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100P Systematic review of large language models in clinical trial matching automation
0
Zitationen
9
Autoren
2025
Jahr
Abstract
Clinical trial enrollment remains low (<5% of adults), with ∼20% of trials terminated for poor accrual. Manual eligibility screening is labor-intensive and error-prone. Earlier AI (rule-based, traditional neural nets, conventional NLP) improved efficiency but struggled with nuanced, compositional criteria. Recently, large language models (LLMs) have accelerated tools that parse complex eligibility logic and fuse structured/unstructured EHR data, delivering superior performance versus prior approaches.
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