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Bridging the AI Chasm: Can EBM Address Representation and Fairness in Clinical Machine Learning?
American Journal of Bioethics 22 (5):30-32 (2022)
Abstract
McCradden et al. propose to close the “AI chasm” between algorithms and clinically meaningful application using the norms of evidence-based medicine and clinical research, with the rat...Author's Profile
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Citations of this work
Response to Open Peer Commentaries: On Social Harms, Big Tech, and Institutional Accountability.James A. Anderson, Melissa D. McCradden & Elizabeth A. Stephenson - 2022 - American Journal of Bioethics 22 (10):6-8.
Promoting Ethical Deployment of Artificial Intelligence and Machine Learning in Healthcare.Kayte Spector-Bagdady, Vasiliki Rahimzadeh, Kaitlyn Jaffe & Jonathan Moreno - 2022 - American Journal of Bioethics 22 (5):4-7.
References found in this work
A Research Ethics Framework for the Clinical Translation of Healthcare Machine Learning.Melissa D. McCradden, James A. Anderson, Elizabeth A. Stephenson, Erik Drysdale, Lauren Erdman, Anna Goldenberg & Randi Zlotnik Shaul - 2022 - American Journal of Bioethics 22 (5):8-22.
Mitigating Racial Bias in Machine Learning.Kristin M. Kostick-Quenet, I. Glenn Cohen, Sara Gerke, Bernard Lo, James Antaki, Faezah Movahedi, Hasna Njah, Lauren Schoen, Jerry E. Estep & J. S. Blumenthal-Barby - 2022 - Journal of Law, Medicine and Ethics 50 (1):92-100.
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