Large AI models, or foundation models, are models recently emerging with massive scales both parameter-wise and data-wise, the magnitudes of which often reach beyond billions. Once pretrained, large AI models demonstrate impressive performance in various downstream tasks. A concrete example is the recent debut of ChatGPT, whose capability has compelled people's imagination about the far-reaching influence that large AI models can have and their potential to transform different domains of our life. In health informatics, the advent of large AI models has brought new paradigms for the design of methodologies. The scale of multimodality data in the biomedical and health domain has been ever-expanding especially since the community embraced the era of deep learning, which provides the ground to develop, validate, and advance large AI models for breakthroughs in health-related areas. This article presents an up-to-date comprehensive review of large AI models, from background to their applications. We identify seven key sectors that large AI models are applicable and might have substantial influence, including 1) molecular biology and drug discovery; 2) medical diagnosis and decision-making; 3) medical imaging and vision; 4) medical informatics; 5) medical education; 6) public health; and 7) medical robotics. We examine their challenges in health informatics, followed by a critical discussion about potential future directions and pitfalls of large AI models in transforming the field of health informatics.

翻译：大型AI模型，或称基础模型，是近期涌现的具有巨大规模参数与数据的模型，其规模通常远超十亿级别。一旦完成预训练，大型AI模型在各种下游任务中展现出令人瞩目的性能。一个具体例证是最近推出的ChatGPT，其能力激发了人们对大型AI模型可能产生的深远影响及其改变生活各领域潜力的想象。在健康信息学中，大型AI模型的出现为方法论设计带来了新范式。生物医学与健康领域的多模态数据规模持续扩大，尤其是自该领域进入深度学习时代以来，这为开发、验证和推进大型AI模型以实现健康相关领域的突破奠定了基础。本文从背景到应用对大型AI模型进行了最新的全面综述。我们确定了大型AI模型可应用且可能产生重大影响的七个关键领域：1）分子生物学与药物发现；2）医学诊断与决策；3）医学影像与视觉；4）医学信息学；5）医学教育；6）公共卫生；以及7）医疗机器人。我们探讨了其在健康信息学中面临的挑战，并随后对大型AI模型在转变健康信息学领域中的潜在未来方向及陷阱进行了批判性讨论。