Key Highlights

Risk & Performance

Pipeline Risk Assessment

Pipeline Risk Assessment

Based on historical performance

Moderate Risk

Score: 45/100

Failure Rate

0.0%

0 terminated/withdrawn out of 34 trials

Success Rate

100.0%

+13.5% vs industry average

Late-Stage Pipeline

0%

0 trials in Phase 3/4

Results Transparency

0%

0 of 20 completed trials have results

Key Signals

4 recruiting

Enrollment Performance

Analytics

N/A
27(100.0%)
27Total
N/A(27)

Activity Timeline

Global Presence

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Clinical Trials (34)

Showing 20 of 34 trials
NCT07402668Not ApplicableRecruiting

Does AI Make Clinicians More Appropriately Confident? A Randomized Study in Preterm Birth Prediction

Role: collaborator

NCT07476638Not ApplicableNot Yet Recruiting

Impact of AI Feedback on Ultrasound Biometry Accuracy Across the Expertise Levels

Role: lead

NCT06842394Not ApplicableCompleted

Can Increased Medical Competence Reduce State Anxiety in Junior Doctors in the Emergency Department?

Role: lead

NCT07468357Not ApplicableNot Yet Recruiting

Human-AI Uncertainty Callibration for Improved Skin Lesion Segmentation

Role: lead

NCT06612619Not ApplicableRecruiting

Is Two-on-one Instruction in Virtual Reality Simulation-based Training of Operating Fractures of the Hip for Medical Students as Effective as One-on-one Instruction

Role: lead

NCT07401368Not ApplicableNot Yet Recruiting

Clinicians' Trust in AI-Based Fetal Growth Estimates

Role: collaborator

NCT06729372Not ApplicableCompleted

Optimizing Simulation-Based Training in Orthopedics: Exploring Deliberate Flawed Performance for Dynamic Hip Screw Osteosynthesis

Role: lead

NCT06314178Completed

Assessing Demographic Biases in Deep Learning Model for Fetal Growth Estimation in Clinical Practice. Patients Eligible for Inclusion Are Women with a Gestational Age Between 24-42 Weeks Undergoing a Third-trimester Growth Scan. the Image Data from the Scan Are Used to Calculate Fetal Weight.

Role: lead

NCT06566014Not ApplicableCompleted

The Study Aims to Improve the Accuracy of Detecting Spina Bifida During Early Ultrasound Scans. to Achieve This, an AI Model Has Been Developed to Provide Feedback About the Presence of Spina Bifida. a RCT Has Been Designed to Compare the Effectiveness of AI Feedback with No AI Feedback.

Role: lead

NCT05884645Recruiting

The DAnish VIdeo IntubaTION (DA-VITION) Study

Role: collaborator

NCT06232187Not ApplicableEnrolling By Invitation

AI Support in Novice's Decision-making for Ultrasound Fetal Weight Estimation

Role: lead

NCT06268392Not Yet Recruiting

A Comparative Study of AI Methods for Fetal Diagnostic Accuracy in Ultrasound

Role: lead

NCT06255080Not ApplicableCompleted

Comparing Skills Acquisition on Different Laparoscopy Software

Role: lead

NCT05834504Not ApplicableCompleted

Exploring the Intervals in Distributed Laparoscopic Skills Training

Role: lead

NCT05736627Not ApplicableRecruiting

Learning Effect of Onsite vs. Online Education in a Medical Context

Role: collaborator

NCT05731674Not ApplicableCompleted

Varied Practice on LAPSIM

Role: lead

NCT05607485Not ApplicableCompleted

Laypersons Cannot Select Preferred Surgeon Based on Videos of Simulated Robot-assisted Radical Prostatectomies

Role: lead

NCT05078762Not ApplicableCompleted

Immersive Virtual Reality in Simulation-based Bronchoscopy Training

Role: lead

NCT05191589Not ApplicableCompleted

Haptic Devices Impact on Laparoscopic Simulators

Role: lead

NCT03864302Not ApplicableUnknown

Simulation in Transurethral Bladder Cancer Surgery

Role: collaborator