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Purpose <p>To evaluate and compare patient perceptions of artificial intelligence (AI) use in mammogram interpretation across academic and safety-net healthcare settings.</p> Methods <p>We offered a 29-item survey to patients visiting our safety-net (SNH) and academic (ACH) hospital breast imaging clinics between 04/2024–06/2024 and 02/2023–08/2023, respectively.&#xa0;Demographic data was compared between populations using Chi-squared tests. We used ORs (95% CI) to estimate response odds by patient factors. Significant group differences were further analyzed via multivariable regression.</p> Results <p>A total of 924 [ACH: 518(56.1%), SNH: 406(43.9%)] surveys were collected. Participants from the ACH were older (≥ 70&#xa0;years: 20%vs3.1%, <i>p</i> &lt; 0.001), mostly identified as Non-Hispanic White (56%vs7.2%, p &lt; 0.001),&#xa0;had higher income (≥ $100,000: 49%vs3.2%, <i>p</i> &lt; 0.001),&#xa0;higher education (≥ college: 71%vs20%, <i>p</i> &lt; 0.001) and higher self-reported knowledge of AI (68%vs56%, <i>p</i> &lt; 0.001) compared to SNH. Use of AI alone or as a second reader&#xa0;was accepted by 74%, with SNH participants being less likely to accept [OR(95%CI): 0.71(0.53–0.96), p = 0.02]. SNH participants were more likely to request a reading by AI following radiologist-interpreted abnormalities [1.83(1.35–2.49), <i>p</i> &lt; 0.001], rate AI as the same or better than a radiologist at detecting cancer [1.54(1.12–2.15), <i>p</i> = 0.01], and have higher concern regarding data privacy [1.87(1.22–2.93), <i>p</i> = 0.01]. Higher education [1.99(1.33–2.99), <i>p</i> &lt; 0.001] and self-reported AI knowledge&#xa0;[1.98(1.38–2.83), p &lt; 0.001] were associated with higher acceptance of AI use, while Non-Hispanic Black race [0.40(0.25–0.65), <i>p</i> &lt; 0.001] was associated with lower acceptance when controlled for other covariates.</p> Conclusion <p>Significant differences exist in patients’ views of AI between the demographically distinct academic and safety-net populations. Our study revealed lower educational attainment and Non-Hispanic Black race as independent factors associated with lower acceptance of AI.</p>

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Patient perspectives on artificial intelligence in mammography interpretation: a comparative survey study of safety-net and academic hospital settings

  • 期刊论文

  • 发表日期：

    2025年12月3日

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  • Jenifer Chisom Ogu,
  • B. Bersu Ozcan,
  • Basak E. Dogan,
  • Yin Xi,
  • Emily E. Knippa

摘要

Purpose

To evaluate and compare patient perceptions of artificial intelligence (AI) use in mammogram interpretation across academic and safety-net healthcare settings.

Methods

We offered a 29-item survey to patients visiting our safety-net (SNH) and academic (ACH) hospital breast imaging clinics between 04/2024–06/2024 and 02/2023–08/2023, respectively. Demographic data was compared between populations using Chi-squared tests. We used ORs (95% CI) to estimate response odds by patient factors. Significant group differences were further analyzed via multivariable regression.

Results

A total of 924 [ACH: 518(56.1%), SNH: 406(43.9%)] surveys were collected. Participants from the ACH were older (≥ 70 years: 20%vs3.1%, p < 0.001), mostly identified as Non-Hispanic White (56%vs7.2%, p < 0.001), had higher income (≥ $100,000: 49%vs3.2%, p < 0.001), higher education (≥ college: 71%vs20%, p < 0.001) and higher self-reported knowledge of AI (68%vs56%, p < 0.001) compared to SNH. Use of AI alone or as a second reader was accepted by 74%, with SNH participants being less likely to accept [OR(95%CI): 0.71(0.53–0.96), p = 0.02]. SNH participants were more likely to request a reading by AI following radiologist-interpreted abnormalities [1.83(1.35–2.49), p < 0.001], rate AI as the same or better than a radiologist at detecting cancer [1.54(1.12–2.15), p = 0.01], and have higher concern regarding data privacy [1.87(1.22–2.93), p = 0.01]. Higher education [1.99(1.33–2.99), p < 0.001] and self-reported AI knowledge [1.98(1.38–2.83), p < 0.001] were associated with higher acceptance of AI use, while Non-Hispanic Black race [0.40(0.25–0.65), p < 0.001] was associated with lower acceptance when controlled for other covariates.

Conclusion

Significant differences exist in patients’ views of AI between the demographically distinct academic and safety-net populations. Our study revealed lower educational attainment and Non-Hispanic Black race as independent factors associated with lower acceptance of AI.