Generalization of Artificial Intelligence Models in Medical Imaging: A Case-Based Review

• URL: http://arxiv.org/abs/2211.13230v1
• Date: Tue, 15 Nov 2022 10:09:51 GMT
• Title: Generalization of Artificial Intelligence Models in Medical Imaging: A Case-Based Review
• Authors: Rishi Gadepally, Andrew Gomella, Eric Gingold, Paras Lakhani
• Abstract summary: It is important for practicing radiologists to understand the pitfalls of various AI algorithms. Use of AI should be preceded by a fundamental understanding of the risks and benefits to those it is intended to help.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The discussions around Artificial Intelligence (AI) and medical imaging are centered around the success of deep learning algorithms. As new algorithms enter the market, it is important for practicing radiologists to understand the pitfalls of various AI algorithms. This entails having a basic understanding of how algorithms are developed, the kind of data they are trained on, and the settings in which they will be deployed. As with all new technologies, use of AI should be preceded by a fundamental understanding of the risks and benefits to those it is intended to help. This case-based review is intended to point out specific factors practicing radiologists who intend to use AI should consider.

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