Artificial Intelligence in Screening Mammography: How Do Patients Feel?

References

• 1. Xie Y, Zhai Y, Lu G. Evolution of artificial intelligence in healthcare: a 30-year bibliometric study. Front Med (Lausanne) 2024;11:1505692.
  Medline Google Scholar
• 2. Young AT, Amara D, Bhattacharya A, Wei ML. Patient and general public attitudes towards clinical artificial intelligence: a mixed methods systematic review. Lancet Digit Health 2021;3(9):e599–e611.
  Medline Google Scholar
• 3. Ozcan BB, Dogan BE, Xi Y, Knippa EE. Patient Perception of Artificial Intelligence Use in Interpretation of Screening Mammograms: A Survey Study. Radiol Imaging Cancer 2025;7(3):e240290.
  Google Scholar
• 4. Anhang Price R, Quigley DD, Hargraves JL, et al. A Systematic Review of Strategies to Enhance Response Rates and Representativeness of Patient Experience Surveys. Med Care 2022;60(12):910–918.
  Medline Google Scholar
• 5. Cestonaro C, Delicati A, Marcante B, Caenazzo L, Tozzo P. Defining medical liability when artificial intelligence is applied on diagnostic algorithms: a systematic review. Front Med (Lausanne) 2023;10:1305756.
  Medline Google Scholar
• 6. Gu T, Yuan J, White-Means S, Li M. Disparities and Gaps in Breast Cancer Screening for Women Aged 40 to 49 Years. JAMA Netw Open 2024;7(12):e2451827.
  Medline Google Scholar
• 7. Wheeler SB, Reeder-Hayes KE, Carey LA. Disparities in breast cancer treatment and outcomes: biological, social, and health system determinants and opportunities for research. Oncologist 2013;18(9):986–993.
  Medline Google Scholar
• 8. Arora A, Alderman JE, Palmer J, et al. The value of standards for health datasets in artificial intelligence-based applications. Nat Med 2023;29(11):2929–2938.
  Medline Google Scholar
• 9. Phalak K, Gerlach K, Parikh JR. Community outreach and integration of breast radiologists. Clin Imaging 2020;66:143–146.
  Medline Google Scholar