Posters
Academic Level (Author 1)
Resident
Discipline/Specialty (Author 1)
Internal Medicine
Discipline Track
Clinical Science
Abstract
Background: The integration of artificial intelligence with electrocardiography has significantly transformed contemporary medical practice, representing a major advancement in cardiac care. These algorithms have enhanced the detection of subtle ECG patterns, improving the diagnosis of conditions such as arrhythmias and providing a more accurate diagnostic environment. However, challenges such as data privacy, algorithm transparency, and clinical integration remain. AI holds substantial potential for improving diagnostic accuracy, with several FDA-approved devices now available in the market aimed at enhancing patient outcomes. Future efforts should focus on further validation, implementation, and research to fully realize AI's capabilities in this field.
Methods: This review examines the application of artificial intelligence (AI) in the context of electrocardiograms (ECGs) across various clinical settings, including primary care and specialized cardiac units. It considers diverse patient populations and reviews data from large datasets, clinical trials, and FDA-approved AI devices from the past five years. The analysis employs a systematic literature review and quantitative performance metrics, addressing challenges such as data privacy, algorithm transparency, and integration into clinical workflows.
Results: Artificial intelligence (AI) algorithms are revolutionizing ECGs by transforming them from routine diagnostic assessments into advanced tools for early detection of both cardiac and non-cardiac conditions. Leveraging extensive datasets, AI can identify patterns and asymptomatic cases that human clinicians might miss. FDA-approved devices, including mobile and wearable technologies, are enhancing the quality of ECG monitoring and making it more accessible for patients and healthcare providers. Additionally, improvements in wearable device comfort, AI algorithm refinement, and increasing computing power are advancing real-time monitoring and expanding AI's role in ECG analysis. Despite these advancements, there remains a need to further understand AI's potential in clinical practice, adhere to new guidelines, and overcome barriers to improve its reproducibility and effectiveness within the field of cardiology.
Conclusions: As technology progresses, more medical devices are incorporating AI software such as DeepRhythmAI and wearable devices like the Apple Watch. The prevalent reliance on publicly available datasets for developing new AI methods underscores the critical need for high-quality data and rigorous algorithm development. AI's role in ECG diagnosis faces challenges such as prospective validation in large datasets, seamless data integration, and the resolution of ethical concerns. While AI can further improve diagnostic accuracy, clinical risk prediction, and patient outcomes, its real-world application requires additional investigation. Continued advancements in cloud platforms and quantum computing are expected to enhance arrhythmia diagnosis. However, it is essential to address limitations, including medico-legal issues, to fully harness AI's potential in cardiology.
Presentation Type
Poster
Recommended Citation
Martinez Borrego, Abdel and Flores, Eduardo, "Integrating Artificial Intelligence with Electrocardiograms: A New Era in Cardiac Care" (2024). Research Colloquium. 68.
https://scholarworks.utrgv.edu/colloquium/2024/posters/68
Included in
Integrating Artificial Intelligence with Electrocardiograms: A New Era in Cardiac Care
Background: The integration of artificial intelligence with electrocardiography has significantly transformed contemporary medical practice, representing a major advancement in cardiac care. These algorithms have enhanced the detection of subtle ECG patterns, improving the diagnosis of conditions such as arrhythmias and providing a more accurate diagnostic environment. However, challenges such as data privacy, algorithm transparency, and clinical integration remain. AI holds substantial potential for improving diagnostic accuracy, with several FDA-approved devices now available in the market aimed at enhancing patient outcomes. Future efforts should focus on further validation, implementation, and research to fully realize AI's capabilities in this field.
Methods: This review examines the application of artificial intelligence (AI) in the context of electrocardiograms (ECGs) across various clinical settings, including primary care and specialized cardiac units. It considers diverse patient populations and reviews data from large datasets, clinical trials, and FDA-approved AI devices from the past five years. The analysis employs a systematic literature review and quantitative performance metrics, addressing challenges such as data privacy, algorithm transparency, and integration into clinical workflows.
Results: Artificial intelligence (AI) algorithms are revolutionizing ECGs by transforming them from routine diagnostic assessments into advanced tools for early detection of both cardiac and non-cardiac conditions. Leveraging extensive datasets, AI can identify patterns and asymptomatic cases that human clinicians might miss. FDA-approved devices, including mobile and wearable technologies, are enhancing the quality of ECG monitoring and making it more accessible for patients and healthcare providers. Additionally, improvements in wearable device comfort, AI algorithm refinement, and increasing computing power are advancing real-time monitoring and expanding AI's role in ECG analysis. Despite these advancements, there remains a need to further understand AI's potential in clinical practice, adhere to new guidelines, and overcome barriers to improve its reproducibility and effectiveness within the field of cardiology.
Conclusions: As technology progresses, more medical devices are incorporating AI software such as DeepRhythmAI and wearable devices like the Apple Watch. The prevalent reliance on publicly available datasets for developing new AI methods underscores the critical need for high-quality data and rigorous algorithm development. AI's role in ECG diagnosis faces challenges such as prospective validation in large datasets, seamless data integration, and the resolution of ethical concerns. While AI can further improve diagnostic accuracy, clinical risk prediction, and patient outcomes, its real-world application requires additional investigation. Continued advancements in cloud platforms and quantum computing are expected to enhance arrhythmia diagnosis. However, it is essential to address limitations, including medico-legal issues, to fully harness AI's potential in cardiology.