Improving clinical documentation: automatic inference of ICD-10 codes from patient notes using BERT model

This paper explores the emerging role of large language models (LLMs) in healthcare, offering an analysis of their applications and limitations. Attention mechanisms and transformer architectures enable LLMs to perform tasks like extracting clinical information and assisting in diagnostics. We highlight research that demonstrates early application of LLMs in various domains and along the care pathway. With their promise, LLMs pose ethical and practical challenges, including data bias and the need for human oversight. This review serves as a guide for clinicians and researchers, outlining potential healthcare applications – ranging from document translation to clinical decision support – while cautioning about inherent limitations and ethical considerations. The aim of this work is to encourage the knowledgeable use of LLMs in healthcare and drive further study in this important emerging field.

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Improving clinical documentation: automatic inference of ICD-10 codes from patient notes using BERT model

Cited by 3 publications

References 26 publications

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TC-BERT: large-scale language model for Korean technology commercialization documents

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