在个性化瘤学中利用大型语言模型进行决策支持
Manuela Benary1,2, Xing David Wang3, Max Schmidt1,4
1Charité Comprehensive Cancer Center, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
大型语言模型 (LLM) 通过建议有用的治疗理念,显示了精确瘤学的潜力,尽管它们尚未与专家医生的质量相匹配. 进一步开发可以加强它们在基于证据的癌症护理中的作用.
科学领域:
- 在瘤学瘤学.
- 生物医学信息学 生物医学信息学
- 人工智能的人工智能
背景情况:
- 精确瘤学依赖于复杂生物标志物的手动解释.
- 大型语言模型 (LLM) 为自动化临床决策支持提供了潜力.
研究的目的:
- 评估四个LLM作为精密瘤学的支持工具的性能.
- 定义LLMs在识别个性化癌症治疗选择中的作用.
主要方法:
- 一项涉及10个虚构的先进癌症病例的诊断研究.
- 四位LLM (ChatGPT,Galactica,Perplexity,BioMedLM) 和一位专家医生提供了治疗方案.
- 分子瘤委员会评估了LLM产生的可识别性和临床有用性的选择.
主要成果:
- 与人类专家相比,LLM产生了更多的治疗选择,但精度和回忆率较低 (F1分数为0.04-0.19).
- 结合的LLM输出提高了性能 (F1得分为0.29).
- 可识别的LLM选项是人工智能生成的,通常是由于缺乏证据,但至少有一个LLM选项对每个案例都很有帮助,一些独特的有用选项仅由LLMs识别出来.
结论:
- 精密瘤学LLM产生的治疗选择目前缺乏人类专家的质量和信誉.
- 法律法学可以提供有用的,互补的想法,并协助对基于证据的个性化癌症治疗进行文献选.
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