Natural language is ubiquitous in the workflow of medical imaging. Radiologists create
and consume free text in their daily work, some of which can be amenable to enhancements
through automatic processing. Recent advancements in deep learning and “artificial
intelligence” have had a significant positive impact on natural language processing
(NLP). This article discusses the history of how researchers have extracted data and
encoded natural language information for analytical processing, starting from NLP's
humble origins in hand-curated, linguistic rules. The evolution of medical NLP including
vectorization, word embedding, classification, as well as its use in automated speech
recognition, are also explored. Finally, the article will discuss the role of machine
learning and neural networks in the context of significant, if incremental, improvements
in NLP.
Key Words
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Article info
Publication history
Published online: September 17, 2019
Accepted:
August 19,
2019
Received in revised form:
August 16,
2019
Received:
April 26,
2019
Identification
Copyright
© 2019 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
