A cardiologist and professor of medicine at Stanford University, Sanjiv M. Narayan, MD, is highly recognized in San Diego for treating numerous veterans with life-threatening heart rhythm disorders. Dr. Sanjiv Narayan attended the University of Birmingham, where he received his MD and PhD. He also takes an interest in various clinical interests, such as machine learning and AI.
Machine learning (ML)-based artificial intelligence systems are revolutionizing cardiovascular medicine. These systems have to offer added precision and timely delivery of crucial care to mitigate the future emergence of debilitating and potentially lethal heart problems. For the most part, ML-embedded AI is trained with ECG data to familiarize underlying algorithms with the concepts of diagnosis and prognosis of cardiovascular abnormalities. With this knowledge, AIs can streamline workflow in clinical settings by helping clinicians perform some tasks that traditionally require the skills of a cardiovascular expert.
To comprehend how useful AIs can be in the field of cardiovascular settings, consider how Mayo Clinic scientists have successfully automated the cardiovascular examination of patients. Mayo Clinic uses AI systems to detect warning signs of dangerous heart problems through ECGs. The Mayo Clinic database has over seven million ECG records. The art of diagnosis was taught to machines by feeding the systems with data. Of course, each patient's ID was removed before relaying the data to computer systems for training to avoid violation of privacy.
The ML-embedded AI systems demonstrated accuracy in detecting weak heart pumps from ECG analysis with the data. Weak heart pumps are warning signs of heart failure in the future. These predictions also come at a lower cost than what would be required if human experts made the diagnosis. The systems also boast faster results, as each evaluation takes just a couple of seconds.
