidelines recommend to investigate the small intestine using Video Capsule Endoscopy (VCE) if ileocolonoscopy is not decisive. To reduce the reading time of VCE and increase the number of identified lesions during the examination, various artificial intelligence software/tools have been developed in recent decades. This study aims to be the first prospective multicentric real-life trial to evaluate AI-assisted VCE using SmartScan in identifying typical mucosal abnormalities of the small intestine in patients with suspected CD and its ability to reduce reading time while maintaining the same diagnostic yield and diagnostic accuracy of standard reading. The objective of the study is to evaluate the role of AI-assisted VCE using the OMOM SmartScan in detecting typical small bowel inflammatory lesions (i.e. erosions and ulcers) in patients with suspected CD, and comparing AI with standard reading.

Eligibility criteria:
Inclusion Criteria:

* Age \>= 18 and \<= 75 years
* Clinical suspicion of Crohn's Disease (CD) with/without occlusive symptoms
* Ileocolonoscopy: negative examination, aspecific inflammatory findings
* Signed informed consent form

Exclusion Criteria:

* Known diagnosis of CD
* Endoscopic diagnosis of active diverticular disease, colorectal cancer, ulcerative colitis, or infectious colitis, microscopic colitis
* Positive stool tests for pathogenic bacteria, Yersinia enterocolitica, parasites, C. difficile infection, fecal antigen for Giardia lamblia within 6 months before VCE
* Known intestinal obstruction or unconfirmed small bowel patency
* Any use of NSAIDs in the 4 weeks before ileocolonoscopy and before VCE
* Known gastrointestinal motility disorder
* Known or suspected delayed gastric emptying
* Swallowing disorders
* Allergy or other contraindications or intolerance to the medications/devices used in the study
* Endoscopic placement of the capsule
* Any condition that prevents adherence to the study
* Pregnancy
* Participation in another clinical trial involving experimental drugs or devices
* Concomitant life-threatening condition
* Chronic kidney disease (eGFR\<30mL/min/1.73 m2)
* Inability to sign the informed consent

Outcomes:
Diagnostic yield (DY)
White Matter Hyperintensities in Migraine: A Multi-modal Imaging Project to Classify Disease Pathology
Trial ID: NCT05112289
Status: ACTIVE_NOT_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-08-16
Completion date: 2026-08
Sponsor: Mayo Clinic
Conditions: Migraine; Small Vessel Ischemic Disease; Multiple Sclerosis
Interventions: Brain Neuroimaging
Locations: Mayo Clinic in Arizona
Countries: United States
Enrollment: 50
Age range: 20 Years - 59 Years
Sex: FEMALE

Summary:
The purpose of this research is to better understand brain white matter hyperintensities (WMH) in women with migraines

Eligibility criteria:
Inclusion Criteria - Migraine (N=20):

* Female only.
* Ages 20-45 years.
* Presence of WMH on MRI.

Exclusion Criteria:

* History of SVI and/or history of MS or other neurological disease other than migraine.
* History of cancer or organ transplantation.
* Contraindication to MRI.
* Pregnancy.
* History of abnormal brain MRI other than WMH.
* Subjects not willing to comply with the study procedures will be removed from the study.

Inclusion Criteria - SVI disease (n=10):

* Female only
* Ages \> 60 years.
* Presence of WMH on MRI.

Exclusion Criteria:

* History of migraine and/or history of MS or other neurological disease other than SVI.
* History of cancer or organ transplantation.
* History of abnormal brain MRI other than WMH.
* Pregnancy.
* History of abnormal brain MRI other than WMH.
* Subjects not willing to comply with the study procedures will be removed from the study.

Inclusion Criteria - MS (n=10):

* Female only.
* Ages 20-45.
* Presence of WMH on MRI.

Exclusion Criteria:

* History of migraine and/or history of SVI or other neurological disease other than MS.
* History of cancer or organ transplantation.
* History of abnormal brain MRI other than WMH.
* Pregnancy.
* History of abnormal brain MRI other than WMH.
* Subjects not willing to comply with the study procedures will be removed from the study.

