Avsnitt
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This interview is reproduced with the kind permission of Dr. Maxwell Cooper, host of the DaVinci Hour podcasts. Dr. Cooper interviews John Banja on various topics related to the ethical dimension of AI in radiology and on medical error in radiology. Please visit Dr. Cooper’s DaVinci Hour podcasts at https://podcasts.apple.com/us/podcast/the-davinci-hour/id1554398921.
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This interview focuses on a variety of ethical vulnerabilities that big data in AI presents. Dr. Amy Kotsenas offers recommendations for better protecting data privacy in the age of AI. See her lead author article on “Rethinking patient consent in the era of artificial intelligence and big data” at https://www.jacr.org/article/S1546-1440(20)30965-0/fulltext.
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Saknas det avsnitt?
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Dr. Hari Trivedi discusses a range of issues on the economics of improving and importing AI technology along with his envisioning the near future of AI business models in radiology. See his article in the Journal of the American College of Radiology at https://www.jacr.org/article/S1546-1440(22)00113-2/fulltext
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In this podcast Joshua Robinson discusses his work at MIT and his recent, lead author paper on how contrastive learning might lead to more reliable predictions in AI. Josh’s paper is at the NeurIPS proceedings website:
https://papers.nips.cc/paper/2021/hash/27934a1f19d678a1377c257b9a780e80-Abstract.html.
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Can AI relieve some of the problems involving the social determinants of health? This podcast discusses these and other aspects of health disparities in the technological age.
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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.
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Drs. Carolyn Meltzer and Adam Alessio comment on the phenomenon of bias in artificial intelligence models. Their conversation focuses on the inevitability of bias, the difficulties that are confronted in eliminating it, and the state of the art in mitigation techniques.
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Dr. Nabile Safdar, Vice Chair of Imaging Informatics at the Department of Radiology and Imaging Sciences at Emory University School of Medicine, comments on the phenomenon of sharing and selling images in radiology. The discussion focuses on ethical and regulatory expectations, securing authorization from data subjects, and important considerations that radiology practices should contemplate when they are invited to participate in sharing or selling arrangements.
https://www.jacr.org/article/S1546-1440(20)30843-7/pdf
