By Emory University Center for Ethics
5
11 ratings
The podcast currently has 19 episodes available.
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.
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.
Dr. Yvonne Lui from NYU discusses her radiology research on brain injuries and on growing a clinical and research program in artificial intelligence.
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
This podcast features Drs. Judy Gichoya and Leo Celi discussing how various biases in artificial intelligence models can affect radiology work. They also discuss certain strategies that might mitigate them.
In this podcast, Pelu Tran discusses how artificial intelligence can improve workflow and reduce various kinds of errors that occur in diagnosis and treatment planning.
In this podcast, Dr. Muhammed Idris talks about his work in using AI for improving self-management health-related behaviors as well as using AI for resettling refugees.
A short snippet from Episode 10: On Pigeons, Residency Training, and the Development of Expertise for you to sample.
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.
In this podcast, Dr. Elizabeth Krupinski at Emory University discusses some similarities between pigeon visual processing and humans as well as the development of expert performance in radiology.
The podcast currently has 19 episodes available.