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69 Artificial Intelligence Trials Near You
Power is an online platform that helps thousands of Artificial Intelligence patients discover FDA-reviewed trials every day. Every trial we feature meets safety and ethical standards, giving patients an easy way to discover promising new treatments in the research stage.
Learn More About PowerAI-Optimized Weight Loss Treatment for Obesity
Philadelphia, Pennsylvania
This trial is testing a smart computer program that helps people lose weight by giving them personalized advice. It tracks their progress and provides the best support based on individual responses. The goal is to see if this method is more effective and cheaper than traditional weight loss coaching.
No Placebo Group
Trial Details
Trial Status:Active Not Recruiting
Trial Phase:Unphased
Key Eligibility Criteria
Disqualifiers:Cancer, Type I Diabetes, Psychosis, Others
336 Participants Needed
Computer-Aided Diagnosis Tool for Lung Nodules
Philadelphia, Pennsylvania
This is a pragmatic clinical trial that will study the effect of a radiomics-based computer-aided diagnosis (CAD) tool on clinicians' management of pulmonary nodules (PNs) compared to usual care. Adults aged 35-89 years with 8-30mm PNs evaluated at Penn Medicine PN clinics will undergo 1:1 randomization to one of two groups, defined by the PN malignancy risk stratification strategy used by evaluating clinicians: 1) usual care or 2) usual care + use of a radiomics-based CAD tool.
No Placebo Group
Trial Details
Trial Status:Recruiting
Trial Phase:Unphased
Age:35 - 89
Key Eligibility Criteria
Disqualifiers:Lung Cancer, Active Cancer, Others
300 Participants Needed
Voice-Activated Smart Speakers for Mild Cognitive Impairment
Atlanta, Georgia
This study aims to test a new technology-based program designed to help improve the ability to manage chronic conditions.
This program includes daily smart speaker use for managing different tasks and technology learning.
Proper self-management of chronic conditions is critical to the maintenance of health. Digital technologies offer substantial potential to enhance self-management behaviors. Voice-operated smart speakers hold promise due to their ability to provide functional, cognitive, and social stimulation, send targeted reminders, and assist with daily schedules. Unfortunately, many older adults who live in low-income communities lack the resources and proficiency to take advantage of these options. Additionally, cognitive impairment is prevalent in independent living older adults, more prevalent in low-income older adults.
The goal is to address these critical challenges by identifying smart speaker-based functions preferred by older adults, exploring their technology challenges, introducing them to these functions, and providing necessary technology training to improve self-efficacy in managing chronic conditions and enhance their engagement in self-management behaviors.
No Placebo Group
Trial Details
Trial Status:Not Yet Recruiting
Trial Phase:Unphased
Age:55+
Key Eligibility Criteria
Disqualifiers:Alzheimer's, Dementias, Severe Impairment, Others
20 Participants Needed
AI-Enhanced Detection for Aortic Stenosis
New York, New York
The DETECT-AS Diagnostic Study will assess the performance of artificial intelligence (AI) risk predictions to detect aortic stenosis using results from portable electrocardiogram (ECG) and cardiac ultrasound devices.
Trial Details
Trial Status:Not Yet Recruiting
Trial Phase:Unphased
Age:70+
Key Eligibility Criteria
Disqualifiers:Echocardiogram, Aortic Valve Replacement, Dementia, Others
410 Participants Needed
AI Analysis of 3D Ultrasound for IVF Outcomes
New York, New York
The use of machine learning techniques using an artificial intelligence tool is proposed to analyze clinical data to predict best possible IVF/ART outcomes. This tool has been utilized to accurately predict embryo quality here at Cornell. Utilizing this tool to assess objective clinical findings and predict outcomes of assisted reproductive techniques is sought, with the ultimate goal of an automated tool to reduce implicit physician bias. Within this goal, using this tool to objectively and accurately assess baseline ovarian reserve at the start of an ART cycle is proposed, using 3D sonography to image the ovary and artificial intelligence tool to objectively identify baseline antral follicle counts.