Outcomes:
WMH imaging disease parameters
Algorithm Development Through Artificial Intelligence for the Triage of Stroke Patients in the Ambulance With Electroencephalography
Trial ID: NCT05437237
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-06-19
Completion date: 2026-06
Sponsor: Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)
Conditions: Ischemic Stroke
Interventions: Dry electrode EEG
Locations: Amsterdam University Medical Centers, location AMC
Countries: Netherlands
Enrollment: 1192
Age range: 18 Years - None
Sex: ALL

Summary:
Endovascular thrombectomy (EVT) enormously improves the prognosis of patients with large vessel occlusion (LVO) stroke, but its effect is highly time-dependent. Direct presentation of patients with an LVO stroke to an EVT-capable hospital reduces onset-to-treatment time by 40-115 minutes and thereby improves clinical outcome. Electroencephalography (EEG) may be a suitable prehospital stroke triage instrument for identifying LVO stroke, as differences have been found between EEG recordings of patients with an LVO stroke and those of suspected acute ischemic stroke patients with a smaller or no vessel occlusion. The investigators expect EEG can be performed in less than five minutes in the prehospital setting using a dry electrode EEG cap. An automatic LVO-detection algorithm will be the key to reliable, simple and fast interpretation of EEG recordings by ambulance paramedics. The primary objective of this study is to develop one or more novel AI-based algorithms (the AI-STROKE algorithms) with optimal diagnostic accuracy for identification of LVO stroke in patients with a suspected acute ischemic stroke in the prehospital setting, based on ambulant EEG data.

Eligibility criteria:
Inclusion Criteria:

* Suspected AIS, as assessed by the attending ambulance paramedic, or a known LVO stroke;
* Onset of symptoms or last seen well \< 24 hours before EEG acquisition;
* Age of 18 years or older;
* Written informed consent by patient or legal representative (deferred).

Exclusion Criteria:

* Skin defect or active infection of the scalp in the area of the electrode cap placement;
* (Suspected) COVID-19 infection.

Outcomes:
One or more novel AI-based EEG algorithms based on dry electrode EEG-data with optimal diagnostic accuracy for LVO-a
Practical Geriatric Assessment in Older Adults With Non-Small Cell Lung Cancer Undergoing Stereotactic Body Radiation Therapy
Trial ID: NCT06987890
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-05-26
Completion date: 2026-06-26
Sponsor: Brigham and Women's Hospital
Conditions: Lung Cancer (NSCLC); Geriatric Assessment; Stereotactic Body Radiation Therapy (SBRT)
Interventions: Practical Geriatric Assessment
Locations: Brigham and Women's Hospital
Countries: United States
Enrollment: 64
Age range: 65 Years - None
Sex: ALL

Summary:
National guidelines recommend that older adults with cancer undergo a special health assessment before starting cancer treatment. This type of assessment evaluates physical function, nutrition, social support, psychological well-being, medical conditions (both cancer-related and non-cancer-related), and cognitive function. The results can help doctors make better treatment decisions and determine whether additional support services-such as nutrition counseling, physical therapy, or social work-would be beneficial. Even though these assessments are recommended, they are not typically used because they need to be performed by a specialist and can take over an hour to complete. Given these challenges, a 10-15-minute assessment called the Practical Geriatric Assessment (PGA) was recently developed. The PGA can be completed by any healthcare provider and helps identify older adults who may need extra support alongside their cancer treatment. While the PGA has the potential to make geriatric assessments more accessible, the investigators do not yet know whether patients will find it useful or easy to complete.

Additionally, it is unclear whether using the PGA will lead to more referrals for recommended supportive care services. This study aims to address these questions. The investigators will evaluate whether using the PGA impacts the number of patients referred to recommended supportive care services. Investigators will also evaluate how participants feel about completing the PGA, including how easy or difficult it is, and to assess the feasibility of implementing this survey on a larger scale. Finally, the investigators will use facial photographs and audio-visual data from the PGA to develop and evaluate artificial intelligence algorithm(s) to identify vulnerable patients who might benefit from additional supportive care services; namely, FaceAge, a validated deep learning model capable of estimating biological age from still facial images.

Eligibility criteria:
Inclusion Criteria:

* Age ≥ 65 years old at time of study enrollment.
* Radiographically or pathologically confirmed stage I-II non-small cell lung cancer.
* All patients must have undergone appropriate complete imaging of their cancer consistent with the standard of care.
* Patient is expected to undergo stereotactic body radiation therapy (SBRT)
* Able to read questions in English or willing to complete survey questionnaires with the assistance of an interpreter.

Exclusion Criteria:

* There are no exclusion criteria.

