The utilization of large language models (LLMs) in the Healthcare domain has
generated both excitement and concern due to their ability to effectively
respond to freetext queries with certain professional knowledge. This survey
outlines the capabilities of the currently developed LLMs for Healthcare and
explicates their development process, with the aim of providing an overview of
the development roadmap from traditional Pretrained Language Models (PLMs) to
LLMs. Specifically, we first explore the potential of LLMs to enhance the
efficiency and effectiveness of various Healthcare applications highlighting
both the strengths and limitations. Secondly, we conduct a comparison between
the previous PLMs and the latest LLMs, as well as comparing various LLMs with
each other. Then we summarize related Healthcare training data, training
methods, optimization strategies, and usage. Finally, the unique concerns
associated with deploying LLMs in Healthcare settings are investigated,
particularly regarding fairness, accountability, transparency and ethics. Our
survey provide a comprehensive investigation from perspectives of both computer
science and Healthcare specialty. Besides the discussion about Healthcare
concerns, we supports the computer science community by compiling a collection
of open source resources, such as accessible datasets, the latest
methodologies, code implementations, and evaluation benchmarks in the Github.
Summarily, we contend that a significant paradigm shift is underway,
transitioning from PLMs to LLMs. This shift encompasses a move from
discriminative AI approaches to generative AI approaches, as well as a shift
from model-centered methodologies to datacentered methodologies.

大型语言模型在医疗领域的利用引发了劲头和担忧，本文概述了目前开发的大型语言模型在医疗保健方面的能力，并阐明它们的发展过程，旨在提供从传统预训练语言模型（PLMs）到大型语言模型（LLMs）的发展路线图的概览。总的来说，我们认为正在进行一次重大的范式转变，从 PLMs 过渡到 LLMs，这包括从辨别式人工智能方法向生成式人工智能方法的转变，以及从以模型为中心的方法论向以数据为中心的方法论的转变。