No Placebo Group
Trial Details
Trial Status:Recruiting
Trial Phase:Unphased
4000 Participants Needed
Canary App for Alzheimer's Disease
Hackensack, New Jersey
The goal is to assess the accuracy of an application that analyzes voice characteristics to diagnose patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD). The main question is whether the application's diagnosis is the same as the clinician's for MCI and AD patients.
No Placebo Group
Trial Details
Trial Status:Recruiting
Trial Phase:Unphased
Age:50+
Key Eligibility Criteria
Disqualifiers:Parkinson's, Huntington's, Cancer, Others
100 Participants Needed
AI Tool for Skin Conditions
Bronx, New York
The goal of this research study is to test a new, investigational tool that uses artificial intelligence (AI) to help primary care providers assess skin conditions. This tool is an AI-powered dermatology image reference app that works with a smartphone. For clarity, the AI makes no diagnoses; it provides reference images. Primary care providers then use their own medical judgement and training to make the diagnosis.
The sponsor aims to compare the diagnoses made by primary care providers (such as doctors, nurse practitioners, and physician assistants) with the support of the AI tool compared to a panel of dermatologists, who are setting the gold standard. By doing so, the sponsor can determine the value of the AI tool for primary care providers and understand how it might be used alongside traditional clinical care.
This AI capability complies with FDA regulatory guidelines and is not considered a medical device, similar to a Google image search, which returns similar looking images for reference purposes. For intervention, they healthcare providers use their own training and clinical judgement to make the diagnosis, and not the AI.
No Placebo Group
Trial Details
Trial Status:Recruiting
Trial Phase:Unphased
Key Eligibility Criteria
Disqualifiers:Physical Or Mental Limitations, Others
263 Participants Needed
Deep Learning Model for Cardiac Amyloidosis
New York, New York
This is a single center, diagnostic clinical trial in which the investigators aim to prospectively validate a deep learning model that identifies patients with features suggestive of cardiac amyloidosis, including transthyretin cardiac amyloidosis (ATTR-CA).
Cardiac Amyloidosis is an age-related infiltrative cardiomyopathy that causes heart failure and death that is frequently unrecognized and underdiagnosed. The investigators have developed a deep learning model that identifies patients with features of ATTR-CA and other types of cardiac amyloidosis using echocardiographic, ECG, and clinical factors. By applying this model to the population served by NewYork-Presbyterian Hospital, the investigators will identify a list of patients at highest predicted risk for having undiagnosed cardiac amyloidosis. The investigators will then invite these patients for further testing to diagnose cardiac amyloidosis. The rate of cardiac amyloidosis diagnosis of patients in this study will be compared to rate of cardiac amyloidosis diagnosis in historic controls from the following two groups: (1) patients referred for clinical cardiac amyloidosis testing at NewYork-Prebysterian Hospital and (2) patients enrolled in the Screening for Cardiac Amyloidosis With Nuclear Imaging in Minority Populations (SCAN-MP) study.
No Placebo Group
Trial Details
Trial Status:Recruiting
Trial Phase:Unphased
Age:50+
Key Eligibility Criteria
Disqualifiers:Primary Amyloidosis, Liver Transplant, Active Malignancy, Others
100 Participants Needed
AI-Generated Health Messages for Outpatient Care
Ottawa, Ontario
This study investigates the use of Generative AI (GAI) to support primary care practices in delivering accurate, accessible patient education. With the rise of health misinformation, increasingly complex patient needs, and a strained healthcare workforce, primary care must find new ways to communicate trusted health information effectively. Leveraging the Canadian Primary Care Information Network (CPIN), this study will generate patient education messages on key health topics using both GAI and human content experts.
Diverse review panels of patients and providers will assess the messages on quality of information, adaptability, and relevance and usefulness, with special attention to socioeconomic factors that may impact message accessibility. CPIN will recruit a diverse sample of participants to evaluate both GAI- and human-generated messages. Review panels will provide structured feedback via surveys, aiming to identify differences in content quality and effectiveness.
