ASGE at DDW AI in GI Workshop | May 2022-Pursuing Research in AI in GI and Endoscopy: Basics and Terminology

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This document titled "Pursuing Research in AI in GI and Endoscopy: Basics and Terminology" provides an overview of AI research in the field of gastroenterology and endoscopy. The author discusses various aspects of AI research, including the importance of data, deep learning techniques, design validation studies, performance metrics, randomized controlled trials, and regulatory considerations.<br /><br />The document highlights the key steps involved in conducting AI research, starting with defining the value of the research and identifying non-machine learning solutions to the problem. It emphasizes the significance of data availability and the speed at which machine learning analysis can aid in decision-making. The author also emphasizes the need to develop user-focused workflows and integrate machine learning into the clinical process.<br /><br />Deep learning techniques are explored, particularly convolutional neural networks, which are effective in tasks such as speech recognition and image analysis. The structure and training process of these networks are explained, including the use of convolution to extract features.<br /><br />The document discusses different performance metrics used in evaluating AI algorithms, such as the area under the receiver operating curve and the dice similarity coefficient. It also touches upon the importance of randomized controlled trials in assessing the effectiveness of AI interventions.<br /><br />Regulatory considerations are addressed, with a focus on regulatory approval for AI-enabled medical devices by the US FDA. The document explains different device classifications based on the potential risks associated with erroneous results.<br /><br />The document concludes by recommending further reading on the topic, including the book "Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again" by Eric Topol.

Asset Subtitle

Dennis Shung, MD, MHS, PhD

Keywords

Pursuing Research in AI in GI and Endoscopy

AI research

gastroenterology

endoscopy

data

deep learning techniques

design validation studies

performance metrics

randomized controlled trials

regulatory considerations