Literature DB >> 32208097

Ethics of Using and Sharing Clinical Imaging Data for Artificial Intelligence: A Proposed Framework.

David B Larson1, David C Magnus1, Matthew P Lungren1, Nigam H Shah1, Curtis P Langlotz1.   

Abstract

In this article, the authors propose an ethical framework for using and sharing clinical data for the development of artificial intelligence (AI) applications. The philosophical premise is as follows: when clinical data are used to provide care, the primary purpose for acquiring the data is fulfilled. At that point, clinical data should be treated as a form of public good, to be used for the benefit of future patients. In their 2013 article, Faden et al argued that all who participate in the health care system, including patients, have a moral obligation to contribute to improving that system. The authors extend that framework to questions surrounding the secondary use of clinical data for AI applications. Specifically, the authors propose that all individuals and entities with access to clinical data become data stewards, with fiduciary (or trust) responsibilities to patients to carefully safeguard patient privacy, and to the public to ensure that the data are made widely available for the development of knowledge and tools to benefit future patients. According to this framework, the authors maintain that it is unethical for providers to "sell" clinical data to other parties by granting access to clinical data, especially under exclusive arrangements, in exchange for monetary or in-kind payments that exceed costs. The authors also propose that patient consent is not required before the data are used for secondary purposes when obtaining such consent is prohibitively costly or burdensome, as long as mechanisms are in place to ensure that ethical standards are strictly followed. Rather than debate whether patients or provider organizations "own" the data, the authors propose that clinical data are not owned at all in the traditional sense, but rather that all who interact with or control the data have an obligation to ensure that the data are used for the benefit of future patients and society. © RSNA, 2020 See also the editorial by Krupinski in this issue.

Entities:  

Year:  2020        PMID: 32208097     DOI: 10.1148/radiol.2020192536

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  18 in total

1.  Interinstitutional Portability of a Deep Learning Brain MRI Lesion Segmentation Algorithm.

Authors:  Andreas M Rauschecker; Tyler J Gleason; Pierre Nedelec; Michael Tran Duong; David A Weiss; Evan Calabrese; John B Colby; Leo P Sugrue; Jeffrey D Rudie; Christopher P Hess
Journal:  Radiol Artif Intell       Date:  2021-11-10

2.  Forging Connections in Latin America to Advance AI in Radiology.

Authors:  Felipe Campos Kitamura; Felipe Barjud Pereira do Nascimento; Guillermo Elizondo-Riojas; Hernán Chaves; Héctor Henríquez Leighton; Emmanuel Salinas-Miranda; Thiago Júlio; Antônio José da Rocha; César Higa Nomura
Journal:  Radiol Artif Intell       Date:  2022-08-31

Review 3.  Big data requirements for artificial intelligence.

Authors:  Sophia Y Wang; Suzann Pershing; Aaron Y Lee
Journal:  Curr Opin Ophthalmol       Date:  2020-09       Impact factor: 3.761

4.  Semisupervised Training of a Brain MRI Tumor Detection Model Using Mined Annotations.

Authors:  Nathaniel C Swinburne; Vivek Yadav; Julie Kim; Ye R Choi; David C Gutman; Jonathan T Yang; Nelson Moss; Jacqueline Stone; Jamie Tisnado; Vaios Hatzoglou; Sofia S Haque; Sasan Karimi; John Lyo; Krishna Juluru; Karl Pichotta; Jianjiong Gao; Sohrab P Shah; Andrei I Holodny; Robert J Young
Journal:  Radiology       Date:  2022-01-18       Impact factor: 11.105

Review 5.  Prospective Deployment of Deep Learning in MRI: A Framework for Important Considerations, Challenges, and Recommendations for Best Practices.

Authors:  Akshay S Chaudhari; Christopher M Sandino; Elizabeth K Cole; David B Larson; Garry E Gold; Shreyas S Vasanawala; Matthew P Lungren; Brian A Hargreaves; Curtis P Langlotz
Journal:  J Magn Reson Imaging       Date:  2020-08-24       Impact factor: 5.119

Review 6.  AI in health and medicine.

Authors:  Pranav Rajpurkar; Emma Chen; Oishi Banerjee; Eric J Topol
Journal:  Nat Med       Date:  2022-01-20       Impact factor: 87.241

Review 7.  Radiology artificial intelligence, a systematic evaluation of methods (RAISE): a systematic review protocol.

Authors:  Brendan Kelly; Conor Judge; Stephanie M Bollard; Simon M Clifford; Gerard M Healy; Kristen W Yeom; Aonghus Lawlor; Ronan P Killeen
Journal:  Insights Imaging       Date:  2020-12-09

Review 8.  Artificial intelligence extension of the OSCAR-IB criteria.

Authors:  Axel Petzold; Philipp Albrecht; Laura Balcer; Erik Bekkers; Alexander U Brandt; Peter A Calabresi; Orla Galvin Deborah; Jennifer S Graves; Ari Green; Pearse A Keane; Jenny A Nij Bijvank; Josemir W Sander; Friedemann Paul; Shiv Saidha; Pablo Villoslada; Siegfried K Wagner; E Ann Yeh
Journal:  Ann Clin Transl Neurol       Date:  2021-05-19       Impact factor: 4.511

Review 9.  The Evolution of Diabetic Retinopathy Screening Programmes: A Chronology of Retinal Photography from 35 mm Slides to Artificial Intelligence.

Authors:  Josef Huemer; Siegfried K Wagner; Dawn A Sim
Journal:  Clin Ophthalmol       Date:  2020-07-20

10.  Protecting Data Privacy in the Age of AI-Enabled Ophthalmology.

Authors:  Elysse Tom; Pearse A Keane; Marian Blazes; Louis R Pasquale; Michael F Chiang; Aaron Y Lee; Cecilia S Lee
Journal:  Transl Vis Sci Technol       Date:  2020-07-06       Impact factor: 3.283

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.