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Natural language processing (NLP) will enable a variety of assistive radiologic applications.
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Advances building on NLP techniques will permit machines to understand, classify, summarize, and generate text to automate linguistic tasks.
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NLP algorithms can be complementary to other radiology imaging artificial intelligence applications for diagnostic or workflow enhancement.
Review of Natural Language Processing in Radiology
Section snippets
Key points
What is natural language processing?
NLP is defined as a branch of artificial intelligence that is concerned with the interaction between computers and humans using natural language. NLP is a multidisciplinary field that combines classic linguistics with traditional computer science and modern artificial intelligence (AI) methods. The intention of NLP is to enable machines to read and understand human languages for meaningful purposes. Given the diversity of tasks possible, there are potentially many different customizations or
Definition
Although simple tokenization of words allows the mapping of each individual word to an index as a one-dimensional array or vector, those tokenization approaches miss the context 2 similar words might have with one another. For instance, a model that assigns a numerical index to a word based on alphabetical position might place “king” and “queen” or “men” and “women” very far apart, but these terms have closely related higher-level concepts (ie, “royal titles”, “gender”).
Instead of simply
Regular Expressions
Regular expressions, additionally termed as regex or regexp is a sequence of characters that define a search pattern, often used for searching and matching patterns found in strings. Although more commonly known whole search keyword matching is encompassed within the functionality of regular expressions, the concept and syntax of regular expressions is an extremely compact but expressive manner in which to compose matching rules for strings. Originating from theoretic works on regular languages
Automatic Protocoling
Some investigators have proposed using machine learning to automate magnetic resonance (MR) imaging protocol selection of radiology requisitions. In a study by Brown and Marotta,39 a machine learning model was developed to classify unstructured clinical history indications and assign MR imaging protocols, with attempted models comparing support vector machine, gradient boosting, and random forest techniques. The most performant model, a gradient boosting machine, was able to achieve a protocol
Limitations
Working with NLP poses different challenges and obstacles in comparison with machine learning in computer vision, some of which are unique to linguistics. One key difficulty when working with NLP problems concerns the accessibility of training datasets. In contrast with the longer open-science public record of recent machine learning and AI competitions and imaging datasets, because of the inherently private nature of reports and biomedical text, large public datasets of medical records
Summary
The ability to generalize with machine learning and AI technologies permits a wide gamut of possibilities and applications. Although much attention has been paid to image interpretative tasks, numerous other opportunities exist with NLP that offer similar or greater clinical value, often as an adjunctive or assistive technology, which could lead to improved clinical workflows, greater safety and efficiencies, and improved patient quality of life and health care satisfaction. Rather than posing
Disclosure
J.W. Luo and J.J.R. Chong: no relevant disclosures.
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