Some of the most translationally mature artificial intelligence (AI) applications in health care belong to colonoscopy, where algorithms can now be used in clinical practice to assist colorectal polyp detection and characterisation.1 The rapid pace of translation reflects the desperate need to identify solutions to drive up quality in colonoscopy and overcome the operator dependence associated with variable colorectal cancer protection. It is not surprising that AI developers are expanding research to further use cases applied to colonoscopy, particularly those that relate to performance measures and quality metrics.
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Omer F. Ahmad (2021) studied this question.
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