AI-Selected Trial Candidates are More Diverse
Could artificial intelligence-enabled medical chart review improve the speed, accuracy, and equity of study enrollment?
Clinical Scorecard: AI-Selected Trial Candidates are More Diverse
At a Glance
| Category | Detail |
|---|
| Condition | Transthyretin amyloid cardiomyopathy |
| Key Mechanisms | AI-enabled platform automating chart review for clinical trial eligibility |
| Target Population | Patients with amyloid-related diagnostic codes |
| Care Setting | Large health system |
Key Highlights
- AI system processed 1,476 patient records in six days
- Achieved 96.2% accuracy in answering trial-relevant questions
- Identified 30 eligible patients, 37% of whom were Black
- 93% of AI-identified patients were not registered with a cardiologist
- 99% accuracy in excluding ineligible patients
Guideline-Based Recommendations
Diagnosis
- Utilize AI to analyze EMRs for identifying eligible patients
Management
- Implement AI systems to streamline clinical trial recruitment processes
Monitoring & Follow-up
- Regularly assess AI performance against traditional methods
Risks
- Ensure AI systems are validated for accuracy and reliability
Patient & Prescribing Data
Patients with transthyretin amyloid cardiomyopathy
AI may enhance recruitment from under-represented populations
Clinical Best Practices
- Integrate AI systems for efficient patient identification
- Provide traceable explanations for AI decisions
- Validate AI findings with clinician review
References
