Kavli Affiliate: Cheng Peng
| First 5 Authors: Mengxian Lyu, Xiaohan Li, Ziyi Chen, Jinqian Pan, Cheng Peng
| Summary:
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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