Toward fairness in artificial intelligence for medical image analysis: identification and mitigation of potential biases in the roadmap from data collection to model deployment-Reference-Cited by-同舟云学术

Toward fairness in artificial intelligence for medical image analysis: identification and mitigation of potential biases in the roadmap from data collection to model deployment

Published:2023-04-26 Issue:06 Volume:10 Page:
ISSN:2329-4302
Container-title:Journal of Medical Imaging
language:
Short-container-title:J. Med. Imag.

Author:

Drukker Karen¹,Chen Weijie²,Gichoya Judy³,Gruszauskas Nicholas¹,Kalpathy-Cramer Jayashree⁴,Koyejo Sanmi⁵,Myers Kyle⁶,Sá Rui C.⁷,Sahiner Berkman²,Whitney Heather¹,Zhang Zi⁸,Giger Maryellen¹

Affiliation:

1. The University of Chicago, Department of Radiology, Chicago, Illinois, United States

2. US Food and Drug Administration, Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, Silver Spring, Maryland, United States

3. Emory University, Department of Radiology, Atlanta, Georgia, United States

4. University of Colorado Anschutz, Department of Ophthalmology, Aurora, Colorado, United States

5. Stanford University, Department of Computer Science, Stanford, California, United States

6. Puente Solutions LLC, Phoenix, Arizona, United States

7. National Institutes of Health, Bethesda, Maryland, United States

8. Jefferson Health, Philadelphia, Pennsylvania, United States

Publisher

SPIE-Intl Soc Optical Eng

Subject

Radiology, Nuclear Medicine and imaging

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