Clinical trials
Machine Learning clinical trials
Below are recruiting machine learning clinical trials, each written for real people, not researchers. We’re tracking 90 recruiting studies, each written for real people, not researchers, below.
Recruiting studies
- NCT07129616Recruiting
Remote monitoring for childhood asthma
This study tests if remote monitoring tools can help manage asthma in children and young people. It aims to see if tracking symptoms and medication use from home improves care.
EdinburghAges 5–17 - NCT07418632Recruiting
Motion training for neck pain using smart classification
This study tests a special motion-training program for people with neck pain. It uses a computer to classify and guide your movements, which may help reduce pain better than usual care.
LjubljanaAges 18–65 - NCT07447596Recruiting
Lung health study using breathing tests and AI
This study uses a simple breathing test called oscillometry along with machine learning to better understand lung conditions like COPD, asthma, and interstitial lung disease. It may help find new ways to diagnose and track these diseases.
BarcelonaAges 18–99 - NCT07110688Recruiting
Brain sensing study for mental states
This trial uses sensors and machine learning to understand mental states in healthy adults and people with epilepsy. Researchers aim to improve how we monitor and support mental health.
Downey, CaliforniaAges 18 years+ - NCT05567640Recruiting
Smart digital mental health support
This study tests a digital mental health program that uses smart technology to personalize your experience and keep you engaged. If you join, you may get a more tailored approach to improving your mental well-being.
Boston, MassachusettsAges 18 years+ - NCT06853301Recruiting
A faster test to find bloodstream infections
This study tests a new machine learning method to quickly identify bacteria in the blood using special sample bottles. If you are getting blood cultures done already, this may help get results faster.
GrenobleAges 18 years+ - NCT07714863Recruiting
Can machine learning predict ABPA recurrence?
This study uses a computer model to see if it can predict when ABPA (a lung condition) might come back. If you have ABPA and are stable after treatment, this could help doctors monitor you better.
Jinan, ShandongAges 18 years+ - NCT04060706Recruiting
Testing new computer tools for radiation treatment planning
This study tests how well new computer methods (called algorithms and machine learning) can support radiation therapy decisions. You might benefit by helping improve how radiation is planned and checked, especially for cancers treated with curative-intent therapy.
Cambridge, CambridgeshireAges 18 years+ - NCT04193475Recruiting
Uses computer learning to improve stress heart ultrasound
This study uses computer technology (“machine learning”) with stress echocardiogram images (a heart ultrasound done during stress) to improve how heart function is measured. If you are able to have a stress echocardiogram, you may be able to help test a better way to interpret the results.
CottinghamAges 20–89 - NCT04726228Recruiting
Using machine learning to improve pain assessment in advanced cancer
This trial looks at how a computer “learns” to better understand and assess cancer pain at home. It may help people with advanced cancer get more accurate pain evaluation while they are receiving pain treatment.
Naples, CampaniaAges 18 years+ - NCT05579496Recruiting
Rebooting infant pain care using skin-to-skin contact
This trial tests how skin-to-skin contact affects pain and brain activity in premature babies during a routine heel prick. It may help find better ways to manage pain for infants in the NICU.
Toronto, OntarioAges 6 months–8 months - NCT05188183Recruiting
Remote brain stimulation plus hand training for phantom limb pain
This trial tests whether remote brain stimulation (tDCS) combined with sensory (hand/limb) training can reduce phantom limb pain. It also uses computer prediction (“machine learning”) to estimate who is most likely to benefit.
Cambridge, MassachusettsAges 18 years+ - NCT05371405Recruiting
Using computer learning to improve care for atrial fibrillation patients
This study uses machine learning (computer pattern-finding) to better understand atrial fibrillation and guide care around the time of an ablation procedure. It may help refine how doctors treat certain AF patients based on their heart patterns.
Stanford, CaliforniaAges 22–80 - NCT05692830Recruiting
Test a wearable patch to estimate blood alcohol level
This study checks how accurately a new wearable skin patch estimates your blood alcohol level. It uses larger human testing and computer methods to improve the patch’s accuracy.
