Evolving Role and Future Directions of Natural Language Processing in Gastroenterology


In line with the current trajectory of healthcare reform, significant emphasis has been placed on improving the utilization of data collected during a clinical encounter. Although the structured fields of electronic health records have provided a convenient foundation on which to begin such efforts, it was well understood that a substantial portion of relevant information is confined in the free-text narratives documenting care. Unfortunately, extracting meaningful information from such narratives is a non-trivial task, traditionally requiring significant manual effort. Today, computational approaches from a field known as Natural Language Processing (NLP) are poised to make a transformational impact in the analysis and utilization of these documents across healthcare practice and research, particularly in procedure-heavy sub-disciplines such as gastroenterology (GI). As such, this manuscript provides a clinically focused review of NLP systems in GI practice. It begins with a detailed synopsis around the state of NLP techniques, presenting state-of-the-art methods and typical use cases in both clinical settings and across other domains. Next, it will present a robust literature review around current applications of NLP within four prominent areas of gastroenterology including endoscopy, inflammatory bowel disease, pancreaticobiliary, and liver diseases. Finally, it concludes with a discussion of open problems and future opportunities of this technology in the field of gastroenterology and health care as a whole.

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We would like to thank An-Lin Cheng, PhD from the department of Bioinformatics at the University of Missouri-Kansas City for her assistance and expertise in developing this manuscript.



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Nehme, F., Feldman, K. Evolving Role and Future Directions of Natural Language Processing in Gastroenterology. Dig Dis Sci (2020). https://doi.org/10.1007/s10620-020-06156-y

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  • Gastroenterology
  • Artificial intelligence
  • Natural Language Processing
  • Health care