大型语言模型作为瘤学的决策工具:比较人工智能建议和专家建议
Loic Ah-Thiane1, Pierre-Etienne Heudel2, Mario Campone3
1Department of Radiotherapy, ICO Rene Gauducheau, Saint-Herblain, France.
JCO clinical cancer informatics
|March 20, 2025
概括
像Claude3-Opus和GPT4-Turbo这样的大型语言模型 (LLM) 在建议早期乳腺癌治疗方面显示出高准确性. 虽然LLM对于内分泌和向疗法有效,但对于放射治疗和基因组测试建议需要进一步改进.
科学领域:
- 在瘤学瘤学.
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 早期乳腺癌 (BC) 治疗决策是复杂的,并从多学科团队会议 (MDTs) 中获益.
- 大型语言模型 (LLM) 是新兴的工具,在医疗保健决策支持中具有潜在的应用.
研究的目的:
- 评估全科医生LLM在推早期乳腺癌患者适当治疗选择方面的准确性.
- 将不同LLM的性能与专家瘤学家的决定进行比较.
主要方法:
- 一项回顾性研究分析了2024年1月至4月早期BC患者的匿名医疗记录.
- 三个LLM (Claude3-Opus,GPT4-Turbo,LLaMa3-70B) 产生了治疗建议,与MDT的专家决定进行了比较.
- 主要结果:适当的LLM建议率;次要结果:LLM F1得分和治疗类别的特异性.
主要成果:
- 克劳德3-Opus (86.6%) 和GPT4-Turbo (85.7%) 在治疗建议中表现出很高的准确性,超过LLaMa3-70B (75.0%).
- 在建议辅助性内分泌和向治疗方面,LLMs表现出色.
- 过度估计辅助放射治疗和在化疗和基因组测试中的可变性表现.
结论:
- 克劳德3-Opus和GPT4-Turbo在协助早期BC治疗建议方面显示出显著的希望.
- 法律法规有潜力提高MDT的决策,特别是在辅助疗法.
- 进一步的前性研究和LLM微调是必要的,以确认临床实用性,特别是手术验证和基因组测试.
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