Scientific article
Open access

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

Published inEuropean radiology, vol. 34, no. 3, p. 2096-2109
Publication date2024-03
First online date2023-09-02

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 .

  • Artificial intelligence
  • Breast neoplasms
  • Deep learning
  • Diagnostic imaging
  • Algorithms
  • Artificial Intelligence
  • Breast
  • Breast Neoplasms / diagnostic imaging
  • Educational Status
  • Female
  • Humans
  • Swiss Cancer Research - SUBREAM project [KFS-5460–08-2021-R]
Citation (ISO format)
LOKAJ, Belinda et al. Barriers and facilitators of artificial intelligence conception and implementation for breast imaging diagnosis in clinical practice: a scoping review. In: European radiology, 2024, vol. 34, n° 3, p. 2096–2109. doi: 10.1007/s00330-023-10181-6
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ISSN of the journal0938-7994

Technical informations

Creation09/15/2023 8:34:23 PM
First validation02/20/2024 3:53:01 PM
Update time02/20/2024 3:53:01 PM
Status update02/20/2024 3:53:01 PM
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