Abstract：
OBJECTIVE: We sought to investigate the adoption and perception of large language model (LLM) applications among pathologists.
METHODS: A cross-sectional survey was conducted, gathering data from pathologists on their usage and views concerning LLM tools. The survey, distributed globally through various digital platforms, included quantitative and qualitative questions. Patterns in the respondents\' adoption and perspectives on these artificial intelligence tools were analyzed.
RESULTS: Of 215 respondents, 100 (46.5%) reported using LLMs, particularly ChatGPT (OpenAI), for professional purposes, predominantly for information retrieval, proofreading, academic writing, and drafting pathology reports, highlighting a significant time-saving benefit. Academic pathologists demonstrated a better level of understanding of LLMs than their peers. Although chatbots sometimes provided incorrect general domain information, they were considered moderately proficient concerning pathology-specific knowledge. The technology was mainly used for drafting educational materials and programming tasks. The most sought-after feature in LLMs was their image analysis capabilities. Participants expressed concerns about information accuracy, privacy, and the need for regulatory approval.
CONCLUSIONS: Large language model applications are gaining notable acceptance among pathologists, with nearly half of respondents indicating adoption less than a year after the tools\' introduction to the market. They see the benefits but are also worried about these tools\' reliability, ethical implications, and security.
METHODS: A cross-sectional survey was conducted, gathering data from pathologists on their usage and views concerning LLM tools. The survey, distributed globally through various digital platforms, included quantitative and qualitative questions. Patterns in the respondents\' adoption and perspectives on these artificial intelligence tools were analyzed.
RESULTS: Of 215 respondents, 100 (46.5%) reported using LLMs, particularly ChatGPT (OpenAI), for professional purposes, predominantly for information retrieval, proofreading, academic writing, and drafting pathology reports, highlighting a significant time-saving benefit. Academic pathologists demonstrated a better level of understanding of LLMs than their peers. Although chatbots sometimes provided incorrect general domain information, they were considered moderately proficient concerning pathology-specific knowledge. The technology was mainly used for drafting educational materials and programming tasks. The most sought-after feature in LLMs was their image analysis capabilities. Participants expressed concerns about information accuracy, privacy, and the need for regulatory approval.
CONCLUSIONS: Large language model applications are gaining notable acceptance among pathologists, with nearly half of respondents indicating adoption less than a year after the tools\' introduction to the market. They see the benefits but are also worried about these tools\' reliability, ethical implications, and security.
摘要：
目的：我们试图调查病理学家对大型语言模型（LLM）应用的采用和感知。
方法：进行了横断面调查，从病理学家那里收集关于他们使用的数据和关于LLM工具的观点。调查,通过各种数字平台分布在全球，包括定量和定性问题。分析了受访者采用这些人工智能工具的模式和观点。
结果：在215名受访者中，100（46.5%）使用LLM报告，特别是ChatGPT(OpenAI)，出于专业目的，主要用于信息检索，校对,学术写作,起草病理报告，突出了显著的节省时间的好处。学术病理学家对LLM的理解比同行更好。尽管聊天机器人有时会提供不正确的一般域信息，他们被认为对病理学特定知识具有中等熟练程度。该技术主要用于起草教材和编程任务。LLM中最受欢迎的功能是其图像分析功能。与会者对信息准确性表示担忧，隐私,以及监管部门批准的必要性。
结论：大型语言模型应用程序在病理学家中获得了显着的认可，近一半的受访者表示，在工具引入市场不到一年的时间里就采用了这种工具。他们看到了好处，但也担心这些工具的可靠性，伦理含义，和安全。
方法：进行了横断面调查，从病理学家那里收集关于他们使用的数据和关于LLM工具的观点。调查,通过各种数字平台分布在全球，包括定量和定性问题。分析了受访者采用这些人工智能工具的模式和观点。
结果：在215名受访者中，100（46.5%）使用LLM报告，特别是ChatGPT(OpenAI)，出于专业目的，主要用于信息检索，校对,学术写作,起草病理报告，突出了显著的节省时间的好处。学术病理学家对LLM的理解比同行更好。尽管聊天机器人有时会提供不正确的一般域信息，他们被认为对病理学特定知识具有中等熟练程度。该技术主要用于起草教材和编程任务。LLM中最受欢迎的功能是其图像分析功能。与会者对信息准确性表示担忧，隐私,以及监管部门批准的必要性。
结论：大型语言模型应用程序在病理学家中获得了显着的认可，近一半的受访者表示，在工具引入市场不到一年的时间里就采用了这种工具。他们看到了好处，但也担心这些工具的可靠性，伦理含义，和安全。
