Value Creation Through Artificial Intelligence and Cardiovascular Imaging: A Scientific Statement From the American Heart Association
Open accessAbstract
Multiple applications for machine learning and artificial intelligence (AI) in cardiovascular imaging are being proposed and developed. However, the processes involved in implementing AI in cardiovascular imaging are highly diverse, varying by imaging modality, patient subtype, features to be extracted and analyzed, and clinical application. This article establishes a framework that defines value from an organizational perspective, followed by value chain analysis to identify the activities in which AI might produce the greatest incremental value creation. The various perspectives that should be considered are highlighted, including clinicians, imagers, hospitals, patients, and payers. Integrating the perspectives of all health care stakeholders is critical for creating value and ensuring the successful deployment of AI tools in a real-world setting. Different AI tools are summarized, along with the unique aspects of AI applications to various cardiac imaging modalities, including cardiac computed tomography, magnetic resonance imaging, and positron emission tomography. AI is applicable and has the potential to add value to cardiovascular imaging at every step along the patient journey, from selecting the more appropriate test to optimizing image acquisition and analysis, interpreting the results for classification and diagnosis, and predicting the risk for major adverse cardiac events.
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By continuously learning from patient data and treatment outcomes, AI agents help refine treatment plans in real-time, ensuring the most effective interventions are implemented throughout the course of care.
“By continuously learning from patient data and treatment outcomes, AI agents help refine treatment plans in real-time, ensuring the most effective interventions are implemented throughout the course of care.95”
From A foundational architecture for AI agents in healthcare (2025) · cites this paper as [95]
In “Assessing the Performance of AI Systems in Cardiovascular Imaging” (page not provided), the paper describes digital twins receiving real-time patient data and feedback from intervention outcomes to train AI. It says this may improve treatment selection, but does not establish that AI agents currently refine plans in real time or ensure the most effective interventions throughout care. (LLM, conf=0.88)
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Flagged first, then by citations
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Cited by
Sorted by citations
Mastrodicasa, D., van Assen, M., Huisman, M., Leiner, T., Williamson, E. E., Nicol, E. D., Allen, B. D., Saba, L., Vliegenthart, R., & Hanneman, K. (2025) · Radiology · doi:10.1148/radiol.240516 · Cited by 69 · see citing works
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- Authors
- Hanneman, K., Playford, D., Dey, D., van Assen, M., Mastrodicasa, D., Cook, T. S., Gichoya, J. W., Williamson, E. E., Rubin, G. D., & on behalf of the American Heart Association Council on Cardiovascular Radiology and Intervention; and Council on Lifelong Congenital Heart Disease and Heart Health in the Young
- ORCID
- Venue
- Circulation
- Published
- 2024-02-06
- Volume
- 149
- Issue
- 6
- Pages
- e296-e311
- ISSN
- 0009-7322 (ISSN-L)
- 1524-4539
- OpenAlex
- W4390750437
- Cited by
- 72
- References
- 108
- Citations by year
- 2026: 35 · 2025: 27 · 2024: 10
- FWCI
- 1.62
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Circulation (publishedVersion)
- PubMed (publishedVersion)
- ResearchOnline@ND (The University of Notre Dame) (submittedVersion)
- 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
- artificial intelligence, cardiovascular imaging, value creation, value chain analysis, machine learning, cardiac CT, cardiac magnetic resonance imaging, cardiac positron emission tomography, healthcare stakeholders, major adverse cardiac events, risk prediction, image acquisition optimization, clinical implementation of AI
- MeSH terms
- Machine Learning, American Heart Association, Artificial Intelligence, Heart, Humans, Magnetic Resonance Imaging
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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