Artificial intelligence–enabled tools in cardiovascular medicine: A survey of current use, perceptions, and challenges
Open accessAbstract
Background: Numerous artificial intelligence (AI)-enabled tools for cardiovascular diseases have been published, with a high impact on public health. However, few have been adopted into, or have meaningfully affected, routine clinical care. Objective: To evaluate current awareness, perceptions, and clinical use of AI-enabled digital health tools for patients with cardiovascular disease, and challenges to adoption. Methods: This mixed-methods study included interviews with 12 cardiologists and 8 health information technology (IT) administrators, and a follow-on survey of 90 cardiologists and 30 IT administrators. Results: We identified 5 major challenges: (1) limited knowledge, (2) insufficient usability, (3) cost constraints, (4) poor electronic health record interoperability, and (5) lack of trust. A minority of cardiologists were using AI tools; more were prepared to implement AI tools, but their sophistication level varied greatly. Conclusion: Most respondents believe in the potential of AI-enabled tools to improve care quality and efficiency, but they identified several fundamental barriers to wide-scale adoption.
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Dissatisfaction was reported if recommendations were too similar, inappropriate, or not useful.
“This also included dissatisfaction if recommendations were too similar, inappropriate, or not useful [35,37,38,40,56].”
From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [56]
In Results > Qualitative focus group sessions/interviews (page not provided), a cardiologist says information that is not actionable or helpful slows clinicians down and is ignored. The paper also reports concern about AI making wrong clinical decisions, but it does not report dissatisfaction with recommendations because they were too similar, or specifically describe inappropriate recommendations. (LLM, conf=0.83)
Experimental — accuracy not yet measuredPrivacy and ethics concerns were focused on maintaining confidentiality, ensuring processes are in place to obtain consent, and having informed consent with clarity on how data will be processed.
“Concerns about privacy and ethics were focused on maintaining confidentiality, ensuring processes are in place to obtain consent, and having informed consent with clarity on how the data will be processed [32,39,47,56].”
From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [56]
The Results (Qualitative focus group sessions/interviews; page unavailable) report concern about protecting patient data privacy, and the quantitative survey reports common HIPAA concerns. The paper does not describe concerns about processes for obtaining consent or explaining to patients how their data will be processed; its reported evaluation processes concern adoption of AI technology, not consent. (LLM, conf=0.86)
Experimental — accuracy not yet measuredLack of and uncertainty surrounding funding are barriers to implementation.
“The lack of and uncertainty surrounding funding are presented as barriers to implementation [32,51,56].”
From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [56]
The Results (Qualitative focus group sessions/interviews; page not specified) identify budget constraints and lack of budget as barriers to implementing AI tools. They also describe the need to justify purchases to procurement committees, but do not specifically identify uncertainty surrounding funding as a barrier. (LLM, conf=0.85)
Experimental — accuracy not yet measuredSurveys of cardiologists suggest generally favorable views regarding the potential of AI to improve efficiency and patient care, but adoption remains uneven because of concerns regarding model validation, transparency, unfamiliarity with AI methods, and trust in automated recommendations.
“Surveys of cardiologists suggest generally favorable views regarding the potential of AI to improve efficiency and patient care, but adoption remains uneven because of concerns regarding model validation, transparency, unfamiliarity with AI methods, and trust in automated recommendations [45].”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [45]
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References 17 · Cited by 40
Research works printed but not linked: 1
- [15]Jacobs M. He J. Pradier M.F. Designing AI for trust and collaboration in time-constrained medical decisions: a sociotechnical lens. CHI: Conference on Human Factors in Computing Systems 2021 Association for Computing Machinery Yokohama, Japan
- Authors
- Schepart, A., Burton, A., Durkin, L., Fuller, A., Charap, E., Bhambri, R., & Ahmad, F. S.
- Affiliations
- Pfizer (United States) (US)
- Northwestern University (US)
- Corresponding author
- Ahmad, Faraz S.
- ORCID
- Ahmad, Faraz S.
- Venue
- Cardiovascular Digital Health Journal
- Publisher
- Elsevier BV
- Published
- 2023-06
- Volume
- 4
- Issue
- 3
- Pages
- 101-110
- ISSN
- 2666-6936 (ISSN-L)
- PMC
- PMC10282011
- OpenAlex
- W4367856213
- Cited by
- 40
- References
- 17
- Citations by year
- 2026: 9 · 2025: 18 · 2024: 11 · 2023: 2
- FWCI
- 1.60
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Cardiovascular Digital Health Journal (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- American Heart Association (856917)
- Pfizer
- National Institutes of Health
- National Heart, Lung, and Blood Institute (K23HL155970)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Keywords
- artificial intelligence, cardiovascular disease, clinical adoption of AI, digital health tools, electronic health records interoperability, usability, cost barriers, trust in AI, implementation barriers
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