Gastroenterology & Artificial Intelligence: 3rd Annual Global Artificial Intelligence Summit | September 2021-Opportunity to Redefine Quality Metrics in Endoscopy Using AI

Opportunity to Redefine Quality Metrics in Endoscopy Using AI

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In this document, Dr. Sravanthi Parasa discusses the potential for using artificial intelligence (AI) to redefine quality metrics in endoscopy. Quality metrics are important in healthcare as they allow for the identification of practices that can lead to higher quality care and efficiency. Currently, there are challenges in measuring quality in endoscopy, including the feasibility of collecting data and the potential for unintended consequences.<br /><br />Dr. Parasa highlights recent developments in the field, including the use of AI in automatic quality control systems for colonoscopy. These systems use deep convolutional neural network (DCNN) models to time the withdrawal phase, supervise withdrawal stability, evaluate bowel preparation, and detect colorectal polyps in real time. Studies have shown the impact of these systems on polyp and adenoma detection, improving the quality of care.<br /><br />Other advancements discussed include AI-based approaches to improving colonoscopy coverage and assessing bowel preparation. These technologies use computer vision algorithms to analyze images and provide objective assessments of coverage and preparation quality.<br /><br />Overall, AI has the potential to standardize quality metrics in endoscopy, remove subjectivity in measurements, and improve outcomes by augmenting the intelligence of clinicians. Dr. Parasa emphasizes the importance of imagination and encourages the embrace of AI technology to redefine quality metrics in endoscopy.<br /><br />In conclusion, the application of AI in endoscopy has the potential to revolutionize quality metrics by providing objective measurements and improving patient outcomes.

Keywords

artificial intelligence

quality metrics

endoscopy

healthcare

data collection

colonoscopy

polyp detection

adenoma detection

bowel preparation

patient outcomes