Research in cardiovascular medicine has expanded exponentially in the past few decades. The quantum leap in technology during this time has resulted in a massive increase in measurable cardiovascular data. Clinicians and scientists are beginning to realize that to truly understand the vast amounts of information, a far more powerful tool that can augment scientific comprehension is required. AI has now begun to fill that gap, providing a means to handle the large amount of multidimensional data and images in order to deepen our understanding and provide insight. Equipped with an understanding of the power of machine learning, clinicians and researchers will then be able to provide better healthcare solutions, ultimately practicing truly personalized cardiovascular medicine.

The global epidemic of cardiovascular disease is an ever-increasing phenomenon. As a component of AI, machine learning can provide a powerful solution to this threat. Although machine learning has expanded rapidly in various fields recently, medicine has been a late adopter. Machine Learning in Cardiovascular Medicine provides the cross-domain knowledge, intellectual skills and insight to equip clinician and computer scientist researchers with the right tools to collaborate at the intersection of cardiovascular medicine and AI.

Machine Learning in Cardiovascular Medicine introduces machine learning to both the clinician and computer science researcher. From explaining the latest advances in digital medicine and giving a general basic overview of artificial intelligence, the book then takes a deeper dive into various machine learning methods such as predictive models, GANs and NLP. Subsequent chapters detail the fundamental principles and examples of machine learning in various modalities and applications in cardiovascular medicine, such as CT, MRI, genomics and drug discovery. Finally, the book outlays a philosophical view of the future of AI and the ethical challenges it faces.

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