Gastroenterology and Artificial Intelligence: 3rd Annual Artificial Intelligence Summit (On-Demand) | September 2021-Panel Discussion and Q&A

Video Transcription

Video Summary

In this video, several panelists discuss the importance of training a well-annotated and carefully curated dataset to train AI systems for medical imaging. The panelists highlight the need for high-quality data sets to improve the accuracy of AI systems and emphasize the importance of having diverse datasets that represent different populations and conditions. They also discuss the challenges of evaluating AI systems in pre-marketing studies and suggest that transparency in labeling and reporting is crucial to accurately reflect the performance of the system. The panelists also touch on the topic of explainable AI and the need for standardizing the type of information presented to physicians in order to understand and explain how these algorithms work. They also discuss the role of regulatory bodies, such as the FDA, in assessing AI algorithms and ensuring their safety and effectiveness. The panelists debate the challenges of reimbursement and the potential for AI to enhance productivity in medical imaging. The video concludes with an announcement of the next part of the meeting. The panelists featured in the video include Dr. Khan, Dr. Hassan, Dr. Riegler, and Dr. Haugen.

Keywords

training dataset

AI systems

medical imaging

diverse datasets

transparency

regulatory bodies