Table 2.

Electrocardiography

Best practices	Description
The acquisition environment of ECGs used to train AI/ML should match those used clinically	Electrocardiographic signals are affected by body position, lead placement, motion, and signal processing issues such as sampling rate and dynamic range.
Accounting of bias enables generalization of results to diverse populations	Different populations show different “normal” electrocardiographic features. These factors should be incorporated into AI/ML models to ensure generalizability.
AI/ML algorithms must be tested in independent, external cohorts	AI/ML algorithm generalizability is ensured by their testing on data structures other than the ones in which they were created, considering different populations, equipment, and clinical workflows.
Gaps and challenges	Description
Develop a robust framework to apply AI/ML algorithms for scenarios that appear superficially similar but differ in important respects	Some AI/ML algorithms work well across different clinical scenarios, yet others do not (eg, an algorithm applied on ECG to detect AF in outpatients may not apply to postoperative AF). On the other hand, ECG-based AI/ML algorithms can detect ventricular dysfunction irrespective of mechanism.
Clinical outcome data are limited	Development and testing of practical workflows that integrate AI/ML ECG-based algorithms may demonstrate real-world utility.
Develop a framework to address the consistency of ground truth labels.	Accurate ground-truth labels are needed for AI/ML algorithm training. Tools to rapidly generate labels, such as natural language processing, may be prone to errors. Semisupervised models are still in the research phase.

AF indicates atrial fibrillation; AI, artificial intelligence; and ML, machine learning.