The DOI matches its Crossref record; 61 of the 62 references it cites were found in a registry or an index.
Adoption of artificial intelligence in breast imaging: evaluation, ethical constraints and limitations
Open accessAbstract
Retrospective studies have shown artificial intelligence (AI) algorithms can match as well as enhance radiologist’s performance in breast screening. These tools can facilitate tasks not feasible by humans such as the automatic triage of patients and prediction of treatment outcomes. Breast imaging faces growing pressure with the exponential growth in imaging requests and a predicted reduced workforce to provide reports. Solutions to alleviate these pressures are being sought with an increasing interest in the adoption of AI to improve workflow efficiency as well as patient outcomes. Vast quantities of data are needed to test and monitor AI algorithms before and after their incorporation into healthcare systems. Availability of data is currently limited, although strategies are being devised to harness the data that already exists within healthcare institutions. Challenges that underpin the realisation of AI into everyday breast imaging cannot be underestimated and the provision of guidance from national agencies to tackle these challenges, taking into account views from a societal, industrial and healthcare prospective is essential. This review provides background on the evaluation and use of AI in breast imaging in addition to exploring key ethical, technical, legal and regulatory challenges that have been identified so far.
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“The presence of AI experts, perhaps a multidisciplinary team, particularly with clinical scientists, data science and subject matter experts with AI skills, an innovation manager, AI experts to provide training, and local champions within departments involved in the end-to-end process, was considered an important element for adoption [32,57,59,62,71].”
From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [71]
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“Another facilitator of trust is the engagement of patients in the development of AI.”
From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [71]
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“Hickman et al [71], in particular, noted the lack of technological (infrastructure) maturity to allow for the integration of AI.”
From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [71]
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LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
The FAIR Guiding Principles for scientific data management and stewardship
Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., et al. (2016) · Scientific Data · doi:10.1038/sdata.2016.18 · Cited by 18,423
Artificial intelligence in radiology
Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H., & Aerts, H. J. W. L. (2018) · Nature Reviews Cancer · doi:10.1038/s41568-018-0016-5 · Cited by 3,803
International evaluation of an AI system for breast cancer screening
McKinney, S. M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., Back, T., Chesus, M., Corrado, G. S., Darzi, A., et al. (2020) · Nature · doi:10.1038/s41586-019-1799-6 · Cited by 3,287
Self-citationLiu, X., Faes, L., Kale, A. U., Wagner, S. K., Fu, D. J., Bruynseels, A., Mahendiran, T., Moraes, G., Shamdas, M., Kern, C., et al. (2019) · The Lancet Digital Health · doi:10.1016/s2589-7500(19)30123-2 · Cited by 1,883
Checklist for Artificial Intelligence in Medical Imaging (CLAIM): a guide for authors and reviewers
Mongan, J., Moy, L., & Kahn, C. E. (2020) · Radiology: Artificial Intelligence · doi:10.1148/ryai.2020200029 · Cited by 1,443
Liu, X., Cruz Rivera, S., Moher, D., Calvert, M. J., Denniston, A. K., The SPIRIT-AI and CONSORT-AI Working Group, SPIRIT-AI and CONSORT-AI Steering Group, Chan, A.-W., Darzi, A., Holmes, C., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-1034-x · Cited by 1,421
Moreira, I. C., Amaral, I., Domingues, I., Cardoso, A. N. L., Cardoso, M. J., & Cardoso, J. S. (2011) · Academic Radiology · doi:10.1016/j.acra.2011.09.014 · Cited by 1,256
The Mammographic Image Analysis Society digital mammogram database
John Suckling, A., Parker, J., Astley, S., Hutt, I., Boggis, C. R. M., Ricketts, I. W., Stamatakis, E. A., Cerneaz, N., Kok, S., Taylor, P., et al. (1994) · Cited by 1,155
Nagendran, M., Chen, Y., Lovejoy, C. A., Gordon, A. C., Komorowski, M., Harvey, H., Topol, E. J., Ioannidis, J. P. A., Collins, G. S., & Maruthappu, M. (2020) · BMJ · doi:10.1136/bmj.m689 · Cited by 1,152
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1 of the 62 references couldn’t be matched to a known paper.
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Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging
Najjar, R. (2023) · Diagnostics · doi:10.3390/diagnostics13172760 · Cited by 1,025
Dembrower, K., Crippa, A., Colón, E., Eklund, M., & Strand, F. (2023) · The Lancet Digital Health · doi:10.1016/s2589-7500(23)00153-x · Cited by 397
Barriers to and facilitators of artificial intelligence adoption in health care: scoping review
Hassan, M., Kushniruk, A., & Borycki, E. (2024) · JMIR Human Factors · doi:10.2196/48633 · Cited by 435
Artificial Intelligence in Breast Cancer Diagnosis and Personalized Medicine
Ahn, J. S., Shin, S., Yang, S., Park, E. K., Kim, K. H., Cho, S. I., Ock, C., & Kim, S. (2023) · Journal of Breast Cancer · doi:10.4048/jbc.2023.26.e45 · Cited by 281
Saheb, T., Saheb, T., & Carpenter, D. O. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104660 · Cited by 148
Metadata
- Authors
- Hickman, S. E., Baxter, G. C., & Gilbert, F. J.
- Affiliations
- University of Cambridge (GB)
- Corresponding author
- Gilbert, Fiona J.
- Venue
- British Journal of Cancer
- Published
- 2021-03-26
- Volume
- 125
- Issue
- 1
- Pages
- 15-22
- ISSN
- 0007-0920 (ISSN-L)
- 1532-1827
- PMC
- PMC8257639
- OpenAlex
- W3136635219
- Cited by
- 110
- References
- 62
- Citations by year
- 2026: 12 · 2025: 23 · 2024: 21 · 2023: 27 · 2022: 21 · 2021: 6
- FWCI
- 1.34
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- British Journal of Cancer (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- Apollo (University of Cambridge) (submittedVersion)
- Apollo
- Funders
- Cancer Research UK (C543/A26884)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Digital Radiography and Breast Imaging
- Keywords
- artificial intelligence, breast imaging, breast cancer screening, artificial intelligence adoption, AI evaluation, ethical considerations, legal and regulatory challenges, data availability, radiologist workload, triage, treatment outcome prediction, workflow efficiency
- MeSH terms
- Artificial Intelligence, Breast, Female, Humans, Prospective Studies, Radiographic Image Interpretation, Computer-Assisted, Retrospective Studies, Practice Guidelines as Topic
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