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Updated: Jan 13, 2026

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大型语言模型在辅助生殖技术中产生临床推理的可靠性:盲目的比较评估研究

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  • 1Department of Obstetrics and Gynecology, West China Second University Hospital of Sichuan University, Chengdu, China.

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为人工智能在生殖医学中产生可靠的临床思维链 (CoTs) 需要战略性提示. 使用多样化,高质量的示例的选择性少数射击方法,比其他方法显著提高了Cot质量.

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辅助生殖技术是一种辅助生殖技术.一个思想链的思想链.临床数据的可靠性.可解释的人工智能大型语言模型

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科学领域:

  • 医学的人工智能 (AI)
  • 生殖医学 生殖医学
  • 临床决策支持 临床决策支持

背景情况:

  • 高质量的临床思维链 (CoTs) 对于可解释的医学AI至关重要.
  • 目前,数据稀缺性限制了可靠COT的开发.
  • 大型语言模型 (LLM) 生成的CoT的临床可靠性仍然不清楚.

研究的目的:

  • 在生殖医学中评估LLM生成的CoT的临床可靠性.
  • 确定有效的提示策略,以提高CoT质量.

主要方法:

  • 一项盲目的比较研究,涉及高级临床医生在辅助生殖技术.
  • 使用零射击,随机少数射击和选择性少数射击提示策略生成的CoT的评估.
  • 专家临床医生的评分与来自最先进的人工智能模型 (GPT-4o) 的评价的比较.

主要成果:

  • 选择性几次射击策略显著改善了CoT的逻辑清晰度,信息使用和临床准确性 (P<.001).
  • 随机的几次射击提示与零次射击基线相比没有显著的改善.
  • 一个人工智能评估器 (GPT-4o) 无法区分战略之间的关键性能差异,突出了战略快速设计的需要.

结论:

  • 提出了一个"双重原则"框架,结合了"黄金标准深度"和"代表性多样性",以产生值得信赖的CoT.
  • 这项研究解决了生殖医学AI发展中的数据瓶.
  • 人类专业知识对于评估人工智能生成数据的临床可靠性至关重要.