由大型语言模型生成的合成肺癌队列:流行病学有效性评估
Álvaro Fuentes-Martín1, Julio Mayol2, Bárbara Segura Méndez3
1Servicio de Cirugía Torácica, Hospital Clínico Universitario de Valladolid, Universidad de Valladolid, Spain.
Open respiratory archives
|February 2, 2026
概括
大型语言模型可以生成合成肺癌患者队列. 这些人工智能生成的数据集虽然对教育有用,但与现实数据相比,它们显示出流行病学偏差.
科学领域:
- 医疗信息学 医疗信息学
- 在瘤学瘤学.
- 人工智能的人工智能
背景情况:
- 大型语言模型 (LLM) 是医学中的新兴工具,特别是用于临床推理和教育模拟.
- 评估人工智能生成的医疗数据的有效性对于可靠的应用至关重要.
研究的目的:
- 评估使用ChatGPT-4.0.0生成的合成肺癌队列的流行病学可信性.
- 识别和量化人工智能生成的流行病学数据中的潜在偏差.
主要方法:
- 在西班牙语中生成了102例合成肺癌病例,使用具有人口,组织学和分子数据的结构化提示.
- 将合成队列与国际数据集 (GLOBOCAN 2020,SEER) 和元分析进行了描述性比较.
主要成果:
- 合成队列反映了一般的肺癌模式,但呈现出统计学上显著的偏差 (p < 0.05).
- 在AI队列中,早期疾病和EGFR阳性瘤的比例过高.
- 晚期阶段,ALK重组和极端PD-L1表达不足.
结论:
- 人工智能生成的队列显示生成偏见,可能是由于训练数据和模型算法.
- 尽管存在偏见,但如果保持方法透明度,合成队列有可能用于教育模拟.
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