Natural Language Generation in Healthcare: A Review of Methods and Applications

TL;DR

This review comprehensively analyzes 113 studies on natural language generation in healthcare, highlighting methods, applications, challenges, and future directions to enhance clinical workflows and decision-making.

Contribution

It provides a systematic categorization of NLG methods and applications in healthcare, offering valuable insights for future research and development.

Findings

NLG models are applied across diverse medical data modalities.

Current methods face limitations in evaluation and clinical integration.

Emerging challenges include data heterogeneity and model interpretability.

Abstract

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.