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Generalization of Artificial Intelligence Models in Medical Imaging: A Case-Based Review (2211.13230v1)
Published 15 Nov 2022 in eess.IV and cs.CV
Abstract: The discussions around 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.
- Rishi Gadepally (1 paper)
- Andrew Gomella (1 paper)
- Eric Gingold (1 paper)
- Paras Lakhani (3 papers)