Artificial intelligence for education, proctoring, and credentialing in cardiovascular medicine
Open accessAbstract
Artificial intelligence and machine learning are rapidly gaining popularity in every aspect of cardiovascular medicine. This review discusses the past, present, and future of artificial intelligence in education, remote proctoring, credentialing, research, and publication as they pertain to cardiovascular procedures. This review describes the benefits and limitations of artificial intelligence and machine learning and the exciting potential of integrating advanced simulation, holography, virtual reality, and extended reality into disease diagnosis and patient care, as well as their roles in cardiovascular research and education. Nonetheless, much of the available data resides in electronic medical records or within industry-sponsored proprietary programs that are not compatible or standardized for current clinical application. Many areas in cardiovascular medicine would benefit from the introduction or increased use of artificial intelligence. Web-based artificial intelligence applications could be used to address unmet needs for education, on-demand procedural proctoring, credentialing, and recredentialing for interventionists and physicians in remote locations. Further progress in artificial intelligence will require further collaboration among computer scientists and researchers in order to identify and correct the most relevant problems and to implement the best data-based approach to achieving this goal. The future success of artificial intelligence in cardiovascular medicine will depend on the degree of collaboration between all pertinent experts in this field. This will undoubtedly be a prolonged, stepwise process.
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AI can be used to analyze applicant data and identify candidates who are most likely to succeed in a given program.
“AI can be used to analyze applicant data and identify candidates who are most likely to succeed in a given program.”
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- Authors
- Krajcer, Z.
- Affiliations
- Baylor College of Medicine (US)
- Corresponding author
- Krajcer, Zvonimir
- ORCID
- Krajcer, Zvonimir
- Venue
- Texas Heart Institute Journal
- Publisher
- Texas Heart Institute Journal
- Published
- 2022
- Volume
- 49
- Issue
- 2
- ISSN
- 0730-2347 (ISSN-L)
- 1526-6702
- PMC
- PMC9053661
- OpenAlex
- W4225100128
- Cited by
- 22
- References
- 18
- Citations by year
- 2026: 4 · 2025: 6 · 2024: 7 · 2023: 5
- FWCI
- 1.36
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Texas Heart Institute Journal (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Surgery › Surgical Simulation and Training
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Keywords
- artificial intelligence, machine learning, medical education, remote proctoring, credentialing, cardiovascular procedures, virtual reality, extended reality, holography, simulation, electronic health records, data standardization, cardiovascular research
- MeSH terms
- Machine Learning, Artificial Intelligence, Credentialing, Forecasting, Humans
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