在瘤学中进行文本挖掘的推理模型:在不同推理水平上对o1预览,GPT-4o和GPT-5进行比较
Paul Windisch1,2, Fabio Dennstädt2, Julia Weyrich1,2
1Department of Radiation Oncology, Cantonal Hospital Winterthur, Winterthur, Switzerland.
JCO clinical cancer informatics
|November 6, 2025
概括
新的人工智能模型,包括具有增强推理功能的GPT-5,在瘤文本挖掘方面表现出卓越的表现. 这些先进的语言模型准确地从临床试验摘要中提取患者的资格标准,可能为医学研究制定新的标准.
科学领域:
- 人工智能在医学中的应用
- 自然语言处理自然语言处理.
- 瘤学研究研究
背景情况:
- 连锁思维提示使大语言模型 (LLM) 能够生成中介推理步骤来解决复杂的问题.
- 最近的LLM进步,如OpenAI的o1预览和GPT-5,包含了内部思维链功能,声称在复杂的推理任务上提高了性能.
研究的目的:
- 评估GPT-4o,o1预览和GPT-5在文本挖掘中的性能,特别是在瘤学领域.
- 评估这些LLM在基于摘要的临床试验资格分类中的准确性.
主要方法:
- 来自高影响力医学期刊的600项临床试验的数据集被策划.
- 根据局部和/或转移性疾病患者的纳入,对试验进行了分类.
- GPT-4o,o1预览和GPT-5的任务是使用摘要对试验资格进行分类,GPT-5在不同的推理力度设置下进行测试.
主要成果:
- GPT-4o获得了F1得分,局部疾病为0.80,转移性疾病为0.97.
- o1预览显示了更高的性能,F1得分为局部疾病的0.91和转移性疾病的0.99.
- 通过增加推理力度,GPT-5达到F1得分,局部疾病高达0.94,转移性疾病高达0.99,表现优于其他模型.
结论:
- o1预览在从摘要中准确识别局部和/或转移性疾病患者的试验资格方面超过了GPT-4o.
- GPT-5,特别是在高推理设置下,与GPT-4o和o1预览相比,表现优越.
- 这些发现表明,先进的推理模型有可能成为医学中文本挖掘应用的新标准.
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