The study's goal is to determine whether GAI can produce high-quality health information that meets primary care standards. Results will reveal how GAI tools can support primary care in reducing misinformation and administrative burdens, fostering patient-provider relationships, and improving health equity. Findings will inform best practices for integrating GAI in primary care to ensure accessible, timely patient education across Canada.
No Placebo Group
Trial Details
Trial Status:Active Not Recruiting
Trial Phase:Unphased
Key Eligibility Criteria
Disqualifiers:Illiteracy, No Email, No Device, Others
50 Participants Needed
AI Decision Support for Gastrointestinal Bleeding
New Haven, Connecticut
The purpose of this research study is to measure the effect on of a large language model interface on the usability, attitudes, and provider trust when using a machine learning algorithm-based clinical decision support system in the setting of bleeding from the upper gastrointestinal tract (upper GIB). Specifically, the investigators are looking to assess the optimal implementation of such machine learning algorithms in simulation scenarios to best engender trust and improve usability. Participants will be randomized to either machine learning algorithm alone or algorithm with a large language model interface and exposed to simulation cases of upper GIB.
No Placebo Group
Trial Details
Trial Status:Recruiting
Key Eligibility Criteria
Disqualifiers:Not Listed
102 Participants Needed
Novel Enuresis Alarm vs Standard Alarm for Bedwetting
New Haven, Connecticut
This trial compares two bedwetting devices, GoGoband® and another device, in children who wet the bed but do not have ADHD. Both devices work by waking the child when they start to wet the bed, helping them learn to stay dry. The body-worn alarm has been shown to be as effective as other methods and superior in terms of rapidity of response and consumer appeal.
No Placebo Group
Trial Details
Trial Status:Recruiting
Age:6 - 21
Key Eligibility Criteria
Disqualifiers:Not Listed
100 Participants Needed
AI Chatbot for Urogynecologic Conditions
Hartford, Connecticut
The goal of this clinical trial is to learn about how Urogynecology patients use Artificial Intelligence (AI) Chatbots like ChatGPT, and how it affects healthcare decision making. The main question\[s\] it aims to answer are:
* How does the AI Chatbot affect participants' understanding of diagnoses and participant satisfaction with a urogynecology consultation?
* How accurate is the chatbot-provided diagnosis and counseling information? Participants will be asked to use the ChatGPT chatbot and ask it questions about the main problem the participant is seeing the doctor for, and will also be asked to fill out some questionnaires.
Researchers will compare using the Chatbot before the visit, after the visit, or not at all to see if the way participants understand the information changes based on timing of use.
No Placebo Group
Trial Details
Trial Status:Active Not Recruiting
Sex:Female
Key Eligibility Criteria
Disqualifiers:Not Listed
125 Participants Needed
AI-Enhanced ECG Screening for Cardiomyopathy
Rochester, Minnesota
The purpose of this study is to assess the feasibility and impact of screening FDR of DCM probands using a mobile ECG with the ability to transmit the ECG for cloud-based AI analysis to detect reduced left ventricular ejection fraction (LVEF). This protocol will examine the impact of incorporating the screening AI enhanced ECG into standard of care recommendations.
No Placebo Group
Trial Details
Trial Status:Recruiting
Trial Phase:Unphased
Key Eligibility Criteria
Disqualifiers:Ischemic Heart Disease, Valvular Abnormality, Congenital Heart Disease, Hypertension, Others
250 Participants Needed
MRI Screening for High-Risk Breast Cancer Detection
Worcester, Massachusetts
Accurate risk assessment is essential for the success of population screening programs and early detection efforts in breast cancer. Mirai is a new deep learning model based on full resolution mammograms.
Mirai is a mammography-based deep learning model designed to predict risk at multiple timepoints, leverage potentially missing risk factor information, and produce predictions that are consistent across mammography machines. Mirai was trained on a large dataset from Massachusetts General Hospital (MGH) in the United States and found to be significantly more accurate than the Tyrer-Cuzick model, a current clinical standard.
The primary aim of this study is to prospectively quantify the clinical benefit (i.e. MRI/CEM cancer detection rate) of Mirai-based guidelines and to compare them to the current standard of care.
