PDF(Pubmed)
Abstract：
Large language models (LLMs) are transformer-based neural networks that can provide human-like responses to questions and instructions. LLMs can generate educational material, summarize text, extract structured data from free text, create reports, write programs, and potentially assist in case sign-out. LLMs combined with vision models can assist in interpreting histopathology images. LLMs have immense potential in transforming pathology practice and education, but these models are not infallible, so any artificial intelligence generated content must be verified with reputable sources. Caution must be exercised on how these models are integrated into clinical practice, as these models can produce hallucinations and incorrect results, and an over-reliance on artificial intelligence may lead to de-skilling and automation bias. This review paper provides a brief history of LLMs and highlights several use cases for LLMs in the field of pathology.
摘要：
大型语言模型（LLM）是基于变压器的神经网络，可以对问题和指令提供类似人类的响应。LLM可以生成教育材料，总结文本，从自由文本中提取结构化数据，创建报告,写程序,并可能在注销时提供帮助。LLM与视觉模型相结合可以帮助解释组织病理学图像。LLM在改变病理学实践和教育方面具有巨大的潜力，但是这些模型并非万无一失，因此，任何人工智能生成的内容都必须使用信誉良好的来源进行验证。必须谨慎对待这些模型如何融入临床实践，因为这些模型会产生幻觉和不正确的结果，对人工智能的过度依赖可能会导致去技能和自动化偏见。这篇综述论文提供了LLM的简要历史，并重点介绍了LLM在病理学领域的几个用例。
