This paper is in the following e-collection/theme issue:

Applications of AI (232) Clinical Information and Decision Making (1787) Natural Language Processing (859) Artificial Intelligence (1964) Decision Support for Health Professionals (1449) Machine Learning (1934) Clinical Informatics (1241)

Published on in Vol 4 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/59251, first published .
Exploring Named Entity Recognition Potential and the Value of Tailored Natural Language Processing Pipelines for Radiology, Pathology, and Progress Notes in Clinical Decision Support: Quantitative Study

Exploring Named Entity Recognition Potential and the Value of Tailored Natural Language Processing Pipelines for Radiology, Pathology, and Progress Notes in Clinical Decision Support: Quantitative Study

Exploring Named Entity Recognition Potential and the Value of Tailored Natural Language Processing Pipelines for Radiology, Pathology, and Progress Notes in Clinical Decision Support: Quantitative Study

Authors of this article:

Veysel Kocaman1 Author Orcid Image ;   Fu-Yuan Cheng2 Author Orcid Image ;   Julio Bonis1 Author Orcid Image ;   Ganesh Raut2 Author Orcid Image ;   Prem Timsina2 Author Orcid Image ;   David Talby1 Author Orcid Image ;   Arash Kia3 Author Orcid Image
Article Authors Cited by Tweetations Metrics

Veysel Kocaman   1 , PhD ;   Fu-Yuan Cheng   2 , MSc ;   Julio Bonis   1 , MD, MSc, MBA ;   Ganesh Raut   2 , MS ;   Prem Timsina   2 , SCD ;   David Talby   1 , PhD ;   Arash Kia   3 , MD, MSc

1 John Snow Labs Inc, Lewes, DE, United States

2 Institute for Healthcare Delivery Science, Mount Sinai, New York, NY, United States

3 Department of Anesteshiology, Perioperative and Pain Medicine, Mount Sinai, New York, NY, United States

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