A scientific statement published by the American Heart Association (AHA) in Circulation today acknowledges the potential “transformative” impact of the technology on cardiovascular medicine, but challenges remain in its development, the authors of the paper state.
The new scientific statement, “Use of Artificial Intelligence in Improving Outcomes in Heart Disease,” comes amid intense attention on AI as a way to improve cardiovascular disease prevention, detection, diagnosis and treatment, AHA says in a press release.
“Here, we present the state-of-the-art including the latest science regarding specific AI uses—from imaging and wearables to electrocardiography and genetics,” said chair of the statement’s writing committee Antonis Armoundas (Massachusetts General Hospital and Harvard Medical School, Boston, USA). “Among the objectives of this manuscript is to identify best practices as well as gaps and challenges that may improve the applicability of AI tools in each area.”
AI has the power to analyse vast amounts of data and make predictions, typically for narrowly defined tasks, such as providing clinical and mechanistic insights in basic, translational, and clinical studies. Machine learning leverages statistical and mathematical models and algorithms to detect patterns in large datasets that may not be evident to human observers using standard approaches. Deep learning, a subfield of machine learning, is the practice of taking very complex data sets and matching them with useful labels, for example in image recognition and interpretation.
Use of these technologies has led to the analysis of electronic health records (EHRs) to understand various treatment effects, to compare the effectiveness of tests and interventions, and, more recently, to build prediction, classification and optimisation models to help inform clinical decision-making. AI applications in cardiovascular care include cardiac imaging, electrocardiography (ECG), bedside monitoring, implantable and wearable technologies, genetics and the interpretation of EHRs.
Protocols
According to the statement protocols to ensure that appropriate information sourcing, selecting and organising, as well as sharing and privacy are critical, and potential ethical and legal challenges also need to be addressed.
A greater scientific knowledge foundation is also needed, AHA states. 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, the writing committee has found, and adds that 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.
The authors also express the need to develop regulatory pathways for AI-enabled technologies to ensure safety and effectiveness to mitigate harm as technologies rapidly evolve.
“Robust prospective clinical validation in large diverse populations that minimises various forms of bias is essential to address uncertainties and bestow trust, which, in turn, will help to increase clinical acceptance and adoption,” Armoundas said.
The authors reviewed several areas of AI use in cardiovascular medicine including imaging, electrocardiography, implantable and wearable technologies and genetics.
“Numerous applications already exist where AI/machine learning-based digital tools can improve screening, extract insights into what factors improve an individual patient’s health and develop precision treatments for complex health conditions,” according to Armoundas.
“There is an urgent need to develop programmes that will accelerate the education of the science behind AI/machine learning tools, thus accelerating the adoption and creation of manageable, cost-effective, automated processes. We need more AI/machine learning-based precision medicine tools to help address core unmet needs in medicine that can subsequently be tested in robust clinical trials,” Armoundas said. “This process must organically incorporate the need to avoid bias and maximise generalisability of findings in order to avoid perpetuating existing healthcare inequities.”
