Gastroenterology and Artificial Intelligence: 3rd Annual Artificial Intelligence Summit (On-Demand)

8- AI and Radiology_Kahn

Pdf Summary

In this document, titled "AI in Radiology: Lessons for GI Endoscopy," Dr. Charles E. Kahn Jr. discusses key lessons that can be learned from the field of radiology in implementing artificial intelligence (AI) in gastroenterology (GI) endoscopy. The author emphasizes the importance of testing, seeking the truth, setting standards, and challenging oneself in order to improve the use of AI in this field.<br /><br />The document delves into various considerations related to data in AI applications. It highlights the need to understand the source of data, how variables are defined, inclusion/exclusion criteria, the quantity of data used, and how well the training data aligns with the intended clinical use.<br /><br />Metrics for evaluating AI algorithms in radiology are also discussed, including the Dice similarity coefficient, Jaccard index, and Hausdorff distance. The author explains how these metrics measure the overlap and similarity between the predicted and the ground truth annotations.<br /><br />The concept of calibration curves is introduced, which depicts the relationship between the estimated risk of malignancy and the actual prevalence of malignancy for different probability levels. The document cites a study by Ayer et al. that illustrates this concept in the context of cancer diagnosis.<br /><br />The importance of understanding misclassification errors, such as false positives and false negatives, is also highlighted. It is crucial to consider these errors and strive for accurate and reliable AI algorithms.<br /><br />The document emphasizes the significance of having a well-defined "ground truth" when training AI models. It discusses the qualifications and training of data annotators, instructions provided to annotators, inter-rater and intra-rater variability, blinding methods, and the process for resolving discrepancies.<br /><br />The author concludes by referring to a specific challenge, the RSNA Pneumonia Detection Challenge, as an example of implementing AI in radiology. The challenge demonstrates the application of AI algorithms to detect pneumonia in chest X-ray images.<br /><br />Overall, the document provides insights and lessons from the field of radiology that can inform the use of AI in GI endoscopy.

Keywords

AI in Radiology: Lessons for GI Endoscopy

Dr. Charles E. Kahn Jr.

artificial intelligence

gastroenterology

testing

data considerations

evaluation metrics

calibration curves

misclassification errors

ground truth