合成瘤病理学数据集的开发,用于医学文本分类中的大型语言模型评估
Werner O Hackl1, Sabrina B Neururer1,2, Stefan Richter1,3
1Division for Digital Health and Telemedicine, UMIT TIROL - Private University for Health Sciences and Health Technology, Hall in Tirol, Austria.
Studies in health technology and informatics
|April 24, 2025
概括
为评估癌症报告分类中的大型语言模型 (LLM) 创建了一个合成瘤病理学数据集. 这种保护隐私的基准可以实现可重现的AI研究,而无需使用真实患者数据.
科学领域:
- 人工智能在病理学中的应用
- 计算病理学计算病理学
- 医疗信息学 医疗信息学
背景情况:
- 大型语言模型 (LLM) 显示了自动化瘤病理学报告分类的潜力.
- 真实患者数据的使用受到隐私,法律和伦理方面的限制.
- 符合隐私的替代方案对于该领域的人工智能研究至关重要.
研究的目的:
- 开发一个合成瘤病理学数据集.
- 在病理学报告分类中建立评估LLM绩效的基准.
- 为了促进可复制和保护隐私的人工智能研究.
主要方法:
- 使用各种LLM (微软Copilot,ChatGPT Plus,Perplexity Pro) 生成了227份合成病理报告.
- 包括前列腺癌,肺癌和乳腺癌病例,平衡恶性和良性发现.
- 通过三家独立的癌症注册机构的分类来验证报告.
主要成果:
- 创建了一个合成瘤病理学报告的结构化数据集.
- 在生成的报告中确保结构和语言的多样性.
- 通过专家注册员实现了基于共识的验证.
结论:
- 合成数据集作为临床相关的LLM评估的基准.
- 在不影响患者隐私的情况下,在病理学文本分类中实现AI模型评估.
- 支持AI在瘤学文档中的可扩展和道德发展.
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