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

Applications of AI (1057) Clinical Informatics (2363) Clinical Information and Decision Making (3850) Natural Language Processing (1320) Decision Support for Health Professionals (2311) Machine Learning (3303) Artificial Intelligence (4933)

Published on in Vol 4 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/59251, first published .
Tablet with medical icons and stethoscope, displaying heart rate and health data.

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
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Journals

  1. Chen G, Yang X, Han Y, Zhang S, Kong Y, Yin D, Sun H, Yi X, Li H. Beyond Screen Time: Inferring Everyday Life Context from Diverse Smartphone Data. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2026;10(2):1 View
  2. Stuhlmiller T, Rabe A, Rapp J, Awawda A, Kouser H, Lui K, Salamon H, Chuyka D, Mahoney W, Furgason J, Paul M, Scarpa F, Kesari S, Newton M, Wong K, Kramer G, Shapiro M. A Scalable Method for Validated Data Extraction from Electronic Health Records with Large Language Models. AI in Precision Oncology 2026 View