From University Medical Imaging Toronto, Joint Department of Medical Imaging, University Health Network, Mount Sinai Hospital, and Women's College Hospital, University of Toronto, Toronto General Hospital, 200 Elizabeth St, Peter Munk Bldg, 1st Fl, Toronto, ON, Canada M5G 24C.
Radiology. 2024 Jan;310(1):e232756. doi: 10.1148/radiol.232756.
Although chatbots have existed for decades, the emergence of transformer-based large language models (LLMs) has captivated the world through the most recent wave of artificial intelligence chatbots, including ChatGPT. Transformers are a type of neural network architecture that enables better contextual understanding of language and efficient training on massive amounts of unlabeled data, such as unstructured text from the internet. As LLMs have increased in size, their improved performance and emergent abilities have revolutionized natural language processing. Since language is integral to human thought, applications based on LLMs have transformative potential in many industries. In fact, LLM-based chatbots have demonstrated human-level performance on many professional benchmarks, including in radiology. LLMs offer numerous clinical and research applications in radiology, several of which have been explored in the literature with encouraging results. Multimodal LLMs can simultaneously interpret text and images to generate reports, closely mimicking current diagnostic pathways in radiology. Thus, from requisition to report, LLMs have the opportunity to positively impact nearly every step of the radiology journey. Yet, these impressive models are not without limitations. This article reviews the limitations of LLMs and mitigation strategies, as well as potential uses of LLMs, including multimodal models. Also reviewed are existing LLM-based applications that can enhance efficiency in supervised settings.
尽管聊天机器人已经存在了几十年，但基于转换器的大型语言模型（LLM）的出现通过最近一波人工智能聊天机器人，包括 ChatGPT，吸引了全世界的注意力。转换器是一种神经网络架构，能够更好地理解语言的上下文，并在大量未标记的数据（例如来自互联网的非结构化文本）上进行高效训练。随着 LLM 的规模不断扩大，其性能的提高和新出现的能力彻底改变了自然语言处理。由于语言是人类思维的组成部分，因此基于 LLM 的应用在许多行业都具有变革性的潜力。事实上，基于 LLM 的聊天机器人在许多专业基准测试中表现出了与人类相当的水平，包括在放射学领域。LLM 在放射学中有许多临床和研究应用，其中一些已经在文献中进行了探索，并取得了令人鼓舞的结果。多模态 LLM 可以同时解释文本和图像以生成报告，这与放射学中的当前诊断途径非常相似。因此，从申请到报告，LLM 有机会积极影响放射学之旅的几乎每一个步骤。然而，这些令人印象深刻的模型并非没有局限性。本文回顾了 LLM 的局限性和缓解策略，以及 LLM 的潜在用途，包括多模态模型。还回顾了现有的基于 LLM 的应用程序，这些应用程序可以在监督环境中提高效率。
