医疗保健领域的自然语言生成：方法与应用综述
Natural Language Generation in Healthcare: A Review of Methods and Applications
AI总结：
本文系统综述了113篇医疗NLG文献，梳理数据模态、模型架构、临床应用与评估方法，为利用NLG推动医学发现和医疗变革提供参考。
摘要：
自然语言生成（NLG）是实现生成式人工智能（AI）的关键技术。随着大语言模型（LLMs）取得突破，NLG已被广泛应用于各类医疗场景，展现出增强临床工作流程、支持临床决策和改善临床文档的潜力。医疗数据模态异构且多样，包括医学文本、图像和知识库等，均被用于NLG。研究人员提出了许多生成模型，并将其应用于多种医疗保健任务中。目前亟需对医学领域中NLG的方法与应用进行全面综述。在本研究中，我们通过文献检索从共计3,988篇与NLG相关的文章中，系统回顾了113篇科学出版物，重点关注数据模态、模型架构、临床应用和评估方法。遵循PRISMA（系统综述和荟萃分析优先报告条目）指南，我们对关键方法进行了分类，识别了临床应用，并评估了它们的能力、局限性和新出现的挑战。这篇及时的综述涵盖了关键的NLG技术和医学应用，并为未来研究利用NLG推动医学发现和医疗保健变革提供了有价值的见解。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
