Artificial Intelligence in Cardiovascular Imaging: “Unexplainable” Legal and Ethical Challenges?
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.
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Opacity of an algorithm leads to ambiguity on whether the ML output can be trusted enough for the clinician to move forward with clinical decision-making or should be overridden due to lack of certitude or misalignment with traditional clinical judgment.
“This leads to ambiguity on whether the ML output can be trusted enough for the clinician to move forward with the clinical decision-making or should be overridden due to a lack of certitude or misalignment with traditional clinical judgment [80].”
From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [80]
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- Authors
- Lang, M., Bernier, A., & Knoppers, B. M.
- Affiliations
- McGill University (CA)
- Corresponding author
- Lang, Michael
- Knoppers, Bartha Maria
- Venue
- Canadian Journal of Cardiology
- Publisher
- Elsevier BV
- Published
- 2022-02
- Volume
- 38
- Issue
- 2
- Pages
- 225-233
- ISSN
- 0828-282X (ISSN-L)
- OpenAlex
- W3209048534
- Cited by
- 37
- References
- 63
- Citations by year
- 2026: 3 · 2025: 10 · 2024: 12 · 2023: 9 · 2022: 1 · 2021: 2
- FWCI
- 0.49
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Canadian Journal of Cardiology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Canada Research Chairs
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Law › Digitalization, Law, and Regulation
- Keywords
- artificial intelligence, cardiovascular imaging, explainable AI, deep learning, legal liability, medical ethics, health regulation, algorithmic transparency, AI adoption in healthcare
- MeSH terms
- Deep Learning, Artificial Intelligence, Cardiology, Cardiovascular Diseases, Delivery of Health Care, Diagnostic Techniques, Cardiovascular, Humans
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