从ChatGPT-4到ChatGPT-5:在儿科瘤病理学和教育中不断发展的应用
Mai He1, Jinyi Weng1
1Department of Pathology & Immunology, Washington University in St. Louis School of Medicine, St. Louis, MO, USA.
Fetal and pediatric pathology
|December 30, 2025
概括
大型语言模型在儿科病理学中表现有前途. 聊天GPT-4o实现了77.1%的诊断一致性,超过了GPT-5,但专家监督至关重要.
科学领域:
- 病理学 病理学 病理学
- 人工智能的人工智能
- 在瘤学瘤学.
背景情况:
- 大型语言模型 (LLM) 正在成为医学诊断中的有价值的工具.
- 它们在儿童瘤病理学等专业领域的应用需要彻底评估.
研究的目的:
- 评估高级LLM,特别是ChatGPT-4o和GPT-5在儿科瘤病理学中的诊断效用.
- 评估LLM在生成诊断建议,免疫组织化学面板和病理学报告方面的表现.
主要方法:
- 对33例儿科瘤病例的回顾性分析.
- 将临床数据和组织学图像输入到ChatGPT-4o和GPT-5.
- 为诊断一致性和免疫组织化学解释准确性等级LLM输出.
主要成果:
- 聊天GPT-4o实现了77.1%的一致性,显著超过了GPT-5 (57.6%) (p <0.05).
- 这两种模型在小圆蓝细胞瘤中都显示出最高的准确性.
- 聊天GPT-4o表现出优异的免疫组织化学解释准确度 (74.4%对比60.7%).
结论:
- 像ChatGPT这样的LLM显示出在儿科病理学中作为诊断和教育辅助工具的潜力.
- 临床整合需要进一步的验证和必要的专家监督.
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