ABSTRACT
Conversational artificial intelligence involves the ability of computers, voice-enabled devices to interact intelligently with the user through voice. This can be leveraged in heart failure care delivery, benefiting the patients, providers, and payers, by providing timely access to care, filling the gaps in care, optimizing management, improving quality of care, and reducing cost. Introduction of machine learning to phonocardiography has potential to achieve outstanding diagnostic and prognostic performances in heart failure patients. There is ongoing research to use voice as a biomarker in heart failure patients. If successful, this may facilitate the screening, diagnosis, and clinical assessment of heart failure.
Subject(s)
Artificial Intelligence , Heart Failure , Delivery of Health Care , Heart Failure/diagnosis , Heart Failure/therapy , Humans , Machine Learning , PhonocardiographyABSTRACT
The current era of big data offers a wealth of new opportunities for clinicians to leverage artificial intelligence to optimize care for pediatric and adult patients with a congenital heart disease. At present, there is a significant underutilization of artificial intelligence in the clinical setting for the diagnosis, prognosis, and management of congenital heart disease patients. This document is a call to action and will describe the current state of artificial intelligence in congenital heart disease, review challenges, discuss opportunities, and focus on the top priorities of artificial intelligence-based deployment in congenital heart disease.
