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Evaluation of six large language models for study identification in an obstetric systematic review
0
Zitationen
6
Autoren
2026
Jahr
Abstract
Current general-purpose LLMs are unreliable for autonomous study identification in clinically relevant systematic reviews and human oversight remains essential. The low F1 scores highlight major limitations in current LLMs' ability to accurately and comprehensively identify relevant studies. These findings underscore the need for fine-tuning and hybrid AI-human workflows before safe integration into evidence synthesis in obstetrics and gynecology.
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