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Natural Language Processing: from Bedside to Everywhere.
Aramaki, Eiji; Wakamiya, Shoko; Yada, Shuntaro; Nakamura, Yuta.
Affiliation
  • Aramaki E; Nara Institute of Science and Technology (NAIST), Nara, Japan.
  • Wakamiya S; Nara Institute of Science and Technology (NAIST), Nara, Japan.
  • Yada S; Nara Institute of Science and Technology (NAIST), Nara, Japan.
  • Nakamura Y; Division of Radiology and Biomedical Engineering, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Yearb Med Inform ; 31(1): 243-253, 2022 Aug.
Article in En | MEDLINE | ID: mdl-35654422
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.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Medical Informatics / Natural Language Processing Language: En Journal: Yearb Med Inform Year: 2022 Document type: Article Affiliation country: Japan Country of publication: Germany

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Medical Informatics / Natural Language Processing Language: En Journal: Yearb Med Inform Year: 2022 Document type: Article Affiliation country: Japan Country of publication: Germany