Augmenting lung cancer diagnosis on chest radiographs: positioning artificial intelligence to improve radiologist performance.

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

To evaluate the role that artificial intelligence (AI) could play in assisting radiologists as the first reader of chest radiographs (CXRs), to increase the accuracy and efficiency of lung cancer diagnosis by flagging positive cases before passing the remaining examinations to standard reporting.

Related collections

Author and article information

Journal

Journal ID (iso-abbrev): Clin Radiol

Title: Clinical radiology

Publisher: Elsevier BV

ISSN (Electronic): 1365-229X

ISSN (Print): 0009-9260

Publication date (Electronic): August 2021

Volume: 76

Issue: 8

Affiliations

[1 ] Mid and South Essex University Hospitals Group, Southend Hospital, Department of Radiology, Prittlewell Chase, Westcliff-on-Sea, SS0 0RY, UK. Electronic address: matthewtam2005@gmail.com.

[2 ] Behold.ai, 180 Borough High St, London SE1 1LB, UK.

[3 ] Dorset County Hospital Foundation Trust, Williams Ave, Dorchester, DT1 2JY, UK.

Article

Publisher Item ID: S0009-9260(21)00237-3

DOI: 10.1016/j.crad.2021.03.021

PubMed ID: 33993997

SO-VID: bc214ace-4969-445f-9619-f7eecd9771e0

History

Data availability:

Comments

Comment on this article

Cited by 7

See all cited by