A large language model-based AI system deployed at Cleveland Clinic effectively identified diverse, eligible participants for a rare disease clinical trial through EMR screening.
Key Details
- 1Cleveland Clinic and Dyania Health used an AI system (Synapsis AI) to pre-screen for a Phase 3 trial in transthyretin amyloid cardiomyopathy (ATTR-CM).
- 2In one week, the AI reviewed 1,476 records and identified 46 potential matches; 29/30 AI-identified matches were missed by traditional methods.
- 3The AI achieved 96.2% accuracy for 7,700 trial-specific questions and a 99% negative predictive value (NPV) in excluding ineligible patients.
- 4AI-assisted recruitment led to a more diverse patient cohort, with 36.6% Black participants versus 7.1% via routine screening.
- 5Only 60% of AI-identified patients had heart failure specialist care, versus 92.8% found by traditional methods, showing improved outreach.
- 6The system used both structured EMR data and NLP to analyze clinical notes and provided auditable inclusion/exclusion justifications.
Why It Matters
This study showcases the potential of AI-driven EMR review to increase trial enrollment efficiency and address disparities by identifying qualified participants who might otherwise be overlooked, particularly in underrepresented groups. It underlines how imaging and clinical AI advancements are intersecting with research recruitment and population health strategies.
Source
EurekAlert
Related News
•EurekAlert
FDA Approves Johns Hopkins AI Tool for Early Sepsis Detection
FDA clears an AI-driven system developed by Johns Hopkins to detect sepsis up to 48 hours earlier and reduce mortality rates.
•EurekAlert
New AI Vision-Language Model Enhances Chest CT Diagnostics
Researchers developed an interpretable AI model that uses visual question answering to generate detailed diagnostic findings from chest CT scans, aimed at improving lung cancer diagnosis.
•EurekAlert
Optical AI Chip Boosts Real-Time Dry Eye Gland Diagnosis Accuracy
A new metasurface spectral AI chip enables rapid, accurate diagnosis of meibomian gland dysfunction (MGD) from tissue samples, achieving 96.22% accuracy.