Table 7.

A Framework for Successful Implementation of AI/ML in Cardiovascular Medicine

Best practices	Description
AI/ML algorithm triangulation in different data sets, by allowing data sharing	Several best practices have been reported by the American Heart Association Precision Medicine Platform to facilitate generalizability of results and data sharing.
Study benchmarking against current standards for gain and cost-effectiveness analysis.	Validation of AI/ML-based precision medicine algorithms (eg, using cluster randomized clinical trials to assess the utility of the developed decision support tools).
Involvement of a multidisciplinary team in AI/ML algorithm development	Use of interdisciplinary teams of clinicians and researchers who leverage AI/ML and informatics, may improve treatment for patients.
Explainability of AI/ML algorithms increases trust and adoption	Scepticism regarding the wide application of “big data” analysis and AI/ML algorithms can be eased by explainable algorithms for interested stakeholders.
Gaps and challenges	Description
Algorithms need to be transferable	Translating precision medicine platforms from the original development cohort to other external patient populations introduces uncertainty in clinical decisions.
Social determinants or measures of deprivation are not used for prediction, classification, or optimization	Inclusion of social determinants or measures of social deprivation have been shown to improve cardiovascular risk scores.
Regulations ensure that AI/ML algorithms are safe, effective, efficient	The diversity of devices, AI/ML algorithms, and databases introduces several risks. The US Food and Drug Administration provides guidance on data use and algorithm development.
Protection of at-risk communities from further discrimination by AI/ML algorithms	It is critical to devise strategies to eradicate rather than exacerbate existing health inequalities.

AI indicates artificial intelligence; and ML, machine learning.