Artificial intelligence (AI) is being implemented into colonoscopy practice, but no study has investigated whether AI is cost-saving. We quantified the cost reduction from using AI as an aid in the optical diagnosis of colorectal polyps.
This study is an add-on analysis of a clinical trial that investigated the performance of AI for differentiating colorectal polyps (ie, neoplastic versus non-neoplastic). We included all patients with diminutive (≤5 mm) rectosigmoid polyp for analyses. The average colonoscopy cost was compared for 2 scenarios: (1) a diagnose-and-leave strategy supported by the AI prediction (ie, diminutive rectosigmoid polyps were not removed when predicted as non-neoplastic), and (2) a resect-all-polyps strategy. Gross annual costs for colonoscopies were also calculated based on numbers and reimbursement of colonoscopies conducted under public health insurances in 4 countries.
Overall, 207 patients with 250 diminutive rectosigmoid polyps (104 neoplastic, 144 non-neoplastic, and 2 indeterminate) were included. AI correctly differentiated neoplastic polyps with 93.3% sensitivity, 95.2% specificity, and 95.2% negative predictive value. Thus, 105 polyps were removed whereas 145 were left under the diagnose-and-leave strategy, which was estimated to reduce the average colonoscopy cost and the gross annual reimbursement for colonoscopies by 18.9% and 149.2 million dollars in Japan, 6.9% and 12.3 million dollars in England, 7.6% and 1.1 million dollars in Norway, and 10.9% and 85.2 million dollars in the United States, respectively, compared to the resect-all-polyps strategy.
The use of AI to enable the diagnose-and-leave strategy results in substantial cost reductions for colonoscopy.

Copyright © 2020 American Society for Gastrointestinal Endoscopy. Published by Elsevier Inc. All rights reserved.

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