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The utilization of large language models (LLMs) in the Healthcare domain hasgenerated both excitement and concern due to their ability to effectivelyrespond to freetext queries with certain professional knowledge. This surveyoutlines the capabilities of the currently developed LLMs for Healthcare andexplicates their development process, with the aim of providing an overview ofthe development roadmap from traditional Pretrained Language Models (PLMs) toLLMs. Specifically, we first explore the potential of LLMs to enhance theefficiency and effectiveness of various Healthcare applications highlightingboth the strengths and limitations. Secondly, we conduct a comparison betweenthe previous PLMs and the latest LLMs, as well as comparing various LLMs witheach other. Then we summarize related Healthcare training data, trainingmethods, optimization strategies, and usage. Finally, the unique concernsassociated with deploying LLMs in Healthcare settings are investigated,particularly regarding fairness, accountability, transparency and ethics. Oursurvey provide a comprehensive investigation from perspectives of both computerscience and Healthcare specialty. Besides the discussion about Healthcareconcerns, we supports the computer science community by compiling a collectionof open source resources, such as accessible datasets, the latestmethodologies, 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 fromdiscriminative AI approaches to generative AI approaches, as well as a shiftfrom model-centered methodologies to datacentered methodologies.

A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics