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AI, Radiology and Ethics: A Podcast Series

AI, Radiology and Ethics: A Podcast Series

By Emory University Center for Ethics
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 bit.ly/3vtFtI2
Questions? Email jbanja@emory.edu
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AI in Health Disparities, Vendor-Hospital Agreements, and Refugee Resettlement

AI, Radiology and Ethics: A Podcast Series

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Health Disparities Revisited: Hopes and Challenges in Medicine and Radiology Practice
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.
40:60
May 16, 2022
On Reducing Error in Clinical Care with Artificial Intelligence
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.
37:24
May 12, 2022
AI in Health Disparities, Vendor-Hospital Agreements, and Refugee Resettlement
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.
37:13
April 19, 2022
PREVIEW of Episode 10: On Pigeons, Residency Training, and the Development of Expertise.
A short snippet from Episode 10: On Pigeons, Residency Training, and the Development of Expertise for you to sample.
02:38
April 06, 2022
Avoiding Shortcut Solutions in Machine Learning Models
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.
35:44
March 14, 2022
On Pigeons, Residency Training, and the Development of Expertise.
In this podcast, Dr. Elizabeth Krupinsky at Emory University discusses some similarities between pigeon visual processing and humans as well as the development of expert performance in radiology.
41:37
November 15, 2021
On AI and Health Disparities with Dr. Bibb Allen
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.
32:54
November 08, 2021
It's Complicated: Reimbursing Radiology Services in the Age of AI
Melissa Chen, MD, from the Department of Diagnostic Radiology at the University of Texas MD Anderson Cancer Center discusses the complexities surrounding reimbursement for AI in radiology.
33:26
August 17, 2021
Preparing the Radiology Department or Clinic for the Future of AI
Dr. Charles Kahn, Professor and Vice Chair of Radiology at the University of Pennsylvania Perelman School of Medicine and editor of Radiology: Artificial Intelligence, discusses strategies for preparing the radiology department or clinic for the future of artificial intelligence.
27:51
July 19, 2021
AI in Radiology: The Role of Professional Organizations in Standards Development, Conflicts of Interest, and Bringing Radiology to the World
Dr. Geraldine McGinty and Ms. Michelle Yi discuss the roles of professional organizations in providing high-quality membership services and in maintaining the quality and distribution of radiology services throughout the world.
31:31
June 15, 2021
Implementing AI in Health Care Delivery: Perspectives from Executive Clinical Leadership
This podcast examines a number of issues related to the implementation of AI in our hospitals and clinics, especially in terms of how health care leaders and their executive committees might be responding to the development and marketing of these models.
35:13
May 27, 2021
How Will Artificial Intelligence Affect the Medical Malpractice Experience in Radiology?
Radiologists Michael Bruno and Richard Duszak discuss the potential impact of artificial intelligence on the medical malpractice landscape of radiology with medical ethicist John Banja.
35:25
February 22, 2021
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.
16:08
November 18, 2020
Bias: Confronting the Problem
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.
22:31
October 14, 2020
Sharing and Selling Images
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
14:05
September 07, 2020