TY - JOUR
T1 - Validating racial and ethnic non-bias of artificial intelligence decision support for diagnostic breast ultrasound evaluation
AU - Koo, Clara
AU - Yang, Anthony
AU - Welch, Colton
AU - Jadav, Vipashyana
AU - Posch, Liana
AU - Thoreson, Nicholas
AU - Morris, Darrell
AU - Chouhdry, Fatima
AU - Szabo, Janet
AU - Mendelson, David
AU - Margolies, Laurie R.
N1 - Publisher Copyright:
© 2023 Society of Photo-Optical Instrumentation Engineers (SPIE).
PY - 2023/11/1
Y1 - 2023/11/1
N2 - Purpose: Breast ultrasound suffers from low positive predictive value and specificity. Artificial intelligence (AI) proposes to improve accuracy, reduce false negatives, reduce inter- and intra-observer variability and decrease the rate of benign biopsies. Perpetuating racial/ethnic disparities in healthcare and patient outcome is a potential risk when incorporating AI-based models into clinical practice; therefore, it is necessary to validate its non-bias before clinical use. Approach: Our retrospective review assesses whether our AI decision support (DS) system demonstrates racial/ethnic bias by evaluating its performance on 1810 biopsy proven cases from nine breast imaging facilities within our health system from January 1, 2018 to October 28, 2021. Patient age, gender, race/ethnicity, AI DS output, and pathology results were obtained. Results: Significant differences in breast pathology incidence were seen across different racial and ethnic groups. Stratified analysis showed that the difference in output by our AI DS system was due to underlying differences in pathology incidence for our specific cohort and did not demonstrate statistically significant bias in output among race/ethnic groups, suggesting similar effectiveness of our AI DS system among different races (p > 0.05 for all). Conclusions: Our study shows promise that an AI DS system may serve as a valuable second opinion in the detection of breast cancer on diagnostic ultrasound without significant racial or ethnic bias. AI tools are not meant to replace the radiologist, but rather to aid in screening and diagnosis without perpetuating racial/ethnic disparities.
AB - Purpose: Breast ultrasound suffers from low positive predictive value and specificity. Artificial intelligence (AI) proposes to improve accuracy, reduce false negatives, reduce inter- and intra-observer variability and decrease the rate of benign biopsies. Perpetuating racial/ethnic disparities in healthcare and patient outcome is a potential risk when incorporating AI-based models into clinical practice; therefore, it is necessary to validate its non-bias before clinical use. Approach: Our retrospective review assesses whether our AI decision support (DS) system demonstrates racial/ethnic bias by evaluating its performance on 1810 biopsy proven cases from nine breast imaging facilities within our health system from January 1, 2018 to October 28, 2021. Patient age, gender, race/ethnicity, AI DS output, and pathology results were obtained. Results: Significant differences in breast pathology incidence were seen across different racial and ethnic groups. Stratified analysis showed that the difference in output by our AI DS system was due to underlying differences in pathology incidence for our specific cohort and did not demonstrate statistically significant bias in output among race/ethnic groups, suggesting similar effectiveness of our AI DS system among different races (p > 0.05 for all). Conclusions: Our study shows promise that an AI DS system may serve as a valuable second opinion in the detection of breast cancer on diagnostic ultrasound without significant racial or ethnic bias. AI tools are not meant to replace the radiologist, but rather to aid in screening and diagnosis without perpetuating racial/ethnic disparities.
KW - artificial intelligence
KW - breast cancer
KW - breast ultrasound
KW - deep learning
KW - racial and ethnicity bias
UR - http://www.scopus.com/inward/record.url?scp=85182357423&partnerID=8YFLogxK
U2 - 10.1117/1.JMI.10.6.061108
DO - 10.1117/1.JMI.10.6.061108
M3 - Article
AN - SCOPUS:85182357423
SN - 2329-4302
VL - 10
JO - Journal of Medical Imaging
JF - Journal of Medical Imaging
IS - 6
M1 - 061108
ER -