Centre for Health Services Research, University of Queensland, Woolloongabba, Australia.
School of Electrical Engineering and Computer Sciences, The University of Queensland, St Lucia, Queensland, Australia.
Intern Med J. 2024 May;54(5):705-715. doi: 10.1111/imj.16393. Epub 2024 May 7.
Foundation machine learning models are deep learning models capable of performing many different tasks using different data modalities such as text, audio, images and video. They represent a major shift from traditional task-specific machine learning prediction models. Large language models (LLM), brought to wide public prominence in the form of ChatGPT, are text-based foundational models that have the potential to transform medicine by enabling automation of a range of tasks, including writing discharge summaries, answering patients questions and assisting in clinical decision-making. However, such models are not without risk and can potentially cause harm if their development, evaluation and use are devoid of proper scrutiny. This narrative review describes the different types of LLM, their emerging applications and potential limitations and bias and likely future translation into clinical practice.
基础机器学习模型是一种深度学习模型,能够使用不同的数据模态(如文本、音频、图像和视频)执行许多不同的任务。它们代表了从传统的特定于任务的机器学习预测模型的重大转变。大型语言模型(LLM),以 ChatGPT 的形式引起了广泛的公众关注,是基于文本的基础模型,有可能通过实现一系列任务的自动化来改变医学,包括编写出院总结、回答患者问题和协助临床决策。然而,如果这些模型的开发、评估和使用缺乏适当的审查,它们并非没有风险,并且可能会造成伤害。本叙述性评论描述了不同类型的 LLM、它们的新兴应用以及潜在的局限性和偏见,以及它们可能在未来转化为临床实践。