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Open Peer Commentaries

Bridging the AI Chasm: Can EBM Address Representation and Fairness in Clinical Machine Learning?

REFERENCES

  • Cho, M. K. 2021. Rising to the challenge of bias in health care AI. Nature Medicine 27 (12): 2079–81. doi:10.1038/s41591-021-01577-2.
  • Gianfrancesco, M. A., S. Tamang, J. Yazdany, and G. Schmajuk. 2018. Potential biases in machine learning algorithms using electronic health record data. JAMA Internal Medicine 178 (11): 1544–7. doi:10.1001/jamainternmed.2018.3763.
  • Jadad, A., and M. Enkin. 2007. Bias in randomized controlled trials. In Randomized controlled trials: Questions, answers and musings, 29–47. New York: John Wiley & Sons, Ltd. doi:10.1002/9780470691922.ch3.
  • Kauh, T. J., J. G. Read, and A. J. Scheitler. 2021. The critical role of racial/ethnic data disaggregation for health equity. Population Research and Policy Review 40 (1):1–7. doi:10.1007/s11113-020-09631-6.
  • Kostick-Quenet, K. M., I. G. Cohen, S. Gerke, B. Lo, J. Antaki, F. Movahedi, H. Njah, L. Schoen, J. E. Estep, and J. S. Blumenthal-Barby. 2022. Mitigating racial bias in machine learning. The Journal of Law, Medicine & Ethics 50 (1):92–100. doi:10.1017/jme.2022.13.
  • Lambert, J. 2011. Statistics in brief: How to assess bias in clinical studies? Clinical Orthopaedics and Related Research 469 (6):1794–6. doi:10.1007/s11999-010-1538-7.
  • McCradden, M. D., J. A. Anderson, E. A. Stephenson, E. Drysdale, L. Erdman, A. Goldenberg, and R. Zlotnik Shaul. 2022. A research ethics framework for the clinical translation of healthcare machine learning. The American Journal of Bioethics 22(5): 8–12. doi:10.1080/15265161.2021.2013977.
  • Miceli, M.,. J. Posada, and T. Yang. 2021. “Studying up machine learning data: Why talk about bias when we mean power?” ArXiv:2109.08131 [Cs], September. http://arxiv.org/abs/2109.08131.
  • Obermeyer, Z., B. Powers, C. Vogeli, and S. Mullainathan. 2019. Dissecting racial bias in an algorithm used to manage the health of populations. Science 366 (6464):447–53. doi:10.1126/science.aax2342.
  • Sica, G. T. 2006. Bias in research studies. Radiology 238 (3):780–9. doi:10.1148/radiol.2383041109.
  • Wu, E., K. Wu, R. Daneshjou, D. Ouyang, D. E. Ho, and J. Zou. 2021. How medical AI devices are evaluated: Limitations and recommendations from an analysis of FDA approvals. Nature Medicine 27 (4):582–4. doi:10.1038/s41591-021-01312-x.

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