Literature DB >> 34737036

Artificial Intelligence in Cardiovascular Imaging: "Unexplainable" Legal and Ethical Challenges?

Michael Lang1, Alexander Bernier2, Bartha Maria Knoppers3.   

Abstract

Nowhere is the influence of artificial intelligence (AI) likely to be more profoundly felt than in health care, from patient triage and diagnosis to surgery and follow-up. Over the medium-term, these effects will be more acute in the cardiovascular imaging context, in which AI models are already successfully performing at approximately human levels of accuracy and efficiency in certain applications. Yet, the adoption of unexplainable AI systems for cardiovascular imaging still raises significant legal and ethical challenges. We focus in particular on challenges posed by the unexplainable character of deep learning and other forms of sophisticated AI modelling used for cardiovascular imaging by briefly outlining the systems being developed in this space, describing how they work, and considering how they might generate outputs that are not reviewable by physicians or system programmers. We suggest that an unexplainable tendency presents 2 specific ethico-legal concerns: (1) difficulty for health regulators; and (2) confusion about the assignment of liability for error or fault in the use of AI systems. We suggest that addressing these concerns is critical for ensuring AI's successful implementation in cardiovascular imaging.
Copyright © 2021 Canadian Cardiovascular Society. Published by Elsevier Inc. All rights reserved.

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Year:  2021        PMID: 34737036     DOI: 10.1016/j.cjca.2021.10.009

Source DB:  PubMed          Journal:  Can J Cardiol        ISSN: 0828-282X            Impact factor:   5.223


  2 in total

1.  Automatic Detection of Image-Based Features for Immunosuppressive Therapy Response Prediction in Oral Lichen Planus.

Authors:  Ziang Xu; Qi Han; Dan Yang; Yijun Li; Qianhui Shang; Jiaxin Liu; Weiqi Li; Hao Xu; Qianming Chen
Journal:  Front Immunol       Date:  2022-06-23       Impact factor: 8.786

Review 2.  Digital Technology Application for Improved Responses to Health Care Challenges: Lessons Learned From COVID-19.

Authors:  Darshan H Brahmbhatt; Heather J Ross; Yasbanoo Moayedi
Journal:  Can J Cardiol       Date:  2021-12-01       Impact factor: 5.223

  2 in total

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