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AI Clinical Trial Matching: NLP Parsing Accuracy & Diversity Enrollment Solutions - Technological Advancements

Former Advisory Committee Chair
UnitedHealthcare
Clinical Research
UK
Europe

$ 795

June 18, 2025

Topics covered in this transcript

  • How does NLP accuracy compare to manual chart reviews?
  • What bias mitigation strategies improve diverse enrollment?
  • How are multilingual systems expanding access?
  • What EHR integration challenges persist?
  • How do AI tools handle complex exclusion criteria?
  • What site selection algorithms optimize diversity?
  • How are real-world data lakes training matching models?
  • What regulatory standards validate AI suggestions?
  • How do patient outreach strategies integrate with AI?
  • What metrics track matching efficiency gains?
  • How are community health workers using AI tools?
  • What lessons come from oncology trial matching?

Keywords

  • NLP Parsing
  • Patient Matching
  • DiversityEnrollment
  • EHR Integration
  • Bias Mitigation
  • Multilingual
  • ProtocolComplexity
  • Site Selection
  • Real-World Data
  • RegulatoryAcceptance
  • Patient Outreach
  • PerformanceMetrics

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Get in touch with us to learn more about our services, ask for assistance with a technical difficulty, or if you would like a product demo.
info@nextyn.com
Singapore
68 Circular Road, #02-01
049422, Singapore
Jakarta

Revenue Tower, Scbd, Jakarta 12190, Indonesia
Mumbai
4th Floor, Pinnacle Business Park, Andheri East, Mumbai, 400093
Bangalore

Cinnabar Hills, Embassy Golf Links Business Park, Bengaluru, Karnataka 560071
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