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Review
. 2022 Sep 26;4(6):632-639.
doi: 10.1093/jbi/wbac065. eCollection 2022 Nov-Dec.

Artificial Intelligence in Clinical Practice: Implementation Considerations and Barriers

Manisha Bahl  1
Affiliations
Review

Artificial Intelligence in Clinical Practice: Implementation Considerations and Barriers

Manisha Bahl. J Breast Imaging. .

Abstract

The rapid growth of artificial intelligence (AI) in radiology has led to Food and Drug Administration clearance of more than 20 AI algorithms for breast imaging. The steps involved in the clinical implementation of an AI product include identifying all stakeholders, selecting the appropriate product to purchase, evaluating it with a local data set, integrating it into the workflow, and monitoring its performance over time. Despite the potential benefits of improved quality and increased efficiency with AI, several barriers, such as high costs and liability concerns, may limit its widespread implementation. This article lists currently available AI products for breast imaging, describes the key elements of clinical implementation, and discusses barriers to clinical implementation.

Keywords: artificial intelligence; breast imaging; implementation; mammography.

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Figures

Figure 1.
Figure 1.
Example of an artificial intelligence product for triage purposes. The algorithm flags mammographic examinations with at least one suspicious finding. Abbreviations: MG, mammography; MRN, medical record number.
Figure 2.
Figure 2.
Steps involved in clinical implementation of an artificial intelligence (AI) product.
See this image and copyright information in PMC

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