大型语言模型在病理学中的应用
1Department of Pathology, University of Michigan, Ann Arbor, MI 48105, USA.
Bioengineering (Basel, Switzerland)
|April 27, 2024
概括
大型语言模型 (LLM) 通过协助像图像解释和数据提取等任务,在病理学中提供了变革性的潜力. 然而,由于存在错误和过度依赖的风险,仔细整合和验证至关重要.
科学领域:
- 人工智能的人工智能
- 计算病理学计算病理学
- 机器学习 机器学习
背景情况:
- 大型语言模型 (LLM) 是先进的神经网络,能够生成类似人类的文本.
- 这些模型正在越来越多地被探索用于各种科学领域的应用,包括医学.
研究的目的:
- 提供大型语言模型的历史概述.
- 突出LLMs在病理学实践和教育中的当前和潜在使用案例.
- 讨论将LLM整合到病理学工作流程中的好处和局限性.
主要方法:
- 对大型语言模型的现有文献的审查.
- 基于变压器的神经网络架构的分析.
- 探索病理学领域的当前和新兴应用.
主要成果:
- LLM可以生成教育内容,总结文本,提取数据和编写代码.
- 与视觉模型相结合,LLMs可以帮助组织病理学图像解释.
- 潜在的应用包括协助案件签约和报告生成.
结论:
- 大型语言模型对彻底改变病理学实践和教育具有重大前景.
- 用户必须谨慎使用,因为LLM可以产生不准确性和幻觉.
- 对人工智能产生的内容的验证和有意识的整合对于减轻技能削减和自动化偏见等风险至关重要.
关键词:
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