Bias, Fairness and Generalizability

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AI, Radiology and Ethics: A Podcast Series

Emory University Center for Ethics

19 episodes

3 days ago

The most recent generation of artificial intelligence technologies is introducing a host of ethical problems to radiology practice and health care delivery. Join Dr. John Banja, a medical ethicist at Emory University’s Center for Ethics, and his guests discuss the ethical challenges that artificial intelligence presents. This podcast is made possible by grants from the Advanced Radiology Services Foundation and the National Institutes of Health National Center for Advancing Translational Sciences. For articles and research, please visit https://bit.ly/3vtFtI2 Questions? Email jbanja@emory.edu

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Bias, Fairness and Generalizability

AI, Radiology and Ethics: A Podcast Series

16 minutes 7 seconds

4 years ago

Bias, Fairness and Generalizability

Dr. Leo Celi discusses various problems involving bias, fairness and generalizability that continue to affect the adoption of artificial intelligence models in hospitals and clinics. Dr. Celi also makes a number of recommendations for improving relationships between health care organizations and the private sector as AI research moves forward.

Articles that Dr. Celi mentions in the podcast are:

Futoma J, Simons M, Panch T, Doshi-Velez F, Celi L. The myth of generalizability in clinical research and machine learning in health care. Lancet Digital Health 2020; 2:e489-92. At: https://www.thelancet.com/journals/landig/article/PIIS2589-7500(20)30186-2/fulltext.

Stuppe A, Singerman D, Celi L. The reproducibility crisis in the age of digital medicine. NPJ Digital Medicine January 29, 2019. At https://www.nature.com/articles/s41746-019-0079-z.

Vyas D, Eisenstein L, Jones D. Hidden in plain sight—reconsidering the use of race correction in clinical algorithms. The New England Journal of Medicine August 27, 2020; 383(9):874-882. At: https://www.nejm.org/doi/full/10.1056/NEJMms2004740.