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Evaluation reveals limitations in large language models for study identification in obstetrics, implying a need for better AI-human workflows.
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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Somer et al. (2026) studied this question.
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