Natural language processing (NLP) is an area of active research in artificial intelligence concerned with human languages. NLP programs use human written text or human speech as data for analysis. The goals of NLP programs can vary from generating insights from texts or recorded speech to generating text or speech.
The first area of NLP to gain wide usage in radiology was voice recognition. In some radiology practices, radiologists use voice recognition programs to create reports. Increasing research in artificial neural nets has sparked an interest in topic modelling algorithms of NLP which can be used to automate the labeling of images. Examples include the NIH chest x-ray data set ChestX-ray8 1.
Due to the brevity, limited vocabulary and structured nature of radiology reports, many different algorithm types have proven successful at annotation of radiology reports.
Areas of active research for the application of NLP in radiology include areas of Natural language understanding (NLU) such as topic modelling, other forms of information extraction and keyword searching. NLP also includes Natural Language Generation (NLG).
- 1. Wang X, Peng Y, Lu L, Lu Z, Bagheri M, Summers RM. ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases. IEEE CVPR 2017,
Related Radiopaedia articles
- artificial intelligence (AI)
- imaging data sets
- computer-aided diagnosis (CAD)
- machine learning (overview)
- common data preparation/preprocessing steps
- DICOM to bitmap conversion
- principal component analysis
- training, testing and validation datasets
- mean squared error
- cross entropy
- optimization algorithms
- stochastic gradient descent
- momentum (Nesterov)
- linear and quadratic
- batch normalization
- natural language processing
- rule-based expert systems