Anthony H Kashou1, Adam M May2, Peter A Noseworthy3. 1. Department of Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN, USA. 2. Cardiovascular Division, Washington University School of Medicine, St. Louis, MO, USA. 3. Division of Cardiovascular Diseases, Mayo Clinic College of Medicine, 200 First Street SW, Rochester, MN, 55905, USA. noseworthy.peter@mayo.edu.
Abstract
PURPOSE OF REVIEW: To (i) review the concept of artificial intelligence (AI); (ii) summarize recent developments in artificial intelligence-enabled electrocardiogram (AI-ECG); (iii) address notable inherent limitations and challenges of AI-ECG; and (iv) discuss the future direction of the field. RECENT FINDINGS: Advancements in machine learning and computing methods have led to application of AI-ECG and potential new applications to patient care. Further study is needed to verify previous findings in diverse populations as well as begin to confront the limitations needed for clinical implementation. Nearly one century after the Nobel Prize was awarded to Willem Einthoven for demonstrating that an electrocardiogram (ECG) could record the electrical signature of the heart, the ECG remains one of the most important diagnostic tests in modern medicine. We now stand at the edge of true ECG innovation. Simultaneous advancements in computing power, wireless technology, digitized data availability, and machine learning have led to the birth of AI-ECG algorithms with novel capabilities and real potential for clinical application. AI has the potential to improve diagnostic accuracy and efficiency by providing fully automated, unbiased, and unambiguous ECG analysis along with promising new findings that may unlock new value in the ECG. These breakthroughs may cause a paradigm shift in clinical workflow as well as patient monitoring and management.
PURPOSE OF REVIEW: To (i) review the concept of artificial intelligence (AI); (ii) summarize recent developments in artificial intelligence-enabled electrocardiogram (AI-ECG); (iii) address notable inherent limitations and challenges of AI-ECG; and (iv) discuss the future direction of the field. RECENT FINDINGS: Advancements in machine learning and computing methods have led to application of AI-ECG and potential new applications to patient care. Further study is needed to verify previous findings in diverse populations as well as begin to confront the limitations needed for clinical implementation. Nearly one century after the Nobel Prize was awarded to Willem Einthoven for demonstrating that an electrocardiogram (ECG) could record the electrical signature of the heart, the ECG remains one of the most important diagnostic tests in modern medicine. We now stand at the edge of true ECG innovation. Simultaneous advancements in computing power, wireless technology, digitized data availability, and machine learning have led to the birth of AI-ECG algorithms with novel capabilities and real potential for clinical application. AI has the potential to improve diagnostic accuracy and efficiency by providing fully automated, unbiased, and unambiguous ECG analysis along with promising new findings that may unlock new value in the ECG. These breakthroughs may cause a paradigm shift in clinical workflow as well as patient monitoring and management.
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