mlsanityData to 7 Oct 2026

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

arXiv 2505.04073 ↗First posted 7 May 2025cs.CLcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 56 of its 133 references that are in the map. First posted on arXiv 7 May 2025.

Builds on

56 of 133 references.

References · 56

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38 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Countering Catastrophic Forgetting of Large Language Models for Better Instruction Following via Weight-Space Model Mergingcited by 0
  2. 2026Detecting HIV-Related Stigma in Clinical Narratives Using Large Language Modelscited by 0
  3. 2026Automated Clinical Report Generation for Remote Cognitive Remediation: Comparing Knowledge-Engineered Templates and LLMs in Low-Resource Settingscited by 0