1. Conduct a prospective study where patients who are identified as high risk by Mirai guidelines are invited to receive supplemental MRI within 12 months.
2. Compare cancer outcomes between patients only identified as high risk by Mirai and patients identified as high risk by existing guidelines The secondary aim is to study the impact of new guidelines by race and ethnicity, to ensure equitable improvements in cancer screening.
No Placebo Group
Trial Details
Trial Status:Recruiting
Trial Phase:Unphased
Age:40+
Sex:Female
Key Eligibility Criteria
Disqualifiers:Under 40, Pregnant, Male, Others
200 Participants Needed
Arterial Measurement Sites for Hemodynamic Management
Montreal, Quebec
The goal of this clinical trial is to demonstrate the importance of arterial pressure measurement sites during major abdominal surgeries. This randomized controlled trial will compare arterial pressure measurements obtained from radial artery catheterization (the current standard method of monitoring) with those obtained from brachial artery catheterization (a more accurate reflection of central arterial pressure). At the end of the study, we are looking to answer the following questions:
1. Does arterial pressure measurement sites influence the amount of vasopressors that is administered during major abdominal surgeries?
2. What are the instances where there is a difference between peripheral (radial catheter) and central (brachial catheter) monitoring and what are the risk factors leading to the appearance of this radial-brachial pressure gradient?
3. With the data collected, can artificial intelligence based analysis help predict the reliability of a radial monitoring and help guide clinicians on choosing a peripheral versus central arterial pressure monitoring site? All adult participants who are scheduled for elective major abdominal surgeries and meeting our inclusion criteria will be approached and included if they consent. Participants will be randomized 1:1 in the intervention group and the standard of care group. In the intervention group, the brachial arterial line will be used intraoperatively to guide vasopressor and fluid administration. A radial line will also be installed to measure the radial arterial pressure simultaneously, but will not be used to guide hemodynamic management. In the standard of care group, both lines will be installed just like in the intervention group, however, it is the radial arterial line that will guide fluid and vasopressor administration. In both groups, the anesthesia protocol will be standardized and the anesthesiologist will be blinded to the arterial pressure measurement site.
No Placebo Group
Trial Details
Trial Status:Not Yet Recruiting
Trial Phase:Unphased
Key Eligibility Criteria
Disqualifiers:Severe Vascular Disease, Arterial Gradient, Allergies, Others
204 Participants Needed
AI-Enhanced ECG Interpretation for Structural Heart Disease
Montreal, Quebec
The HEART-AI (Harnessing ECG Artificial Intelligence for Rapid Treatment and Accurate Interpretation) is an open-label, single-center, randomized controlled trial, that aims to deploy a platform called DeepECG at point-of-care for AI-analysis of 12-lead ECGs. The platform will be tested among healthcare professionals (medical students, residents, doctors, nurse practitioners) who read 12-lead ECGs. In the intervention group, the platform will display the ECHONeXT structural heart disease (SHD) scores in randomized patients to help doctors prioritize transthoracic echocardiography (TTEs) and reduce the time to diagnosis of structural heart disease. Also, this platform will display the DeepECG-AI interpretation which detects problems such as ischemic conditions, arrhythmias or chamber enlargements and acts an improved alternative to commercially available ECG interpretation systems such as MUSE.
Our primary objective is to assess the impact of displaying the ECHONeXT interpretation on 12-lead ECGs on the time to diagnosis of Structural Heart Disease (SHD) among newly referred patients at MHI. We will compare the time interval from the initial ECG to SHD diagnosis by transthoracic echocardiogram (TTE) between patients in the intervention arm (where ECHONeXT prediction of SHD and TTE priority recommendation are displayed) and patients in the control arm (where ECHONeXT prediction and recommendation are hidden).
The main secondary objective is to evaluate the rate of SHD detection on TTE among newly referred patients. We also aim to assess the delay between the time of the first ECG opened in the platform and the TTE evaluation among newly referred patients at high or intermediate risk of SHD.
