Artificial Intelligence in Screening Mammography: A Population Survey of Women’s Preferences
Open accessAbstract
OBJECTIVE: To investigate the general population's view on the use of artificial intelligence (AI) for the diagnostic interpretation of screening mammograms. METHODS: Dutch women aged 16 to 75 years were surveyed using the Longitudinal Internet Studies for the Social sciences panel, representative for the Dutch population. Attitude toward AI in mammography screening was measured by means of five items: necessity of a human check; AI as a selector for second reading; AI as a second reader; developer is responsible for error; and radiologist is responsible for error. RESULTS: Of the 922 participants included, 77.8% agreed with the necessity of a human check, whereas the item AI as a selector for a second reading was more heterogeneously answered, with 41.7% disagreement, 31.5% agreement, and 26.9% responding with "neither agree nor disagree." The item AI as a second reader was mostly responded with "neither agree nor disagree" (37.1%) and "agree" (37.6%), whereas the two last items on developer's and radiologist' responsibilities were mostly answered with "neither agree nor disagree" (44.6% and 39.2%, respectively). DISCUSSION: Despite recent breakthroughs in the diagnostic performance of AI algorithms for the interpretation of screening mammograms, the general population currently does not support a fully independent use of such systems without involving a radiologist. The combination of a radiologist as a first reader and an AI system as a second reader in a breast cancer screening program finds most support at present. Accountability in case of AI-related diagnostic errors in screening mammography is still an unresolved conundrum.
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Other studies show that patients want humans involved when AI is used, indicating a preference for assistive over autonomous uses of medical AI.
“Practically speaking, these results add to other studies which show that patients want humans involved when AI is used, indicating a preference for assistive over autonomous uses of medical AI (e.g., Ongena et al., 2021).”
From Medical AI and human dignity: Contrasting perceptions of human and artificially intelligent (AI) decision making in diagnostic and medical resource allocation contexts (2022) · cites this paper as Ongena et al., 2021
There is a preference for the use of medical AI only in assistive and backup roles to humans.
“and the preference for the use of medical AI only in assistive and backup roles to humans (e.g., Ongena et al., 2021).”
From Medical AI and human dignity: Contrasting perceptions of human and artificially intelligent (AI) decision making in diagnostic and medical resource allocation contexts (2022) · cites this paper as Ongena et al., 2021
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Hemphill, S., Jackson, K., Bradley, S., & Bhartia, B. (2023) · Future Healthcare Journal · doi:10.7861/fhj.2022-0097 · Cited by 16 · see citing works
- Authors
- Ongena, Y. P., Yakar, D., Haan, M., & Kwee, T. C.
- Affiliations
- University of Groningen (NL)
- University Medical Center Groningen (NL)
- Corresponding author
- Ongena, Yfke P.
- Venue
- Journal of the American College of Radiology
- Publisher
- Elsevier BV
- Published
- 2021-01
- Volume
- 18
- Issue
- 1
- Pages
- 79-86
- ISSN
- 1546-1440 (ISSN-L)
- OpenAlex
- W3093064335
- Cited by
- 105
- References
- 22
- Citations by year
- 2026: 11 · 2025: 26 · 2024: 30 · 2023: 15 · 2022: 15 · 2021: 8
- FWCI
- 2.80
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of the American College of Radiology (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, cc-by-nc-nd)
- Funders
- Open Data Infrastructure for Social Science and Economic Innovations
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Digital Radiography and Breast Imaging
- Keywords
- artificial intelligence, mammography screening, public attitudes, breast cancer screening, human oversight, radiologist responsibility, women's preferences
- MeSH terms
- Radiologists, Artificial Intelligence, Breast Neoplasms, Female, Humans, Mammography, Mass Screening, Early Detection of Cancer
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