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
- NCT07611838Recruiting
Predicting ABPA Recurrence Using Blood and Immune Markers
This study helps doctors predict whether allergic bronchopulmonary aspergillosis (ABPA)—a serious lung condition caused by fungal allergy—will come back after treatment. Researchers will analyze blood samples and immune system markers to identify patients at higher risk of relapse, which could improve how doctors monitor and treat you.
Jinan, ShandongAges 18–80 - NCT07620119Recruiting
AI-assisted heart attack diagnosis in patients with LBBB
This study tests whether artificial intelligence can help doctors more accurately diagnose heart attacks in patients who have a specific heart rhythm pattern called Left Bundle Branch Block (LBBB). The goal is to improve emergency care by reducing delays in identifying who needs urgent treatment.
Konya, KaratayAges 18 years+ - NCT07558785Enrolling by invitation
Testing speech and memory checks at home for early thinking problems
This study uses a video-based system at home to check your speech and memory, helping doctors spot thinking problems early using artificial intelligence. The goal is to see if this simple, at-home test can reliably catch early memory and thinking changes.
PilsenAges 40 years+ - NCT07285434Enrolling by invitationEarly Phase 1
Personalized vaccine for first-line advanced lung cancer
This study tests a personalized vaccine (Microlyvaq™) made just for your tumor, given together with standard chemo and immunotherapy (pembrolizumab). It is for people with advanced non-small cell lung cancer who have not yet started treatment for the advanced stage.
Thessaloniki, MacedoniaAges Any age - NCT07096232Enrolling by invitation
AI vs doctor for eye surgery and keratoconus diagnosis
This study compares an AI system to a specialist eye doctor to see which is better at diagnosing vision problems like nearsightedness, farsightedness, astigmatism, and keratoconus. Your existing medical records will be used to see how well the AI performs.
Cairo, MaadiAges Any age - NCT07047937Enrolling by invitation
Machine learning to predict early gastric cancer
This study uses machine learning to find patterns in patient data that might help predict early-stage stomach cancer. It could help doctors catch the disease sooner.
Wenzhou, ZhejiangAges Any age - NCT06823024Enrolling by invitation
Using speech patterns to predict treatment response in depression, PTSD, or bipolar disorder
This study tests if a computer algorithm can analyze your speech to predict how well you will respond to treatments like TMS or Spravato. It aims to create a simple tool that helps doctors choose the right treatment for you.
Sunnyvale, CaliforniaAges 18–68 - NCT06682455Enrolling by invitation
Predicting AMD progression with advanced scans and AI
This study is looking at people with intermediate AMD who previously took part in the PINNACLE study. It will use advanced eye scans and machine learning to learn more about how AMD progresses and how to predict it.
ViennaAges Any age - NCT06447532Enrolling by invitation
Machine learning to track inflammation in breast cancer
This study uses a computer program to look at blood test results from women treated for breast cancer. The goal is to see if patterns in inflammation markers can help predict outcomes.
Buenos Aires, Buenos AiresAges 18–75 - NCT06017505Enrolling by invitation
Using eSAGE for early detection of cognitive decline
This study uses a simple computer-based self-assessment (eSAGE) to detect early signs of memory or thinking problems. It may help identify cognitive impairment sooner and track changes over time.
Columbus, OhioAges 50 years+ - NCT05942859Enrolling by invitation
AI study using heart ECGs to spot pulmonary hypertension
This study looks at patterns on a 12-lead ECG (a common heart tracing) to help diagnose pulmonary hypertension, using results from a right heart catheter test. It may help doctors use ECGs better in the future, without changing your current care.
BathAges 18 years+ - NCT05746247Enrolling by invitation
Helping diagnose and manage familial high cholesterol
This study is testing ways to improve how doctors identify and manage familial hypercholesterolemia (a strong inherited tendency to have very high cholesterol) using computer tools and behavior-focused strategies. It may help patients get earlier, more coordinated care if they are at high risk but not yet officially diagnosed.
Philadelphia, PennsylvaniaAges 18 years+ - NCT05474274Enrolling by invitation
Home pain app that teaches treatment after outpatient plastic surgery
This trial tests a home-based “pain coaching” system (using a phone app and a TENS device) to help people manage pain after lower-risk outpatient plastic surgery. It may help you feel more in control of pain at home using guided exercises and device-based pain relief.
Jacksonville, FloridaAges 18 years+ - NCT06478394Recruiting
Blood test for hidden stomach cancer spread
This study tests a new blood test that uses machine learning to find hidden stomach cancer spread (peritoneal metastases) early. It could help people with locally advanced stomach cancer avoid unnecessary surgery.
Shijiazhuang, HebeiAges 18 years+ - NCT07396636Recruiting
Using machine learning to manage low blood pressure during surgery
This study tests whether an AI tool can help doctors better manage low blood pressure during planned surgeries. If you are having an elective surgery with general anesthesia and need an arterial line for monitoring, this trial may be for you.
Konya, Konya/MeramAges 18 years+ - NCT05652361Recruiting
AI guidance system for robotic rectal cancer surgery
This trial tests a machine-learning (AI) system that may help guide surgeons during robotic rectal cancer operations. It’s meant to support safer, more accurate surgery while comparing outcomes to standard surgical guidance.
Dresden, SaxonyAges 18 years+ - NCT06350201Recruiting
Online CBT Based on Symptom Groups
This study uses a computer program to group symptoms and then offers an online cognitive behavioral therapy (CBT) program. It aims to help people who feel depressed or anxious by tailoring treatment based on their specific symptom patterns.
WuhanAges 18–64 - NCT06427265Recruiting
Recovery after ICU: a long-term study
This study follows people after they leave the ICU to see how they recover over time. It uses computer programs to find patterns and create early warning tools to help doctors spot problems sooner.
Guiyang, GuizhouAges 18–100
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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.