当人工智能加入桌面时:评估软组织肉瘤瘤委员会决定中的大型语言模型性能
Reza Dehdab1, Saif Afat1, Fiona Mankertz1
1Department of Radiology, Tübingen University Hospital, University of Tübingen, Tübingen, Germany.
Journal of cancer research and clinical oncology
|February 27, 2026
概括
像ChatGPT-4o这样的大型语言模型在协助软组织肉瘤 (STS) 治疗建议的多学科瘤委员会方面表现有希望,特别是在临床上下文化. 然而,专家监督对于治疗顺序和化疗选择至关重要.
科学领域:
- 在瘤学瘤学.
- 人工智能在医学中的应用
- 医疗信息学 医疗信息学
背景情况:
- 多学科瘤板 (MDTs) 对于个性化的软组织肉瘤 (STS) 管理至关重要.
- 目前的MDT面临着包括时间,成本和资源限制在内的限制.
- 大型语言模型 (LLM) 为增强MDT工作流提供了一个潜在的途径.
研究的目的:
- 评估ChatGPT-4o在为现实世界STS病例生成治疗建议方面的临床性能.
- 用预定义的标准将ChatGPT-4o的建议与MDT专家的决定进行比较.
- 评估跨癌症护理和肉瘤亚型不同领域的LLM绩效.
主要方法:
- 追溯分析了152名STS患者的匿名注册信件.
- 聊天GPT-4o生成了基于指南的治疗建议.
- 专家审查员对源头盲目评分了五个领域的输出:诊断,治疗,测序/定时,化疗和临床上下文化.
主要成果:
- 在所有评估标准中,ChatGPT-4o的绩效得分明显低于最大值 (p < 0.0001).
- 与其他领域相比,临床上下文化领域的得分明显高 (p < 0.05).
- 不同类型的肉瘤亚型之间没有发现显著的性能差异 (p = 0.138).
结论:
- 在生成STS瘤委员会建议方面,ChatGPT-4o表现出了相当大的专家评级性能,特别是在临床上.
- 需要改进的领域包括治疗顺序和化疗选择,强调需要专家的人类监督.
- 这些发现支持将LLM整合到瘤学工作流程中,需要进一步开发以确保安全的临床应用.
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