医疗保健中的大型语言模型:对医学问答数据集的系统评估
Khuzaima Tofeeq1, Asma Naseer1, Aamir Wali1
1FAST School of Computing, National University of Computer and Emerging Sciences, Lahore, Pakistan.
Health information science and systems
|November 24, 2025
概括
大型语言模型 (LLM) 在医疗保健方面表现有前途,大型模型通常在医学问题上表现更好. 解决诸如偏见和幻觉等挑战是他们负责任整合的关键.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 大型语言模型 (LLM) 正在越来越多地探索其在各种科学领域的潜力.
- 由于医学知识和患者数据的复杂性,医疗和医疗保健部门为LLM应用提出了独特的挑战和机会.
研究的目的:
- 系统地评估最先进的LLM在医疗问答任务上的表现.
- 在基准医疗数据集中比较各种LLM,包括GPT-4和Med-PaLM-2的准确性.
- 探索在医疗保健中部署LLM的更广泛影响和挑战.
主要方法:
- 使用的基准医疗问题答案数据集:PubMedQA,MedQA和MedMCQA.
- 评估了一系列的LLM,其规模和领域特定的微调不同.
- 基于准确性,模型大小和数据集特征分析模型性能.
主要成果:
- 像OpenBioLLM-70B和Med-PaLM-2这样的大型模型通常表现出优异的性能.
- 较小的模型,如BioMistral-7B,在特定数据集上显示了竞争性结果,表明了资源有限的设置的潜力.
- 数据集的性能各不相同,突出显示了模型规模和微调的影响.
结论:
- 在提高医疗保健效率,患者治疗成果和医学研究方面,LLM具有显著的潜力.
- 对于负责任的采用,必须解决包括可解释性,数据安全性,偏见和幻觉在内的关键挑战.
- 持续创新和专注于以患者为中心,公平的部署对于实现LLM在医疗保健中的全部好处至关重要.
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