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237P Leveraging AI-assisted curation to streamline evidence extraction from pivotal NSCLC trials
0
Zitationen
9
Autoren
2025
Jahr
Abstract
Timely access to clinical trial evidence is essential for oncology research and health technology assessment. Manual retrieval and extraction of trial outcomes and baseline characteristics is resource-intensive and prone to inconsistencies, particularly across multiple targeted therapies in non-small cell lung cancer (NSCLC). Artificial intelligence (AI) tools, including large language models, offer the potential to accelerate literature review and data structuring while maintaining accuracy.
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