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Article analysis:

The article provides a comprehensive overview of the current state of diagnosis for valvular heart disease and the potential benefits of applying deep learning to electrocardiograms for improved diagnosis. The authors provide evidence from multiple studies that support their claims, as well as data from five New York City hospitals within the Mount Sinai Health System (MSHS). The article also presents results from internal testing and external validation that demonstrate the efficacy of their model in diagnosing Aortic Stenosis and Mitral Regurgitation.

However, there are some potential biases in the article that should be noted. First, the authors do not discuss any potential risks associated with using deep learning models for diagnosis or any ethical considerations related to patient privacy or data security. Second, while they mention that their cohort was socioeconomically and demographically diverse, they do not provide any details on how this diversity was measured or accounted for in their analysis. Third, while they present longitudinal follow-up data on patients diagnosed as true positives for Aortic Stenosis, they do not provide similar data for Mitral Regurgitation or other forms of valvular pathology. Finally, while they mention that machine learning can be used to guide clinical care by analyzing rates of cardiac procedures with respect to model predictions, they do not provide any evidence or examples of this being done in practice.

In conclusion, while this article provides a comprehensive overview of the current state of diagnosis for valvular heart disease and presents promising results from internal testing and external validation regarding the efficacy of deep learning models in diagnosing Aortic Stenosis and Mitral Regurgitation, there are some potential biases that should be noted when evaluating its trustworthiness and reliability.