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An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...
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一个用于人类病理的多式生成人工智能副驾驶员

Ming Y Lu1,2,3,4, Bowen Chen1,2, Drew F K Williamson1,2,3

  • 1Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.

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科学领域:

  • 计算病理学
  • 医学中的人工智能
  • 视觉语言模型

背景情况:

  • 通过预测模型和自我监督视觉编码器,
  • 生成型人工智能增长超过了用于病理学的通用人工智能助手的发展.

研究的目的:

  • 介绍PathChat, 一种针对人类病理的全新视觉语言通用人工智能助理.
  • 将PathChat的性能与现有的多式人工智能助理和GPT-4V进行评估.

主要方法:

  • 调整了一个基础视觉编码器用于病理学.
  • 结合一个经过预训练的大型语言模型.
  • 在超过456,000个特定病理的视觉语言指令上微调了系统.

主要成果:

  • 在多选项诊断问题上取得了最先进的表现.
  • 人类专家评估显示PathChat对开放式查询提供了更准确和更喜欢的答案.
  • 在病理学相关任务中,PathChat的表现优于其他多式人工智能助理和GPT-4V.

结论:

  • 通过PathChat, 人工智能助理在病理学方面取得了重大进步.
  • 它在处理视觉和自然语言输入方面的能力为教育,研究和临床决策提供了潜在的应用.
  • 通过互动辅助,PathChat可以提高病理学家的表现.