Artificial Intelligence in Cardiology.

By Kipp W. Johnson, Jessica Torres Soto, Benjamin S. Glicksberg, Khader Shameer, Riccardo Miotto, Mohsin Ali, Euan A. Ashley, Joel T. Dudley. Journal of the American College of Cardiology, 2018

769 citations recorded by the source corpus.

Artificial intelligence and machine learning are poised to influence nearly every aspect of the human condition, and cardiology is not an exception to this trend. This paper provides a guide for clinicians on relevant aspects of artificial intelligence and machine learning, reviews selected applications of these methods in cardiology to date, and identifies how cardiovascular medicine could incorporate artificial intelligence in the future. In particular, the paper first reviews predictive modeling concepts relevant to cardiology such as feature selection and frequent pitfalls such as improper dichotomization. Second, it discusses common algorithms used in supervised learning and reviews selected applications in cardiology and related disciplines. Third, it describes the advent of deep learning and related methods collectively called unsupervised learning, provides contextual examples both in general medicine and in cardiovascular medicine, and then explains how these methods could be applied to enable precision cardiology and improve patient outcomes.

Subfields: Artificial Intelligence, Health Informatics, Health Information Management

View Paper: https://www.semanticscholar.org/paper/a4b586888a784ad0790a029cca72f2f17740bb13

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Junaid Qadir
2. Ahad Nasab
3. Khansa Rasheed
4. Gopi Battineni
5. Nalini Chinatalapudi
6. Adeel Razi
7. J. Halperin
8. Zhen Wang

In Health Informatics

1. Junaid Qadir
2. Daniel Shu Wei Ting
3. Louisa H.Y. Tsi
4. Shakked Noy
5. Whitney Zhang
6. Cecilia Ka Yuk Chan

In Health Information Management

1. Ahad Nasab
2. Junaid Qadir
3. Nafiseh Ghaffar Nia
4. Erkan Kaplanoglu
5. Adnan Qayyum

Citations in the corpus

• Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis (2020, methodology): “This is because of the large advancements happening in contemporary ideas in the development of AI [2].”
• Deep learning for cardiovascular medicine: a practical primer. (2019, background): “Deep learning is a specific type of ML inspired by the way that the human brain processes data, and enabled by hardware advances such as graphics processing units (GPUs), 16,18,19 vast cata-logues of labelled data, and advances in computer science theory.”
• The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare. (2021, background): “On the organizational level, health data is not only expensive, 27 but there is ingrained reluctance towards data sharing between hospitals as they are considered the property of each hospital to manage their individual patients.”
• Evaluation of artificial intelligence techniques in disease diagnosis and prediction (2023, methodology): “Due to the existence of a wide range of medical imaging methods, such as CT, ECG, and echocardiography in cardiology, DL can be used accurately and advanced in the analysis and review of cardiovascular data [77–79].”
• Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review (2020, background): “Biomedical fields with large image datasets—such as radiology [47], cardiology [48], pathology [49], and genomics [50]—are using various ML”