Patients Are Generally Supportive of Artificial Intelligence in Breast Imaging: A Multisite Survey of Breast Imaging Patients.
설문조사
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TL;DR
Breast imaging patients have an overall favorable view of AI in breast cancer screening, with variable support by demographics, and education and outreach efforts should target perceived challenges to AI adoption to improve patient acceptance.
OpenAlex 토픽 ·
AI in cancer detection
Artificial Intelligence in Healthcare and Education
Radiomics and Machine Learning in Medical Imaging
Breast imaging patients have an overall favorable view of AI in breast cancer screening, with variable support by demographics, and education and outreach efforts should target perceived challenges to
- p-value P <.05
APA
Lars Grimm, Katerina Dodelzon, et al. (2026). Patients Are Generally Supportive of Artificial Intelligence in Breast Imaging: A Multisite Survey of Breast Imaging Patients.. Journal of breast imaging, 8(2), 169-177. https://doi.org/10.1093/jbi/wbaf066
MLA
Lars Grimm, et al.. "Patients Are Generally Supportive of Artificial Intelligence in Breast Imaging: A Multisite Survey of Breast Imaging Patients.." Journal of breast imaging, vol. 8, no. 2, 2026, pp. 169-177.
PMID
41686530 ↗
Abstract 한글 요약
[OBJECTIVE] To understand the perspective of patients undergoing breast imaging on the use of artificial intelligence (AI) in breast cancer screening.
[METHODS] A 36-item survey was administered to breast imaging patients at 6 academic and 2 private practice groups in the United States. The survey included questions regarding demographics, breast imaging history, and electronic health literacy. Respondents were asked Likert scale questions on the role of AI in breast cancer screening, the role of AI as an independent or complementary reader, and concerns regarding AI in breast imaging.
[RESULTS] The survey yielded 3532 responses, a response rate of 69.9% (3532/5053). The median age was 55.9 years (SD, 12.3 years), and most respondents were White (73.0%, 2679/3532). Respondents indicated support for the role of AI to identify suspicious findings (70.6%, 2492/3532), triage findings for review (69.5%, 2382/3532), calculate breast density (73.2%, 2588/3532), and estimate breast cancer risk (61.9%, 2186/3532). Significantly higher support was noted among patients who were White, had more education, and had greater health literacy (all P <.05). There was strong agreement that it was necessary for radiologists to also review each examination (67.3%, 376/3532). Respondents were uncertain about whether AI (41.2%, 1456/3532) or radiologists (31.8%, 1124/3532) were responsible for errors. There was concern that AI will limit communication between patients and radiologists (75.7%, 2673/3532).
[CONCLUSION] Breast imaging patients have an overall favorable view of AI in breast cancer screening, with variable support by demographics. Education and outreach efforts should target perceived challenges to AI adoption to improve patient acceptance.
[METHODS] A 36-item survey was administered to breast imaging patients at 6 academic and 2 private practice groups in the United States. The survey included questions regarding demographics, breast imaging history, and electronic health literacy. Respondents were asked Likert scale questions on the role of AI in breast cancer screening, the role of AI as an independent or complementary reader, and concerns regarding AI in breast imaging.
[RESULTS] The survey yielded 3532 responses, a response rate of 69.9% (3532/5053). The median age was 55.9 years (SD, 12.3 years), and most respondents were White (73.0%, 2679/3532). Respondents indicated support for the role of AI to identify suspicious findings (70.6%, 2492/3532), triage findings for review (69.5%, 2382/3532), calculate breast density (73.2%, 2588/3532), and estimate breast cancer risk (61.9%, 2186/3532). Significantly higher support was noted among patients who were White, had more education, and had greater health literacy (all P <.05). There was strong agreement that it was necessary for radiologists to also review each examination (67.3%, 376/3532). Respondents were uncertain about whether AI (41.2%, 1456/3532) or radiologists (31.8%, 1124/3532) were responsible for errors. There was concern that AI will limit communication between patients and radiologists (75.7%, 2673/3532).
[CONCLUSION] Breast imaging patients have an overall favorable view of AI in breast cancer screening, with variable support by demographics. Education and outreach efforts should target perceived challenges to AI adoption to improve patient acceptance.
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