Abstract
Large Language Models (LLMs) have fundamentally transformed approaches to Natural Language Processing (NLP) tasks across diverse domains. In healthcare, accurate and cost-efficient text classification is crucial, whether for clinical notes analysis, diagnosis coding, or any other task, and LLMs present promising potential. Text classification has always faced multiple challenges, including manual annotation for training, handling imbalanced data, and developing scalable approaches. With healthcare, additional challenges are added, particularly the critical need to preserve patients' data privacy and the complexity of the medical terminology. Numerous studies have been conducted to leverage LLMs for automated healthcare text classification and contrast the results with existing machine learning-based methods where embedding, annotation, and training are traditionally required. Existing systematic reviews about LLMs either do not specialize in text classification or do not focus on the healthcare domain. This research synthesizes and critically evaluates the current evidence found in the literature regarding the use of LLMs for text classification in a healthcare setting. Major databases (e.g., Google Scholar, Scopus, PubMed, Science Direct) and other resources were queried, which focused on the papers published between 2018 and 2024 within the framework of PRISMA guidelines, which resulted in 65 eligible research articles. These were categorized by text classification type (e.g., binary classification, multi-label classification), application (e.g., clinical decision support, public health and opinion analysis), methodology, type of healthcare text, and metrics used for evaluation and validation. This review reveals the existing gaps in the literature and suggests future research lines that can be investigated and explored.
Abstract (translated)
大型语言模型（LLMs）已经在不同领域的自然语言处理（NLP）任务中根本性地转变了方法。在医疗保健领域，无论是临床记录分析、诊断编码还是其他任何任务，准确且成本效益高的文本分类都至关重要，而LLMs展现出了巨大的潜力。然而，文本分类一直面临着诸如训练所需的手动注释、不平衡数据处理以及开发可扩展的方法等多重挑战。特别是在医疗保健中，还需要解决患者数据隐私保护这一关键问题及医学术语的复杂性带来的额外挑战。 许多研究致力于利用LLMs进行自动化的医疗文本分类，并将结果与传统基于机器学习方法（这些方法通常需要嵌入、注释和训练）相比较。现有的关于LLMs的系统综述要么不专门针对文本分类，要么不对准医疗保健领域。这项研究旨在综合并批判性地评估文献中有关在医疗环境下使用LLMs进行文本分类的现有证据。研究人员查询了主要数据库（例如Google Scholar、Scopus、PubMed、Science Direct）及其他资源，并按照PRISMA指南框架筛选出2018年至2024年间发表的相关论文，共找到65篇符合条件的研究文章。 这些研究被按文本分类类型（如二元分类或多标签分类）、应用领域（如临床决策支持、公共卫生和舆论分析）、方法论、医疗文本种类以及用于评估与验证的指标进行了分类。此综述揭示了文献中的现有空白，并提出了未来可以探索的研究方向。
URL
https://arxiv.org/abs/2503.01159
