ArXiv Preprint
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.
Jianing Qiu, Lin Li, Jiankai Sun, Jiachuan Peng, Peilun Shi, Ruiyang Zhang, Yinzhao Dong, Kyle Lam, Frank P. -W. Lo, Bo Xiao, Wu Yuan, Dong Xu, Benny Lo
2023-03-21
