公共可用的大型语言模型的演变,用于乳腺癌护理中的复杂决策
Sebastian Griewing1,2,3, Johannes Knitza4, Jelena Boekhoff5
1Institute for Digital Medicine, Philipps-University Marburg, Marburg, Germany. s.griewing@uni-marburg.de.
Archives of gynecology and obstetrics
|May 28, 2024
概括
大型语言模型 (LLM) 显示乳腺癌治疗建议的准确性各不相同,GPT4表现最好,但尚未准备好临床使用. 为了在患者护理中安全应用,需要进一步的研究和技术进步.
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:
- 多学科瘤委员会 (MTB) 对于复杂的乳腺癌治疗决策至关重要.
- 大型语言模型 (LLM) 是新兴的工具,在医疗保健中具有潜在的应用.
研究的目的:
- 评估领先的LLM与复杂乳腺癌病例的MTB建议的一致性.
- 评估不同的LLM版本的性能,并确定需要改进的领域.
主要方法:
- 五位LLM (GPT4,两种GPT3.5版本,Llama2,Bard) 得到了20个复杂的乳腺癌患者个人资料.
- 将LLM治疗建议与MTB决定进行手术,内分泌,全身,放射治疗和基因检测进行了比较.
主要成果:
- GPT4显示了最高的一致性 (侵入性为70.6%,所有病例为60.0%),其次是GPT3.5版本,Llama2和Bard.
- GPT4在放射治疗建议方面达到了100%的一致性,而遗传测试建议在所有LLM中显示出最低的一致性.
结论:
- 目前的LLM在乳腺癌治疗建议中显示出可变的准确性,其中GPT4显示出最有前途.
- 在LLM能够安全可靠地整合到临床乳腺癌护理中之前,方法和技术进步至关重要.
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