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This study reveals that a guideline-anchored retrieval-augmented generation (RAG) large language model configuration significantly surpasses both a baseline GPT-5 and a literature-anchored AI in gynecologic oncology decision support. Key outcomes emphasize the importance of integrating trusted guideline repositories like NCCN to enhance the accuracy and reduce hallucinations in AI-generated clinical recommendations.
Inter-rater reliability was moderate for the primary evaluation metric, supporting the reproducibility of the findings. This benchmarking work precedes clinical integration, underscoring performance differences rather than clinical safety or patient outcome improvements.
