Use of AI to Improve Outcomes in Heart Disease: Key Points

Armoundas AA, Narayan SM, Arnett DK, et al., on behalf of the American Heart Association Institute for Precision Cardiovascular Medicine; Council on Cardiovascular and Stroke Nursing; Council on Lifelong Congenital Heart Disease and Heart Health in the Young; Council on Cardiovascular Radiology and Intervention; Council on Hypertension; Council on the Kidney in Cardiovascular Disease; and Stroke Council.
Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association. Circulation 2024;Feb 28:[Epub ahead of print].

The following are key points to remember from an American Heart Association scientific statement on the use of artificial intelligence (AI) in improving outcomes in heart disease:

  1. The objective of this scientific statement is to present the state of the art on the use of AI or machine learning (ML) to enable precision medicine and implementation science in cardiovascular research and clinical care.
  2. At this dawn in the era of precision medicine, scientists and clinicians, computer and data scientists, patient advocacy groups, health care organizations, and policymakers must develop principles and guidance for the development and application of AI/ML-based digital health.
  3. Numerous applications already exist where AI/ML-based digital tools can improve disease screening, extract insights into what makes individual patients healthy, and develop precision treatments for complex diseases.
  4. Of note, while promising research is beginning to emerge in many areas of cardiovascular medicine, AI-based tools, algorithms, and systems of care have not yet been proven to improve care enough to justify widespread use.
  5. There is an urgent need to develop implementation science for AI/ML tools to create trackable cost-effective workflows for AI/ML-based precision medicine that address core unmet clinical (or translational) needs, the evidence of which can be robustly tested in trials.
  6. This process must organically incorporate the need to avoid bias and maximize generalizability of findings to avoid perpetuating existing health care inequalities. Robust prospective clinical validation in large diverse populations that minimizes various forms of bias is essential to address uncertainties and bestow trust, which, in turn, will help to increase clinical acceptance and adoption.
  7. Protocols that ensure appropriate information sourcing, selecting and organizing, as well as sharing and privacy are critical. Potential ethical and legal challenges also need to be addressed.
  8. Furthermore, a greater scientific knowledge foundation is needed. Current AI-based algorithms lack prospective research or studies that model the effects of AI in order to closely examine its potential impact in the future. There are urgent needs for prospectively collected information, clinical trials, and development of automated workflows to launch and maintain specific tasks that may improve efficiency.
  9. Implementing AI algorithms in practice at this time may be limited by a lack of standardized platforms across the health care industry to report predictions and scale findings in data sets.
  10. Finally, there is a need to develop regulatory pathways for AI-enabled technologies in the United States to ensure safety and effectiveness to mitigate harm as technologies rapidly evolve.

Clinical Topics: Cardiovascular Care Team

Keywords: Artificial Intelligence, Heart Diseases, Machine Learning

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