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

M D B S Tam¹, T Dyer², G Dissez², T Naunton Morgan², M Hughes², J Illes³, R Rasalingham², S Rasalingham²

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

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

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

PubMed DOI

Abstract

AIM: 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. MATERIALS AND METHODS: A dataset of 400 CXRs including 200 difficult lung cancer cases was curat…

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