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

TL;DR

This paper reviews the generalization of AI models in medical imaging, emphasizing the importance for radiologists to understand development, data, and deployment considerations to ensure safe and effective use.

Contribution

It provides a case-based overview highlighting key factors radiologists should consider when applying AI in medical imaging.

Findings

Identifies common pitfalls in AI model generalization.

Highlights importance of understanding training data and deployment settings.

Provides practical guidance for radiologists using AI tools.

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.