Machine intelligence in cardiovascular medicine
Abstract
The computer science technology trend called artificial intelligence (AI) is not new. Both machine learning and deep learning AI applications have recently begun to impact cardiovascular medicine. Scientists working in the AI domain have long recognized the importance of data quality and provenance to AI algorithm efficiency and accuracy. A diverse array of cardiovascular raw data sources of variable quality—electronic medical records, radiological picture archiving and communication systems, laboratory results, omics, etc.—are available to train AI algorithms for predictive modeling of clinical outcomes (in-hospital mortality, acute coronary syndrome risk stratification, etc.), accelerated image interpretation (edge detection, tissue characterization, etc.) and enhanced phenotyping of heterogeneous conditions (heart failure with preserved ejection fraction, hypertension, etc.). A number of software as medical device narrow AI products for cardiac arrhythmia characterization and advanced image deconvolution are now Food and Drug Administration approved, and many others are in the pipeline. Present and future health professionals using AI-infused analytics and wearable devices have 3 critical roles to play in their informed development and ethical application in practice: (1) medical domain experts providing clinical context to computer and data scientists, (2) data stewards assuring the quality, relevance and provenance of data inputs, and (3) real-time and post-hoc interpreters of AI black box solutions and recommendations to patients. The next wave of so-called contextual adaption AI technologies will more closely approximate human decision-making, potentially augmenting cardiologists’ real-time performance in emergency rooms, catheterization laboratories, imaging suites, and clinics. However, before such higher order AI technologies are adopted in the clinical setting and by healthcare systems, regulatory agencies, and industry must jointly develop robust AI standards of practice and transparent technology insertion rule sets.
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Health professionals using AI technologies have three key ethical roles to play in their practices: (1) as medical domain experts who should provide computer and data scientists with the clinical context they need, (2) as gatekeepers for data quality who ensure that data inputs are relevant, accurate and sourced appropriately, and (3) as interpreters of AI black-box solutions who make real-time и
“Health professionals using AI technologies have three key ethical roles to play in their practices: (1) as medical domain experts who should provide computer and data scientists with the clinical context they need, (2) as gatekeepers for data quality who ensure that data inputs are relevant, accurate and sourced appropriately, and (3) as interpreters of AI black-box solutions who make real-time”
From SHIFTing artificial intelligence to be responsible in healthcare: a systematic review (2022) · cites this paper as Miller, 2020
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SHIFTing artificial intelligence to be responsible in healthcare: a systematic review
Siala, H., & Wang, Y. (2022) · Social Science & Medicine · doi:10.1016/j.socscimed.2022.114782 · Cited by 358 · see citing works
- Authors
- Miller, D. D.
- Affiliations
- Augusta University (US)
- Corresponding author
- Miller, D. Douglas
- ORCID
- Miller, D. Douglas
- Venue
- Cardiology in Review
- Published
- 2020-03
- Volume
- 28
- Issue
- 2
- Pages
- 53-64
- ISSN
- 1061-5377 (ISSN-L)
- OpenAlex
- W3004640731
- Cited by
- 58
- References
- 59
- Citations by year
- 2026: 4 · 2025: 10 · 2024: 18 · 2023: 5 · 2022: 7 · 2021: 8 · 2020: 5 · 2019: 1
- FWCI
- 0.47
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Cardiology in Review (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- artificial intelligence in cardiology, machine learning, deep learning, data provenance, clinical outcome prediction, heart failure with preserved ejection fraction, software as a medical device, wearable devices, explainable AI, AI regulation
- MeSH terms
- Machine Learning, Artificial Intelligence, Cardiology, Cardiovascular Diseases, Humans, Neural Networks, Computer
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