Establishing a Validation Infrastructure for Imaging-Based Artificial Intelligence Algorithms Before Clinical Implementation

doi:10.1016/j.jacr.2024.04.027

2024-10-01

PRE

DOI：10.1016/j.jacr.2024.04.027

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摘要

En 中文

With promising artificial intelligence (AI) algorithms receiving FDA clearance, the potential impact of these models on clinical outcomes must be evaluated locally before their integration into routine workflows. Robust validation infrastructures are pivotal to inspecting the accuracy and generalizability of these deep learning algorithms to ensure both patient safety and health equity. Protected health information concerns, intellectual property rights, and diverse requirements of models impede the development of rigorous external validation infrastructures. The authors propose various suggestions for addressing the challenges associated with the development of efficient, customizable, and cost-effective infrastructures for the external validation of AI models at large medical centers and institutions. The authors present comprehensive steps to establish an AI inferencing infrastructure outside clinical systems to examine the local performance of AI algorithms before health practice or systemwide implementation and promote an evidence-based approach for adopting AI models that can enhance radiology workflows and improve patient outcomes.

Keyword:

Artificial intelligence

deep learning

external validation

clinical implementation

radiologist work fl ow

期刊

Journal of the American College of Radiology

IF:

5.1

论文数:

5.6K

被引数:

8.0K

机构

University of Washington

学者数:

8.0W

论文数: 7.0W

被引数: 12.5W

University of California System

学者数:

37.7W

论文数: 33.8W

被引数: 6.6K

引用论文

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