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European Radiology

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Open Access 02-09-2023 | Artificial Intelligence | Review

Barriers and facilitators of artificial intelligence conception and implementation for breast imaging diagnosis in clinical practice: a scoping review

Authors: Belinda Lokaj, Marie-Thérèse Pugliese, Karen Kinkel, Christian Lovis, Jérôme Schmid

Published in: European Radiology | Issue 3/2024

Abstract

Objective

Although artificial intelligence (AI) has demonstrated promise in enhancing breast cancer diagnosis, the implementation of AI algorithms in clinical practice encounters various barriers. This scoping review aims to identify these barriers and facilitators to highlight key considerations for developing and implementing AI solutions in breast cancer imaging.

Method

A literature search was conducted from 2012 to 2022 in six databases (PubMed, Web of Science, CINHAL, Embase, IEEE, and ArXiv). The articles were included if some barriers and/or facilitators in the conception or implementation of AI in breast clinical imaging were described. We excluded research only focusing on performance, or with data not acquired in a clinical radiology setup and not involving real patients.

Results

A total of 107 articles were included. We identified six major barriers related to data (B1), black box and trust (B2), algorithms and conception (B3), evaluation and validation (B4), legal, ethical, and economic issues (B5), and education (B6), and five major facilitators covering data (F1), clinical impact (F2), algorithms and conception (F3), evaluation and validation (F4), and education (F5).

Conclusion

This scoping review highlighted the need to carefully design, deploy, and evaluate AI solutions in clinical practice, involving all stakeholders to yield improvement in healthcare.

Clinical relevance statement

The identification of barriers and facilitators with suggested solutions can guide and inform future research, and stakeholders to improve the design and implementation of AI for breast cancer detection in clinical practice.

Key Points

• Six major identified barriers were related to data; black-box and trust; algorithms and conception; evaluation and validation; legal, ethical, and economic issues; and education.

• Five major identified facilitators were related to data, clinical impact, algorithms and conception, evaluation and validation, and education.

• Coordinated implication of all stakeholders is required to improve breast cancer diagnosis with AI.

Available only for authorised users

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Title: Barriers and facilitators of artificial intelligence conception and implementation for breast imaging diagnosis in clinical practice: a scoping review
Authors: Belinda Lokaj
Marie-Thérèse Pugliese
Karen Kinkel
Christian Lovis
Jérôme Schmid
Publication date: 02-09-2023
Publisher: Springer Berlin Heidelberg
Keywords: Artificial Intelligence
Mammography
Mammography
Published in: European Radiology / Issue 3/2024
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-023-10181-6

2024 ASCO Annual Meeting

Springer Medicine

Barriers and facilitators of artificial intelligence conception and implementation for breast imaging diagnosis in clinical practice: a scoping review

Abstract

Objective

Method

Results

Conclusion

Clinical relevance statement

Key Points

2024 ASCO Annual Meeting

Springer Medicine

Abstract

Objective

Method

Results

Conclusion

Clinical relevance statement

Key Points

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