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基于生成性大语言模型的临床瘤学临床决策能力评估的探索

Li Zhao¹, Chunyan Yang¹, Chunhui Chen¹

¹Department of Oncology, Beijing Anzhen Nanchong Hospital of Capital Medical University, Nanchong Central Hospital, Nanchong, 637200, China.

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology

|November 13, 2025

概括

像DeepSeek R1和ChatGPT-4这样的生成AI模型显示了与人类临床医生相比较的瘤学临床决策支持 (CDS). 虽然有效,但人工智能需要进一步开发复杂的病例,不能取代医生的专业知识.

科学领域:

人工智能在医学中的应用
临床决策支持系统临床决策支持系统
在瘤学瘤学.

背景情况:

大型语言模型 (LLM) 和生成AI为医疗保健优化提供了潜力.
目前用于临床决策支持 (CDS) 的AI模型通常依赖于标准化准则,限制复杂,高风险场景的验证.
这项研究评估了DeepSeek R1和ChatGPT-4的现实世界瘤CDS疗效.

研究的目的:

评估DeepSeek R1和ChatGPT-4用于瘤学临床决策支持的准确性和适应性.
将这些生成性AI模型的性能与真实世界瘤学环境中的人类临床医生的性能进行比较.

主要方法:

基于真实瘤病例开发了一个时间序列,链式结构的临床问题集.
来自DeepSeek R1和ChatGPT-4的响应由四位瘤专家生成并盲目评估其准确性和可行性.
使用Prism GraphPad 10.0.0.进行统计分析和可视化.

主要成果:

无论是DeepSeek R1还是ChatGPT-4,都显示出有能力的瘤CDS性能,与人类临床医生相比.
临床决策质量评估尺度显示,这两种AI模型的性能都强.
在医学人文护理方面,DeepSeek R1表现出色,而ChatGPT-4显示出卓越的可读性;在知识准确性,测试理性或药物标准化方面没有发现显著差异.

结论:

生成型人工智能模型 (DeepSeek R1,ChatGPT-4) 显示了全面的瘤CDS功能,表明了临床实用性.
在复杂的病例中,人工智能的可靠性需要提高,还不能取代医生的专业知识.
未来的研究应该侧重于多式联网知识整合和伦理监督,以提高人工智能在诊断和人文护理中的作用.

关键词:

人工智能的人工智能是人工智能.临床决策支持临床决策支持诊断和治疗诊断和治疗大型语言模型.在瘤学瘤学.

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