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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 Lu^1,2,3,4, Bowen Chen^1,2, Drew F K Williamson^1,2,3

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

|June 12, 2024

概括

这是一个新的AI助理, 整合视觉和语言功能. 与其他人工智能模型相比,它在诊断问题上表现出卓越的表现,并提供更准确,更喜欢的答案.

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.

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