Automatic pre-population of normal chest x-ray reports using a high-sensitivity deep learning algorithm: a prospective study of clinical AI deployment (RPS1005b)


To evaluate a high-sensitivity deep learning algorithm for normal/abnormal chest x-ray (CXR) classification by deploying it in a real clinical setting.

Methods and materials:

A commercially available deep learning algorithm (QXR,, India) was integrated into the clinical workflow for a period of 3 months at an outpatient imaging facility. The algorithm, deployed on-premise, was integrated with PACS and RIS such that it automatically analysed all adult CXRs and reports for those which were determined to be “normal” were automatically populated in the RIS using HL7 messaging. Radiologists reviewed the CXRs as part of their regular workflow and ‘accepted’ or changed the pre-populated reports. Changes in reports were divided into ‘clinically insignificant’ and ‘clinically significant’ following which those CXRs with clinically significant changes were reviewed by a specialist chest radiologist with 8 years’ experience.


A total of 1,970 adult CXRs were analysed by AI, out of which 388 (19.69%) were identified to be normal. 361/388 (93.04%) of these were accepted by radiologists and in 14/388 (3.60%) clinically less significant changes (e.g. increased broncho-vascular markings) were made in reports. Upon review of the balance 13/388 (3.35%) CXRs, it was found that 12 had truly clinically significant missed findings by AI, including 3 with opacities, 3 with lymphadenopathy, 3 with blunted CP angle, 2 with nodules, and 1 with consolidation.


This study shows that there is a great potential to automate the identification of normal CXRs to a great degree, with very high sensitivity.