Applications and implementation of generative artificial intelligence in cardiovascular imaging with a focus on ethical and legal considerations: what cardiovascular imagers need to know!
Open accessAbstract
Machine learning (ML) and deep learning (DL) have potential applications in medicine. This overview explores the applications of AI in cardiovascular imaging, focusing on echocardiography, cardiac MRI (CMR), coronary CT angiography (CCTA), and CT morphology and function. AI, particularly DL approaches like convolutional neural networks, enhances standardization in echocardiography. In CMR, undersampling techniques and DL-based reconstruction methods, such as variational neural networks, improve efficiency and accuracy. ML in CCTA aids in diagnosing coronary artery disease, assessing stenosis severity, and analyzing plaque characteristics. Automatic segmentation of cardiac structures and vessels using AI is discussed, along with its potential in congenital heart disease diagnosis and 3D printing applications. Overall, AI integration in cardiovascular imaging shows promise for enhancing diagnostic accuracy and efficiency across modalities. The growing use of Generative Adversarial Networks in cardiovascular imaging brings substantial advancements but raises ethical concerns. The “black box” problem in DL models poses challenges for interpretability crucial in clinical practice. Evaluation metrics like ROC curves, image quality, clinical relevance, diversity, and quantitative performance assess GAI models. Automation bias highlights the risk of unquestioned reliance on AI outputs, demanding careful implementation and ethical frameworks. Ethical considerations involve transparency, respect for persons, beneficence, and justice, necessitating standardized evaluation protocols. Health disparities emerge if AI training lacks diversity, impacting diagnostic accuracy. AI language models, like GPT-4, face hallucination issues, posing ethical and legal challenges in healthcare. Regulatory frameworks and ethical governance are crucial for fair and accountable AI. Ongoing research and development are vital to evolving AI ethics.
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AI models often process input data in ways that no human interpreter can easily explain, but interpretability is crucial for physicians and patients to trust and use the outputs in clinical decision-making.
“In terms of transparency, AI models often process input data in ways that no human interpreter can easily explain, but interpretability is crucial for physicians and patients to trust and use the outputs in clinical decision-making.62”
From Prognostication and clinical opportunities with AI for coronary artery calcium: a scoping review (2026) · cites this paper as [62]
The ‘Considerations around transparency and explainability’ section says deep-learning models can be difficult to explain and that interpretability is crucial for physicians to trust and use generative AI in clinical practice. The ‘Evaluation metrics’ section says transparency helps build patients’ trust, but does not say patients need interpretability to use AI outputs in clinical decision-making. No page numbers are available in our copy. (LLM, conf=0.85)
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References 99 · Cited by 7
Research works printed but not linked: 5
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- Authors
- Marey, A., Serdysnki, K. C., Killeen, B. D., Unberath, M., & Umair, M.
- Affiliations
- Alexandria University (EG)
- Rowan-Virtua School of Osteopathic Medicine , Stratford, NJ, 08084,
- Johns Hopkins University (US)
- Corresponding author
- Umair, Muhammad
- Venue
- BJR|Artificial Intelligence
- Publisher
- Oxford University Press (OUP)
- Published
- 2024-01
- Volume
- 1
- Issue
- 1
- Pages
- ubae008-ubae008
- ISSN
- 2976-8705 (ISSN-L)
- PMC
- PMC13045692
- OpenAlex
- W4399153308
- Cited by
- 7
- References
- 99
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 2
- FWCI
- 0.16
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BJR|Artificial Intelligence (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Cardiac Imaging and Diagnostics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- generative AI, cardiovascular imaging, generative adversarial networks, echocardiography, cardiac MRI, coronary CT angiography, coronary artery disease, cardiac image segmentation, congenital heart disease, 3D printing, AI ethics, legal issues, interpretability, automation bias, health disparities, regulatory framework, AI governance
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