Outcomes:
Rate of referral to recommended supportive care services
Gastric Cancer and Artificial Intelligence: a National-level Project
Trial ID: NCT06275997
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-06-10
Completion date: 2028-06
Sponsor: Istituto Clinico Humanitas
Conditions: Gastric Cancer
Interventions: Integration of Artificial Intelligence (AI) assistance to screening gastroscopy
Enrollment: 6600
Age range: 60 Years - None
Sex: ALL

Summary:
Our GAIN project comprises four core work packages (WPs): WP1. Nation-level randomized controlled trial; WP2. Development of an innovative AI tool; WP3. Novel microsimulation modelling; WP4. Patient inclusion.

The nation-level multi-center tandem randomized controlled trial (WP1) will contribute to a better understanding of how the real-time AI algorithm can reduce miss rate of early gastric cancer and dysplasia during gastroscopy. Moreover, the innovation project will contribute to development of a novel AI tool (WP2) that can stratify the risk of gastric cancer by identifying in vivo precancerous conditions. Furthermore, a microsimulation modelling will allow us to predict how the use of AI can prevent gastric cancer and affect cost and patients' burdens. The assessment of the balance between benefits and harms is quite crucial especially for this type of medical device because the value of innovative tools is sometimes overestimated due to stakeholders' enthusiasm (WP3). Finally, we will take care of patients' perspective throughout the study project by including patient organization in both WP1, 2, and 3 (WP4).

Eligibility criteria:
Inclusion Criteria:

* All \>60 years-old patients undergoing upper-gastrointestinal (GI) endoscopy for selected indications in Italian areas at high-risk of gastric cancer (Lombardia, Emilia Romagna, Veneto, Friuli-Venezia Giulia).

Exclusion Criteria:

* contraindications to upper-GI endoscopy.
* contraindications to biopsy.
* active upper-GI bleeding or urgent upper-GI endoscopy.
* patients with previous upper-GI surgery involving the stomach.
* patients who were not able or refused to give informed written consent.

Outcomes:
Miss rate reduction
Comparison of Artificial Intelligence and Clinicians With Different Experience Levels in Assessing Gingival Phenotype
Trial ID: NCT07570290
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-05-15
Completion date: 2026-10-15
Sponsor: Ondokuz Mayıs University
Conditions: Gingival Phenotype Assessment
Interventions: Periodontal Probe Transparency Method; Visual Assessment Method; Deep Learning-Based Artificial Intelligence Model
Enrollment: 40
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this observational study is to compare the performance of clinicians with different experience levels and a deep learning-based artificial intelligence (AI) model in assessing gingival phenotype using two diagnostic methods: the periodontal probe transparency method and visual assessment from standardized clinical photographs. The main questions the study aims to answer are:

Can AI achieve comparable accuracy to human examiners in both probe transparency and visual assessment methods?

Does examiner experience level influence diagnostic performance and agreement with the reference standard in these methods?

Researchers will compare AI, dental students, and periodontology research assistants to determine accuracy, sensitivity, specificity, and agreement with the gold standard for each method.

Participants will:

Undergo standardized intraoral photography of maxillary anterior teeth, with and without a periodontal probe in place, following a validated protocol.

Have gingival phenotype determined by a reference periodontologist using the probe transparency method as the gold standard.

Have their photographs evaluated by AI, dental students, and research assistants for phenotype classification using both methods.

Eligibility criteria:
Inclusion Criteria for Volunteer Participants Who Will Participate in Transparency and Visual Assessment:

* Systemically and periodontally healthy individuals.
* Presence of natural maxillary anterior incisors.

Exclusion Criteria:

* Presence of fixed crowns or cervical restorations on the evaluated teeth.
* Pregnant or breastfeeding women.
* Signs of gingival inflammation or periodontal disease with attachment loss.
* Presence of buccal gingival recession.
* Use of medications known to cause gingival enlargement.
* Presence of congenital anomalies or dental structural defects.

Inclusion Criteria for Clinicians:

* Research assistants: Must be currently working in the Department of Periodontology.
* Dental Intern Students: Fourth- or fifth-year students who have completed periodontology clinical rotation.

Exclusion Criteria for Clinicians:

* Those who are confirmed to be color blind by the Ishihara test

Outcomes:
Diagnostic Accuracy of Each Examiner Group and AI Model in the Periodontal Probe Transparency Method