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解码AI能力:在卵巢癌诊断和治疗中对大型语言模型 (LLM) 进行基准测试 - - 对生成AI准确性和完整性的系统评估

Haojie Cai¹, Chao Wang¹, Yue Zhang¹

¹Department of Gynecology, Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai 200092, China.

Diagnostics (Basel, Switzerland)

|February 27, 2026

概括

在解决卵巢癌诊断和治疗问题方面,DeepSeek-R1表现优于Doubao-1.5-pro,尽管这两种大型语言模型 (LLM) 需要进一步改进以获得临床应用.

科学领域:

人工智能在医学中的应用
在瘤学瘤学.
自然语言处理自然语言处理.

背景情况:

大型语言模型 (LLM) 越来越多地被用于医学应用.
在妇科瘤学等专业领域评估LLM能力至关重要.
卵巢癌的管理需要准确和及时的诊断和治疗信息.

研究的目的:

评估DeepSeek-R1和Doubao-1.5-pro在卵巢癌管理中的实际价值.
为了比较这两个法学士的诊断和治疗相关能力.

主要方法:

二十个关键的卵巢癌诊断和治疗问题被制定成问题.
两个LLM,DeepSeek-R1和Doubao-1.5-pro,回答了这些问题.
妇科瘤学专家在1-10的尺度上评估了LLM的完整性和准确性.

主要成果:

与Dubao-1.5-pro.相比,DeepSeek-R1获得了明显更高的"优秀"评分 (98比41) 和平均分数.
在所有评估领域中,DeepSeek-R1表现出卓越的性能,特别是在"医疗"方面.
统计测试证实了显著的差异,其中DeepSeek-R1的表现优于Doubao-1.5-pro.

结论:

在卵巢癌管理方面,DeepSeek-R1显示出前景,但表现出包括不准确和过时信息在内的局限性.
法律法规可以作为医学教育和辅助诊断的有价值的工具,但需要不断更新.
精心选择和改进LLM对于提高其临床实用性和准确性至关重要.

关键词:

在DeepSeek搜索中深入搜索.杜巴巴奥 (Doubaobao) 是一个多巴奥.人工智能是一种人工智能.大型语言模型.卵巢癌是发生在卵巢的癌症.

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