ABSTRACT
Machine learning (ML) can be defined as a subfield of artificial intelligence (AI) technology and can be explained as the capability of any machine to mimic human behaviour. Deep learning was proposed by Geoffrey Hinton as a subtype of ML. Learning from big data and automatic programming of algorithms can be enabled by deep learning. Processing of image, audio, video, and voice-based data is significantly affected by this and can be applied in the field of medical imaging and radiography. Cardiovascular disorders are one of the most prominent causes of death throughout the globe, and AI can be used in their management. Deep learning technique can be used in the field of cardiovascular research in many ways, including personalized or precision medicine, imaging analysis, robotic tools, and accurate predictions for patients. The project “Apple Heart Study” was launched recently by Stanford and Apple. Sensor technology, along with ML, is one of the best examples of AI application in healthcare. A medical device named “Apple Watch Series 4.0” was launched for the measurement of electrocardiogram, which has been approved by the USFDA (United States Food and Drug Authority) for clinical use. A major challenge in the frequent usage of AI technology for healthcare is the large amount of high-quality data and there is a need for higher quality standards of AI technology for clinical applications as compared to other fields.
