系统梳理医疗领域自然语言生成技术与应用，助力临床效率提升。
Natural Language Generation in Healthcare: A Review of Methods and Applications
- 基于113篇论文的系统综述，涵盖数据模态与模型架构
- 识别出临床决策、文档生成等关键应用场景
- 为未来医疗AI研究提供方法论参考，适合临床与算法研究者
自然语言生成（NLG）是实现生成式人工智能的核心技术。随着大语言模型（LLMs）的突破，NLG已在多种医疗场景中广泛应用，展现出提升临床工作流程、支持临床决策和改善临床文档记录的潜力。该技术利用医学文本、图像和知识库等多种异构数据模态。研究者提出了多种生成模型，并应用于众多医疗任务。本文基于文献检索筛选出3,988篇相关文章中的113篇，系统回顾了医疗领域NLG的方法与应用，重点分析数据模态、模型架构、临床应用场景及评估方法。遵循PRISMA指南，分类总结关键技术，识别主要临床应用，并评估其能力、局限与新兴挑战。本综述全面覆盖了关键NLG技术与医疗应用，为未来研究如何利用NLG推动医学发现与医疗变革提供了重要洞见。
原文摘要 · Abstract (English)
Natural language generation (NLG) is the key technology to achieve generative artificial intelligence (AI). With the breakthroughs in large language models (LLMs), NLG has been widely used in various medical applications, demonstrating the potential to enhance clinical workflows, support clinical decision-making, and improve clinical documentation. Heterogeneous and diverse medical data modalities, such as medical text, images, and knowledge bases, are utilized in NLG. Researchers have proposed many generative models and applied them in a number of healthcare applications. There is a need for a comprehensive review of NLG methods and applications in the medical domain. In this study, we systematically reviewed 113 scientific publications from a total of 3,988 NLG-related articles identified using a literature search, focusing on data modality, model architecture, clinical applications, and evaluation methods. Following PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) guidelines, we categorize key methods, identify clinical applications, and assess their capabilities, limitations, and emerging challenges. This timely review covers the key NLG technologies and medical applications and provides valuable insights for future studies to leverage NLG to transform medical discovery and healthcare.
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