Natural Language Processing: from Bedside to Everywhere

Eiji Aramaki; Shoko Wakamiya; Shuntaro Yada; Yuta Nakamura

doi:10.1055/s-0042-1742510

Yearbook of Medical Informatics, Table of Contents

CC BY-NC-ND 4.0 · Yearb Med Inform 2022; 31(01): 243-253
DOI: 10.1055/s-0042-1742510

Section 10: Natural Language Processing

Survey

Natural Language Processing: from Bedside to Everywhere

Authors

Eiji Aramaki

¹Nara Institute of Science and Technology (NAIST), Nara, Japan
Shoko Wakamiya

¹Nara Institute of Science and Technology (NAIST), Nara, Japan
Shuntaro Yada

¹Nara Institute of Science and Technology (NAIST), Nara, Japan
Yuta Nakamura

²Division of Radiology and Biomedical Engineering, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan

Abstract

Summary

Objectives: Owing to the rapid progress of natural language processing (NLP), the role of NLP in the medical field has radically gained considerable attention from both NLP and medical informatics. Although numerous medical NLP papers are published annually, there is still a gap between basic NLP research and practical product development. This gap raises questions, such as what has medical NLP achieved in each medical field, and what is the burden for the practical use of NLP? This paper aims to clarify the above questions.

Methods: We explore the literature on potential NLP products/services applied to various medical/clinical/healthcare areas.

Results: This paper introduces clinical applications (bedside applications), in which we introduce the use of NLP for each clinical department, internal medicine, pre-surgery, post-surgery, oncology, radiology, pathology, psychiatry, rehabilitation, obstetrics, and gynecology. Also, we clarify technical problems to be addressed for encouraging bedside applications based on NLP.

Conclusions: These results contribute to discussions regarding potentially feasible NLP applications and highlight research gaps for future studies.

Keywords

Natural language processing - medical application - chatbot - randomized controlled trial - social media

Full Text

References

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