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ASGE Masterclass: Artificial Intelligence 101 (On- ...
Clinical Evaluation of ML Algorithms in Gastroente ...
Clinical Evaluation of ML Algorithms in Gastroenterology
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Pdf Summary
The document discusses the clinical evaluation of machine learning algorithms in the field of gastroenterology, focusing on computer-aided polyp detection (CADe) and diagnosis (CADx). It highlights the need for evidence to adopt CADx and practical considerations for evaluation in endoscopy suites. Studies have shown that AI consistently improves adenoma detection rates, particularly for adenomas 5mm in size. The use of AI during colonoscopy has been shown to reduce adenoma miss rates and increase the need for intensive surveillance. The implementation of CADe in real practice is being studied through observational research, which has shown some concerns regarding endoscopist performance. Additionally, the document emphasizes the importance of CADx in encouraging AI adoption for colonoscopy, with the potential for cost-savings. It also mentions the challenges in critically evaluating AI tools in GI units and the importance of measuring relevant metrics. The integration of AI with human decision-making can lead to better outcomes in clinical practice. The document concludes by stressing the significance of NLP-powered analytics in gastroenterology practices and the need for further research and development in the field of AI and ML for improved patient outcomes.
Asset Subtitle
Tyler Berzin MD, MS, FASGE
Keywords
machine learning algorithms
gastroenterology
computer-aided polyp detection
CADe
diagnosis
CADx
adenoma detection rates
colonoscopy
AI adoption
NLP-powered analytics
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