Natural Language Processing in Radiology: A Systematic Review.

By E. Pons, Loes M. M. Braun, M. Hunink, Jan A. Kors. 2016

515 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Health Informatics, Radiology, Nuclear Medicine and Imaging

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Pranav Rajpurkar
2. Mohit Bansal
3. Matthew P. Lungren
4. Andrew Y. Ng
5. Sijia Liu
6. Arvid Lundervold
7. Joel T. Dudley
8. Liwei Wang

In Health Informatics

1. Joel T. Dudley
2. Pranav Rajpurkar
3. Arvid Lundervold
4. Riccardo Miotto

In Radiology, Nuclear Medicine and Imaging

1. Pranav Rajpurkar
2. Matthew P. Lungren
3. Andrew Y. Ng
4. Arvid Lundervold
5. Florian Tramèr
6. Dan Boneh

Citations in the corpus

• An overview of deep learning in medical imaging focusing on MRI (2018, methodology): “Recently, deep learning methods have also been used for automated generation of radiology reports, typically incorporating long-short-term-memory (LSTM) network models to generate the textual paragraphs [314, 315, 316, 317], and also to identify findings in radiology reports [318, 319, 320].”
• AI in Medical Imaging Informatics: Current Challenges and Future Directions (2020, unknown): “Undoubtedly, research in deep learning applications and methods is expected to grow, especially in in view of documented advances across the spectrum of healthcare data, including EHR [260], genomic [261], [262], physiological parameters [263], and natural language data processing [264].”
• CheXbert: Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT (2020, background): “Many natural language processing systems have been designed to label reports using sophisticated feature engineering of medical domain knowledge (Pons et al., 2016).”
• Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review (2019, background): “Systematic reviews related to processing of clinical notes have been published in the past [13-18]; however, none have focused specifically on chronic diseases, making it difficult to derive conclusions and recommendations on this specific and very diverse domain.”
• Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology (2018, background): “NLP offers a variety of applications and use cases, with applications in diagnostic surveillance, identifying cases for research studies, and assessment of radiologic practice quality [43].”
• How Machine Learning will Transform Biomedicine (2020, unknown): “…have attempted to use natural language processing algorithms to mine publications (Dong et al., 2018), EHRs (Shickel et al., 2018), and clinical reports (Kreimeyer et al., 2017; Pons et al., 2016) for useful knowledge, such as biomarker-therapy associations and biological pathways of interest.”
• Clinical Information Extraction Applications: A Literature Review (2017, background): “[37] took a close look at NLP methods and tools that support practical applications in radiology.”