大型语言模型在产生病理图像中的临床应用
Lingxuan Zhu1,2, Yancheng Lai3, Na Ta4
1Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
JCO clinical cancer informatics
|July 2, 2025
概括
人工智能 (AI) 模型 DALL·E 3 在生成合成前列腺癌 (PCa) 病理图像的教育方面表现有前途. 虽然对教学有价值,但细节上的限制需要仔细的伦理整合到病理学实践中.
科学领域:
- 数字病理学数字病理学
- 人工智能在医学中的应用
- 医疗教育 技术 技术 医学教育
背景情况:
- 前列腺癌 (PCa) 诊断依赖于准确的组织病理学.
- 产生多样化和代表性的培训数据集对于医学教育至关重要.
- 目前用于病理学培训的资源可能缺乏足够多样化的格里森等级.
研究的目的:
- 评估DALL·E 3在合成前列腺癌 (PCa) 图像创建的能力.
- 评估不同格里森等级的AI生成图像的现实性和准确性.
- 探索合成图像的实用性,以提高病理学教育和研究.
主要方法:
- DALL·E 3被用来生成30个合成PCa图像,这些图像来自各种格里森等级.
- 图像是根据标准的格里森模式描述创建的.
- 九位泌尿病学家评估了图像的真实性和准确性,与实际的H&E染色幻灯片对比.
主要成果:
- 人工智能生成的图像获得了平均现实性和代表性分数分别为6.04和6.17.
- 在格里森模式 (P < .05) 中观察到得分的显著变化.
- 格里森5图像得分最高,准确地反映了关键的病理特征,尽管精细的核细节有限.
结论:
- DALL·E 3展示了生成定制病理图像的潜力,以扩大教育资源.
- 伦理考虑,包括数据伪造风险,需要负责任的AI实施.
- 人工智能开发人员和病理学家之间的合作对于伦理整合到病理学中至关重要.
相关概念视频
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