儿科罕见病:大型语言模型可以帮助非标签处方吗?
Anna Flamigni1, Giulia Zamagni1,2, Gilda Paternuosto1
1Institute for Maternal and Child Health (IRCCS Burlo Garofolo), Trieste, Italy.
British journal of clinical pharmacology
|July 16, 2025
概括
大型语言模型 (LLM) 可以检索用于儿科罕见病处方的生物医学数据,但性能不同. GPT-4o显示得分高于Scopus AI,尽管人类监督对准确性至关重要.
科学领域:
- 生物医学信息学 生物医学信息学
- 人工智能在医学中的应用
- 儿科药理学 儿科药理学
背景情况:
- 非标签药物处方在儿科罕见病中很常见.
- 获得可靠的生物医学信息对于安全有效的处方至关重要.
- 大型语言模型 (LLM) 提供了信息检索和合成的潜力.
研究的目的:
- 评估LLM在获取和合成用于非标签儿科罕见病处方的生物医学信息方面的有效性和可靠性.
- 将LLM的表现与人类撰写的关于科学理由,不良事件和药物相互作用的参考资料进行比较.
主要方法:
- 四个LLM (GPT-4o,Sophos-2,Claude-3,Scopus AI) 用于审查20个非标签的儿科罕见疾病病例.
- 查询的重点是科学理由,不良事件和药物相互作用.
- 使用灵敏度,精度,准确度,F1评分,响应质量和参考质量来衡量性能,这些都被整合到全球性能评分中.
主要成果:
- 在四个LLM (P=.001) 中观察到全球绩效分数的显著差异.
- 与Scopus AI相比,GPT-4o表现出更高的性能.
- 在LLM产生的参考质量往往超过人类的性能,但变化性需要谨慎解释.
结论:
- LLM有效地检索和合成生物医学信息,性能因查询和搜索模式而异.
- 这些人工智能工具可以加快对非标签处方适当性的评估.
- 人类监督对于保证人工智能生成信息的准确性和可靠性至关重要.
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