Limitations
After the development of an AI-ECG model, rigorous external validation in diverse populations and clinical trials to demonstrate superior clinical outcomes to the standard of care is needed. To date, most models have not undergone rigorous evaluation and thus their results remain confined to their own datasets. Furthermore, due to the several knowledge gaps surrounding AF, it becomes challenging to prove immediate benefits in clinical outcomes.
A limitation to widespread applicability of these models is the need to be tailored to the target application. Although, cross domain utility of a pre-trained model to perform the same function has shown promising results. For instance, in a study a DNN model was trained to detect paroxysmal AF from 24-hour Holter ECG and then fine tuned to a completely different input data of smartwatch PPG signal with superior results (AUC of 0.97).79 However, as noted by authors, this study was limited by a lack of gold standard labels to study true performance.79
Several other limitations surrounding AI such as data security, perpetuating bias, physician acceptance and regulatory considerations are also important to be addressed and have been discussed elsewhere in more detail.8,89