A Survey of Large Language Models for Healthcare: from Data, Technology,
  and Applications to Accountability and Ethics
Kai He Rui Mao Qika Lin Yucheng Ruan Xiang Lan Mengling Feng Erik Cambria
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.