Champaign, IllinoisAges 21 years+ - NCT05732974Recruiting
Using a computer tool to find lung cancer relapse risk
This study uses a machine-learning computer method to identify people with early-stage lung cancer who have a higher risk of the cancer coming back after surgery. If you qualify, your information may help researchers improve how doctors predict relapse and plan follow-up care.
ToulouseAges 18 years+ - NCT05736302Recruiting
Check an activity-tracking algorithm against a body-weight test
This trial checks whether a new computer algorithm that uses an accelerometer (movement sensor) can accurately estimate your body’s activity and energy use. It compares the phone/wearable movement data to results from a special way of measuring how your body uses energy.
Milwaukee, WisconsinAges 18 years+ - NCT05739331Recruiting
AI-assisted breathing test to check swollen chest lymph nodes
This trial uses an AI-supported breathing procedure to help doctors examine enlarged lymph nodes in the chest when the cause is still unknown. It may help improve how accurately and efficiently doctors assess these lymph nodes so you can get answers sooner.
LevangerAges 18 years+ - NCT05771844Recruiting
Home sleep treatment for older adults with memory changes
This study tests whether a home sleep therapy program can improve symptoms in older adults who have amnestic mild cognitive impairment (MCI) or help compare to people without MCI. It may be useful if your main issue is memory or thinking changes and you also have sleep problems.
Winston-Salem, North CarolinaAges 40–85 - NCT05876598Recruiting
Predict bloodstream infection risk using a computer model
This study builds and tests a computer (machine-learning) model to predict bloodstream infections based on what happens in the first days of care. It may help doctors start the right antibiotic faster for patients who truly have an infection.
RomeAges 18 years+ - NCT06066372Recruiting
Using computer models to reduce need for scans for bile duct stones
This trial tests if a computer model can accurately predict when someone has a stone in the bile duct, so fewer people need invasive scans like endoscopic ultrasound or MRCP. It may help avoid unnecessary procedures in patients with a medium chance of having a bile duct stone.
Hyderabad, TelanganaAges 18–80 - NCT06069973Recruiting
Using AI to spot delayed brain injury after a brain bleed
This study uses artificial intelligence (machine learning) and blood tests to find early signs of delayed brain injury after a brain bleed. It aims to help doctors detect and treat this complication sooner.
GothenburgAges 18–110 - NCT06277297Recruiting
Heart MRI study for Takotsubo syndrome patients
This study uses MRI to learn more about Takotsubo syndrome (a temporary heart condition often triggered by stress). If you have been diagnosed with Takotsubo and have had certain heart tests, you may be able to join.
Cagliari, ItalyAges 18 years+ - NCT06364670Recruiting
Acupuncture for tinnitus: brain imaging study
This study is testing whether acupuncture can help treat tinnitus by using a special brain imaging technique (fNIRS) to see how the brain responds. It may help find out who benefits most from acupuncture.
Hangzhou, ZhejiangAges 18–60 - NCT06378619Recruiting
Using a tap test and spiral drawing to diagnose tremors
This study uses a simple tapping test and drawing a spiral (like on paper) to tell the difference between tremors caused by Parkinson's disease and essential tremor. It may help you get a more accurate diagnosis without extra procedures.
Barcelona, CataloniaAges 45–79
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Common questions
- Are there clinical trials for machine learning?
- Yes. Clin2 currently lists 90 recruiting machine learning studies from the U.S. registry, each rewritten for real people, not researchers, so you can see what it’s testing and who it’s for.
- How do I know if I qualify for a machine learning trial?
- Each study lists its eligibility criteria — rules about age, diagnosis, and prior treatments. On every Clin2 trial page we explain these in words written for real people and offer a short, optional pre-screen for a fit read. The study team makes the final decision.
- Does it cost anything to join a machine learning trial?
- Using Clin2 is always free. Many trials cover the cost of the study treatment and related visits; some reimburse travel. The study team explains exactly what’s covered before you decide.
Related conditions
Clin2 helps you find and understand clinical trials and does not provide medical advice. Study data comes from ClinicalTrials.gov. Talk with your doctor about whether a specific trial is right for you.