Ethical Challenges and Opportunities in Applying Artificial Intelligence to Cardiovascular Medicine
Open accessAbstract
Much anticipation surrounds artificial intelligence's (AI) emergence as a promising tool in health care. It offers potential to revolutionise clinical practice through assistive and autonomous operation. The high prevalence of cardiac disease globally provides an opportunity for AI technology to increase health care efficiency and improve patient outcomes. This article explores the ethical considerations necessary for safe and acceptable implantation of AI within the health care space. We aim to highlight several challenges such as data privacy, consent, sustainability, and cybersecurity. In addition, we outline the future opportunities for AI use in cardiovascular medicine. Overall, we argue that AI deployment demands robust regulation, transparent algorithms, and safeguarding of patient privacy.
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Uneven performance in gatekeeper use could translate into unequal access to imaging.
“This issue is especially important for gatekeeper use, where uneven performance could translate into unequal access to imaging.”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [62]
The “Minimisation of bias” section says that imprecise cardiovascular risk assessments for underrepresented groups can cause over- or underestimation of risk and inappropriate testing. That supports the concern about uneven performance affecting testing, but the paper does not specifically discuss gatekeeper use or unequal access to imaging. (LLM, conf=0.82)
Experimental — accuracy not yet measuredInterpretability tools may make model behavior more inspectable, but they do not by themselves resolve questions of accountability, acceptable error, or downstream clinical action in opportunistic screening.
“Interpretability tools may make model behavior more inspectable, but they do not by themselves resolve questions of accountability, acceptable error, or downstream clinical action in opportunistic screening.”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [62]
The Transparency and explainability section says interpretability tools can clarify aspects of model decisions, while Responsibility and accountability says responsibility for those decisions remains with clinicians and other parties. Promoting human health and the public interest also discusses balancing misdiagnosis and overdiagnosis risks against follow-up and actionability for incidental findings. The copy does not specifically frame these points as acceptable error in opportunistic screening. (LLM, conf=0.82)
Experimental — accuracy not yet measuredFairness is not only a model-performance issue; it is also shaped by ECG acquisition quality, care setting, access to confirmatory echocardiography, and linkage to disease-specific treatment.
“Fairness is not only a model-performance issue; it is also shaped by ECG acquisition quality, care setting, access to confirmatory echocardiography, and linkage to disease-specific treatment.”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [62]
The “Minimisation of bias” section says fairness depends on more than model performance, including historically unequal access to care. “The case for AI: The promises and the peril” discusses access to echocardiographic screening in remote communities. Neither section links fairness specifically to ECG acquisition quality, care setting, confirmatory echocardiography, or access to disease-specific treatment; no page numbers are available in our copy. (LLM, conf=0.83)
Experimental — accuracy not yet measuredEthical considerations surrounding AI-CAC include bias, privacy and consent, transparency and explainability.
“Ethical considerations surrounding AI-CAC include bias, privacy and consent, transparency and explainability.59”
From Prognostication and clinical opportunities with AI for coronary artery calcium: a scoping review (2026) · cites this paper as [59]
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- Authors
- Lewin, S., Chetty, R., Ihdayhid, A. R., & Dwivedi, G.
- Affiliations
- Fiona Stanley Hospital (AU)
- Curtin University (AU)
- The University of Western Australia (AU)
- Corresponding author
- Dwivedi, Girish
- Venue
- Canadian Journal of Cardiology
- Publisher
- Elsevier BV
- Published
- 2024-10
- Volume
- 40
- Issue
- 10
- Pages
- 1897-1906
- ISSN
- 0828-282X (ISSN-L)
- OpenAlex
- W4400844643
- Cited by
- 36
- References
- 56
- Citations by year
- 2026: 8 · 2025: 24 · 2024: 4
- FWCI
- 0.82
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Canadian Journal of Cardiology (publishedVersion, cc-by)
- UWA Profiles and Research Repository (UWA) (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- artificial intelligence, AI ethics, data privacy, informed consent, sustainability, cybersecurity, AI regulation, algorithmic transparency, patient privacy, clinical decision support
- MeSH terms
- Artificial Intelligence, Cardiology, Cardiovascular Diseases, Confidentiality, Humans, Computer Security
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