By integrating an AI-analysis platform at the point of care and evaluating its impact on ECG interpretation accuracy and prioritization of incremental tests, the HEART-AI study aims to provide valuable insights into the potential of AI in improving cardiac care and patient outcomes.
No Placebo Group
Trial Details
Trial Status:Enrolling By Invitation
Trial Phase:Unphased
Key Eligibility Criteria
Disqualifiers:Arrhythmia, Heart Failure, Coronary Artery Disease, Others
16160 Participants Needed
Multidisciplinary Screening for Heart Failure
Montréal, Quebec
This trial uses a computer program to identify potential heart failure patients from medical records, followed by blood tests and AI heart imaging. It targets patients at risk of heart failure to catch the condition early. The process involves scanning records, testing blood for a specific marker, and using AI for detailed heart images if needed.
No Placebo Group
Trial Details
Trial Status:Recruiting
Trial Phase:Unphased
Age:40+
Key Eligibility Criteria
Disqualifiers:Heart Failure, Renal Replacement, Others
1360 Participants Needed
AI Screening for Diabetic Retinopathy
Montréal, Quebec
This prospective study aims to validate if NeoRetina, an artificial intelligence algorithm developped by DIAGNOS Inc. and trained to automatically detect the presence of diabetic retinopathy (DR) by the analysis of macula centered eye fundus photographies, can detect this disease and grade its severity.
No Placebo Group
Trial Details
Trial Status:Recruiting
Trial Phase:Unphased
Key Eligibility Criteria
Disqualifiers:Under 18, Prior Retinal Treatment, Others
630 Participants Needed
MARVIN Chatbots for Breast Cancer
Montréal, Quebec
This research is a continuation of a usability study with the MARVIN chatbot. The investigators aim to adapt the MARVIN chatbot to open it to other health domains (e.g. breast cancer) and populations (e.g. pharmacists). Therefore, this protocol constitutes a master research protocol that will englobe different research projects with individual chatbots. The investigators adopt an adaptive platform trial design, which will allow flexibility in handling multiple interventions adapted to different populations while retaining the characteristics of a platform trial design allowing early withdrawal of ineffective trial arms based on interim data (implementation outcomes) and introduction of new trial arms.
No Placebo Group
Trial Details
Trial Status:Recruiting
Trial Phase:Unphased
Age:14+
Key Eligibility Criteria
Disqualifiers:Cognitive Deficit, Others
400 Participants Needed
Therabot-CALM for Anxiety and Depression
Lebanon, New Hampshire
The goal of this clinical trial is to learn if Therabot-CALM (Cannabis, Anxiety, Low Mood) has acceptability among users and could work to improve the symptoms of persons with cannabis use disorder and anxiety and/or depression. The main question it aims to answer is:
What is the usability, feasibility, and acceptability of Therabot-CALM in persons with Cannabis Use Disorder and Anxiety and/or Depression?
Participants will
* Take a screening questionnaire
* Participate in two virtual 1-hour interviews to provide feedback on app design and suggest features.
* Engage with Therabot-CALM in a 4-week clinical trial and provide feedback on their app experience in a third virtual interview
No Placebo Group
Trial Details
Trial Status:Recruiting
Trial Phase:Phase 1
Key Eligibility Criteria
Disqualifiers:Psychosis, STBs Risk, Others
15 Participants Needed
Why Other Patients Applied
"I've tried several different SSRIs over the past 23 years with no luck. Some of these new treatments seem interesting... haven't tried anything like them before. I really hope that one could work."
ZS
Depression PatientAge: 51
"I have dealt with voice and vocal fold issues related to paralysis for over 12 years. This problem has negatively impacted virtually every facet of my life. I am an otherwise healthy 48 year old married father of 3 living. My youngest daughter is 12 and has never heard my real voice. I am now having breathing issues related to the paralysis as well as trouble swallowing some liquids. In my research I have seen some recent trials focused on helping people like me."
AG
Paralysis PatientAge: 50
"As a healthy volunteer, I like to participate in as many trials as I'm able to. It's a good way to help research and earn money."
IZ
Healthy Volunteer PatientAge: 38
"I changed my diet in 2020 and I’ve lost 95 pounds from my highest weight (283). I am 5’3”, female, and now 188. I still have a 33 BMI. I've been doing research on alternative approaches to continue my progress, which brought me here to consider clinical trials."
WR
Obesity PatientAge: 58
"I was diagnosed with stage 4 pancreatic cancer three months ago, metastatic to my liver, and I have been receiving and responding well to chemotherapy. My blood work revealed that my tumor markers have gone from 2600 in the beginning to 173 as of now, even with the delay in treatment, they are not going up. CT Scans reveal they have been shrinking as well. However, chemo is seriously deteriorating my body. I have 4 more treatments to go in this 12 treatment cycle. I am just interested in learning about my other options, if any are available to me."
ID
Pancreatic Cancer PatientAge: 40
Ultrasound Algorithms for Pregnancy
Minneapolis, Minnesota
The goal of this clinical trial is to
* Collect ultrasound data from pregnant and non-pregnant individuals presenting to multiple study sites.
* Use the collected data and ultrasound images to train and validate Artificial intelligence algorithms developed by the Sponsor
Consented participants will be asked to take part in a research ultrasound scan
No Placebo Group
Trial Details
Trial Status:Not Yet Recruiting
Trial Phase:Unphased
Sex:Female
Key Eligibility Criteria
Disqualifiers:Unable To Consent, Compromised Care
700 Participants Needed
AI Triage for Breast Cancer Screening
Boston, Massachusetts
One method of breast cancer screening involves radiologists reading digital tomosynthesis (DBT) images. DBT consists of a 3D stack of x-ray "slices" through the breast. The exam is accompanied by a 2D image like a standard mammogram, a single x-ray of the breast. In a screening setting, most cases are normal. Sometimes it is obvious that a case is normal from a quick look at the 2D image. It would speed up the process of screening if readers could dismiss a clearly normal case on the basis of the 2D image, alone, without looking at the DBT images. Obviously, the investigators would only want to "triage" cases in this way if the investigators were almost perfectly sure that no cancers would be missed. In this study, the investigators look at radiologist's willingness to triage cases and on the accuracy of their answers. In addition, the investigators ask about the impact of an Artificial Intelligence (AI) opinion. Would it be possible to triage an image on the basis of the AI opinion, alone?
Radiologists will look at each case for up to five seconds and offer an opinion (on a 1-10 scale) about how sure they are that a case is normal. Next, they will see the opinion of the AI. Finally, they will say (using a 1-10) scale, how willing they would be for the AI to triage this case without human intervention.
This study is the start of an effort to understand the conditions under which radiologists might be willing to declare a case "normal" with little or no human examination.
No Placebo Group
Trial Details
Trial Status:Recruiting
Trial Phase:Unphased
Key Eligibility Criteria
Disqualifiers:Acuity Less Than 20/25
30 Participants Needed
Smart Reminders for Treatment Compliance
Tallahassee, Florida
This study will examine whether among older adults an adaptive and personalized reminder system can better support adherence to home-based cognitive training over typical reminder systems.
No Placebo Group
Trial Details
Trial Status:Recruiting
Age:65+
Key Eligibility Criteria
Disqualifiers:Not Listed
190 Participants Needed
AI-Assisted Detection for Adenoma During Colonoscopy
Montréal, Quebec
This is a pragmatic, double-blind, randomized, controlled trial, to evaluate the effect of implementing a Computer-assisted detection (CADe) system within the routine clinical practice of Canadian healthcare institutions.
The main hypothesis of this study is that the ADR in the operating room equipped with the GI genius CADe system will be significantly higher than the ADR in the ordinary operating room.
