[论文解读] A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics

Kai He, Rui Mao+5 more|arXiv (Cornell University)|Oct 9, 2023

Artificial Intelligence in Healthcare and Education被引用 28

一句话总结

本综述分析大型语言模型（LLMs）在医疗保健中的开发与应用，与传统的PLMs进行比较，并讨论伦理与评估。

ABSTRACT

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 data-centered methodologies. Also, we determine that the biggest obstacle of using LLMs in Healthcare are fairness, accountability, transparency and ethics.

研究动机与目标

• 将从预训练语言模型（PLMs）到大型语言模型（LLMs）在医疗保健中的发展路线图进行总结。
• 比较PLMs与LLMs并分析它们在医学领域的优点、局限性与应用场景。
• 整理医疗保健领域的训练数据、训练方法、优化策略与LLMs的使用指南。
• 审视在部署医疗保健LLMs时的公平性、问责性、透明度及伦理问题。
• 提供开源资源和构建私有医疗保健LLMs的实用指南。

提出的方法

• 回顾并综合从PLMs到LLMs在医疗保健中的关键发展。
• 概述LLMs在医疗保健任务中的能力与局限性，如NER、RE、TC、STS、QA和对话。
• 概述医疗保健LLMs的数据来源、训练方法、优化策略与评估方法。
• 讨论在医疗保健LLM部署中的公平性、问责性、透明度与伦理问题。
• 汇编与医疗保健LLMs相关的开源数据集、方法、代码和基准。

Figure 1: The development from PLMs to LLMs. GPT-3 [ 17 ] marks a significant milestone in the transition from PLMs to LLMs, signaling the beginning of a new era.

实验结果

研究问题

• RQ1 当前在医疗保健应用中，LLMs的能力与局限性是什么？
• RQ2 PLMs在医疗保健开发和使用方面与LLMs有何不同，对实践有何影响？
• RQ3 医疗保健LLMs使用了哪些数据、训练方法与评估策略，以及它们如何影响性能与安全性？
• RQ4 在医疗保健LLMs中出现了哪些伦理、公平、问责与透明度方面的关切，如何应对？

主要发现

• LLMs在包括NER、RE、TC、STS、QA和对话生成等多种医疗保健任务中推动了进展。
• Med-PaLM 2在USMLE风格的题目上表现出高水平的潜力，体现了在医学领域的专家级能力。
• 在医疗保健领域正经历从判别式PLMs向生成式LLMs的范式转变，以及从以模型为中心向以数据为中心的发展。
• 医疗保健LLMs越来越依赖多模态数据和知识图谱来支持复杂的临床推理与报告。
• 该综述提供了一系列开源数据集、方法、代码和基准，以支持私有医疗保健LLM的开发。
• 如鲁棒性、偏见、公平性、问责和透明性等伦理考量被分析，并提供负责任部署的指导。

Figure 2: The organizational framework for the content. Section III , Section IV , Section V are technology details, while Section II , Section VI and Section VI are more valued for Healthcare professionals.