Patient Perception of Artificial Intelligence Use in Interpretation of Screening Mammograms: A Survey Study.

Journal: Radiology. Imaging cancer
PMID:

Abstract

Purpose To assess patient perceptions of artificial intelligence (AI) use in the interpretation of screening mammograms. Materials and Methods In a prospective, institutional review board-approved study, all patients undergoing mammography screening at the authors' institution between February 2023 and August 2023 were offered a 29-question survey. Age, race and ethnicity, education, income level, and history of breast cancer and biopsy were collected. Univariable and multivariable logistic regression analyses were used to identify the independent factors associated with participants' acceptance of AI use. Results Of the 518 participants, the majority were between the ages of 40 and 69 years (377 of 518, 72.8%), at least college graduates (347 of 518, 67.0%), and non-Hispanic White (262 of 518, 50.6%). Participant-reported knowledge of AI was none or minimal in 76.5% (396 of 518). Stand-alone AI interpretation was accepted by 4.44% (23 of 518), whereas 71.0% (368 of 518) preferred AI to be used as a second reader. After an AI-reported abnormal screening, 88.9% (319 of 359) requested radiologist review versus 51.3% (184 of 359) of radiologist recall review by AI ( < .001). In cases of discrepancy, higher rate of participants would undergo diagnostic examination for radiologist recalls compared with AI recalls (94.2% [419 of 445] vs 92.6% [412 of 445]; = .20]. Higher education was associated with higher AI acceptance (odds ratio [OR] 2.05, 95% CI: 1.31, 3.20; = .002). Race was associated with higher concern for bias in Hispanic versus non-Hispanic White participants (OR 3.32, 95% CI: 1.15, 9.61; = .005) and non-Hispanic Black versus non-Hispanic White participants (OR 4.31, 95% CI: 1.50, 12.39; = .005). Conclusion AI use as a second reader of screening mammograms was accepted by participants. Participants' race and education level were significantly associated with AI acceptance. Breast, Mammography, Artificial Intelligence Published under a CC BY 4.0 license.

Authors

  • B Bersu Ozcan
    The University of Texas Southwestern Medical Center, Department of Radiology, Dallas, TX, USA.
  • Basak E Dogan
    The University of Texas Southwestern Medical Center, Department of Radiology, Dallas, TX, USA.
  • Yin Xi
    Departments of Radiology (T.J.O., Y.X., E.S., T.B., Y.S.N., R.M.P.) and Health Systems Information Resources (C.B.), University of Texas Southwestern Medical Center at Dallas, Dallas, Texas, 5323 Harry Hines Blvd, Dallas TX 75235.
  • Emily E Knippa
    Department of Radiology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, MC 8896, Dallas, TX 75390-8896.