No Placebo Group
Trial Details
Trial Status:Recruiting
Trial Phase:Unphased
Age:45 - 89
Key Eligibility Criteria
Disqualifiers:Emergency Colonoscopy, Familial Polyposis, Inflammatory Bowel, Others
1596 Participants Needed
ICG for Gallbladder Disorders
Houston, Texas
The goal of this study is to evaluate the utility and efficacy of an artificial intelligence (AI) model at identifying structures and phases of surgery compared to traditional white light assessment by trained surgeons. Surgeons will perform the procedure in their standard practice, while the AI model analyzes data from the laparoscopic camera. Surgeons will be asked to audibly state when they identify structures and enter different phases of the surgical procedure.
The AI will not alter the surgeon's view or be visible to the surgeon, and the surgeon will perform the procedure in the exact same fashion as they typically do.
No Placebo Group
Trial Details
Trial Status:Recruiting
Trial Phase:Unphased
Key Eligibility Criteria
Disqualifiers:Pregnancy, Liver Disease, Renal Dysfunction, Others
Must Not Be Taking:Investigational Agents
120 Participants Needed
AI-Assisted Treatment Planning for Gastrointestinal Cancer
Duarte, California
This study will evaluate the utility of ChatGPT in recommending treatment plans for patients with gastrointestinal cancers, using both retrospective and prospective data.
No Placebo Group
Trial Details
Trial Status:Recruiting
Trial Phase:Unphased
Key Eligibility Criteria
Disqualifiers:Non-GI Cancers, Multiple Hospitals, Others
400 Participants Needed
Advanced Imaging Technology for Ventricular Tachycardia
La Jolla, California
The purpose of this study is to evaluate the clinical outcomes (clinical efficacy and safety) of using supplemental non-invasive computational ECG and cardiac imaging analysis tools to help guide ablation of ventricular tachycardia.
No Placebo Group
Trial Details
Trial Status:Enrolling By Invitation
Trial Phase:Unphased
Age:21 - 90
Key Eligibility Criteria
Disqualifiers:Diabetes, Hypertension, Bipolar, Others
250 Participants Needed
Artificial Intelligence for Diabetic Retinopathy
San Diego, California
This research study is being conducted to improve eye care by using artificial intelligence (AI) to make diabetic eye screenings faster and more accessible. AI technology mimics human decision-making, enabling computers and systems to analyze medication information. Specifically for this screening, AI examines digital images of the eye and based on that information, may identify if a participant has diabetic retinopathy. It can assist doctors in making decisions about a participant's diagnosis, treatment or care plans to improve patient care. This is a collaboration between San Ysidro Health (SYHealth), University of California, San Diego (UC San Diego), and Eyenuk. The Kaiser Permanente Augmented Intelligence in Medicine and Healthcare Initiative (AIM-HI) awarded SYHealth funds to demonstrate the value of AI technologies in diverse, real-world settings.
No Placebo Group
Trial Details
Trial Status:Enrolling By Invitation
Trial Phase:Unphased
Age:22+
Key Eligibility Criteria
Disqualifiers:Diabetic Retinopathy, Macular Edema, Retinal Vascular Occlusion, Others
848 Participants Needed
AI Scribe Tools for Physician Workflow
Los Angeles, California
This is a three-arm pragmatic RCT of 238 outpatient physicians at a large academic health system, randomized 1:1:1 to one of two AI scribe tools or a usual-care control group. The two-month study will observe and compare the effects of each tool prior to system-wide roll out of selected tool (anticipated Spring 2025). We will use covariate-constrained randomization to balance the arms in terms of physician baseline time in notes, survey-measured level of burnout, and clinic days per week.
The primary purpose of the initiative is to improve quality, efficiency, and business operations at University of California, Los Angeles (UCLA) Health, and this initiative is not being done for research purposes. The results of this operational initiative will inform the widespread roll out of AI scribe tools across all providers within the UCLA Health System. Nevertheless, the UCLA study team plans to rigorously examine and publish the impact of this intervention across the health system, which is why the study team pre-registered the initiative.
No Placebo Group
Trial Details
Trial Status:Active Not Recruiting
Key Eligibility Criteria
Disqualifiers:Not Listed
238 Participants Needed
AI Tool for Breast Cancer Screening
Los Angeles, California
The goal of this clinical trial is to compare patient-centered outcomes when 3D screening mammograms are interpreted with versus without a leading FDA-cleared AI decision-support tool in real-world U.S. settings and to assess patient's perspectives on AI in medicine.
