简要介绍一些人工智能在脏病学中的方法
1Department of Pediatrics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA. klemley@chla.usc.edu.
Pediatric nephrology (Berlin, Germany)
|October 28, 2025
概括
本综述解释了脏学中的人工智能 (AI),重点是结神经网络 (CNN) 用于脏图像分析和大型语言模型 (LLM),如ChatGPT用于文本任务. 虽然CNN有助于活检解释和生存预测,但LLM在一致性和环境影响方面存在局限性.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 人工智能 (AI) 越来越多地被用于科研究.
- 两种突出的AI方法是机器视觉 (CNNs) 和大型语言模型 (LLMs).
- 了解它们的应用和局限性对于科医生来说至关重要.
研究的目的:
- 为了提供一个非技术概述的AI方法在科.
- 解释CNN和LLMs的用途和局限性.
- 为研究人员提供有关病研究当前人工智能工具的信息.
主要方法:
- 在科文献中审查人工智能应用.
- 专注于使用卷积神经网络 (CNN) 的机器视觉.
- 分析大型语言模型 (LLM),如ChatGPT.
主要成果:
- 建立CNN是为了细分和分类脏活检图像,预测结果.
- 现有的LLM是较新的,提供自然语言交互,但表现不一致和幻觉.
- 与基于CNN的模型相比,LLM可能具有更高的碳足迹.
结论:
- CNNs为病理和预后的定量分析提供了强大的工具.
- 由于可靠性问题,目前医学LLM聊天机器人需要仔细验证.
- 需要进一步的研究来优化LLM的表现,并评估它们在科的长期影响.
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