• Alex Novak, Sarim Ather, Avneet Gill, Peter Aylward, Giles Maskell, Gordon W Cowell, Abdala Trinidad Espinosa Morgado, Tom Duggan, Melissa Keevill, Olivia Gamble, Osama Akrama, Elizabeth Belcher, Rhona Taberham, Rob Hallifax, Jasdeep Bahra, Abhishek Banerji, Jon Bailey, Antonia James, Ali Ansaripour, Nathan Spence, John Wrightson, Waqas Jarral, Steven Barry, Saher Bhatti, Kerry Astley, Amied Shadmaan, Sharon Ghelman, Alec Baenen, Jason Oke, Claire Bloomfield, Hilal Johnson, Mark Beggs, and Fergus Gleeson.
• Emergency Department, Oxford University Hospitals NHS Foundation Trust, Oxford, UK alex.novak@ouh.nhs.uk.
• Emerg Med J. 2024 Sep 25; 41 (10): 602609602-609.

BackgroundArtificial intelligence (AI)-assisted image interpretation is a fast-developing area of clinical innovation. Most research to date has focused on the performance of AI-assisted algorithms in comparison with that of radiologists rather than evaluating the algorithms' impact on the clinicians who often undertake initial image interpretation in routine clinical practice. This study assessed the impact of AI-assisted image interpretation on the diagnostic performance of frontline acute care clinicians for the detection of pneumothoraces (PTX).MethodsA multicentre blinded multi-case multi-reader study was conducted between October 2021 and January 2022. The online study recruited 18 clinician readers from six different clinical specialties, with differing levels of seniority, across four English hospitals. The study included 395 plain CXR images, 189 positive for PTX and 206 negative. The reference standard was the consensus opinion of two thoracic radiologists with a third acting as arbitrator. General Electric Healthcare Critical Care Suite (GEHC CCS) PTX algorithm was applied to the final dataset. Readers individually interpreted the dataset without AI assistance, recording the presence or absence of a PTX and a confidence rating. Following a 'washout' period, this process was repeated including the AI output.ResultsAnalysis of the performance of the algorithm for detecting or ruling out a PTX revealed an overall AUROC of 0.939. Overall reader sensitivity increased by 11.4% (95% CI 4.8, 18.0, p=0.002) from 66.8% (95% CI 57.3, 76.2) unaided to 78.1% aided (95% CI 72.2, 84.0, p=0.002), specificity 93.9% (95% CI 90.9, 97.0) without AI to 95.8% (95% CI 93.7, 97.9, p=0.247). The junior reader subgroup showed the largest improvement at 21.7% (95% CI 10.9, 32.6), increasing from 56.0% (95% CI 37.7, 74.3) to 77.7% (95% CI 65.8, 89.7, p<0.01).ConclusionThe study indicates that AI-assisted image interpretation significantly enhances the diagnostic accuracy of clinicians in detecting PTX, particularly benefiting less experienced practitioners. While overall interpretation time remained unchanged, the use of AI improved diagnostic confidence and sensitivity, especially among junior clinicians. These findings underscore the potential of AI to support less skilled clinicians in acute care settings.© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

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