The main questions it aims to answer are:
1. Will AI use be associated with an increase in cancer detection and an initially higher recall rate as radiologists start using AI, followed by a recall rate comparable to that without AI (no more than 1.5 percentage-points higher) after a learning curve period? Will AI use will be associated with lower rates of missed breast cancers and similar rates of false alarms after a learning curve period?
2. Will improved patient outcomes with AI be most pronounced for exams on women who are White, older, and have less dense breasts, and on baseline exams? Will AI aid patient outcomes when the interpretation is by radiologists with less clinical experience, lower annual interpretive volume, and less tolerance of ambiguity? Yet, will there be greater automation bias (the tendency for humans to defer to a computer algorithms' results) noted among these radiologists?
3. What are patients' perspectives on AI in mammography, including their confidence in breast cancer screening when interpreted with vs. without AI? What are patients' perspectives on the importance of the study results?
Researchers will compare patient-centered outcomes when 3D screening mammograms are interpreted with versus without a leading FDA-cleared AI decision-support tool in real-world U.S. settings.
This trial will include all adult patients undergoing 3D mammography breast cancer screening at imaging facilities across University of California at Los Angeles and University of Washington health systems and all radiologists interpreting breast cancer screening. All screening mammograms at these facilities will be randomized to either intervention (radiologist with AI support) versus usual care (radiologist alone) to see if interpreting these mammograms with the AI tool's assistance improves patient outcomes.
No Placebo Group
Trial Details
Trial Status:Not Yet Recruiting
Trial Phase:Unphased
Key Eligibility Criteria
Disqualifiers:Non-adult, Non-patient, Non-radiologist, Others
154474 Participants Needed
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Frequently Asked Questions
How much do Artificial Intelligence clinical trials pay?
Each trial will compensate patients a different amount, but $50-100 for each visit is a fairly common range for Phase 2–4 trials (Phase 1 trials often pay substantially more). Further, most trials will cover the costs of a travel to-and-from the clinic.
How do Artificial Intelligence clinical trials work?
After a researcher reviews your profile, they may choose to invite you in to a screening appointment, where they'll determine if you meet 100% of the eligibility requirements. If you do, you'll be sorted into one of the treatment groups, and receive your study drug. For some trials, there is a chance you'll receive a placebo. Across Artificial Intelligence trials 30% of clinical trials have a placebo. Typically, you'll be required to check-in with the clinic every month or so. The average trial length for Artificial Intelligence is 12 months.
How do I participate in a study as a "healthy volunteer"?
Not all studies recruit healthy volunteers: usually, Phase 1 studies do. Participating as a healthy volunteer means you will go to a research facility several times over a few days or weeks to receive a dose of either the test treatment or a "placebo," which is a harmless substance that helps researchers compare results. You will have routine tests during these visits, and you'll be compensated for your time and travel, with the number of appointments and details varying by study.
What does the "phase" of a clinical trial mean?
The phase of a trial reveals what stage the drug is in to get approval for a specific condition. Phase 1 trials are the trials to collect safety data in humans. Phase 2 trials are those where the drug has some data showing safety in humans, but where further human data is needed on drug effectiveness. Phase 3 trials are in the final step before approval. The drug already has data showing both safety and effectiveness. As a general rule, Phase 3 trials are more promising than Phase 2, and Phase 2 trials are more promising than phase 1.
Do I need to be insured to participate in a Artificial Intelligence medical study?
Clinical trials are almost always free to participants, and so do not require insurance. The only exception here are trials focused on cancer, because only a small part of the typical treatment plan is actually experimental. For these cancer trials, participants typically need insurance to cover all the non-experimental components.
What are the newest Artificial Intelligence clinical trials?
Most recently, we added Pediatric eCART for High-Risk Outcomes, Arterial Measurement Sites for Hemodynamic Management and AI Tool for Breast Cancer Screening to the Power online platform.
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