Artificial Intelligence in Cardiology: Present and Future

Artificial intelligence (AI) is a nontechnical, popular term that refers to machine learning of various types but most often to deep neural networks. Cardiology is at the forefront of AI in medicine. For this review, we searched PubMed and MEDLINE databases with no date restriction using search term...

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Bibliographic Details
Published in:Mayo Clinic proceedings Vol. 95; no. 5; pp. 1015 - 1039
Main Authors: Lopez-Jimenez, Francisco, Attia, Zachi, Arruda-Olson, Adelaide M., Carter, Rickey, Chareonthaitawee, Panithaya, Jouni, Hayan, Kapa, Suraj, Lerman, Amir, Luong, Christina, Medina-Inojosa, Jose R., Noseworthy, Peter A., Pellikka, Patricia A., Redfield, Margaret M., Roger, Veronique L., Sandhu, Gurpreet S., Senecal, Conor, Friedman, Paul A.
Format: Journal Article
Language:English
Published: England Elsevier Inc 01.05.2020
Frontline Medical Communications Inc
Elsevier Limited
Subjects:
Algorithms
Artificial intelligence
Cardiology
Electrocardiography
Learning algorithms
Machine learning
Natural language processing
Neural networks
Statistical analysis
NSTEMI
EF
CRT
CAD
MPI
AI
DL
ECG
TPD
HFpEF
UA
HFrEF
EHR
SPECT
AUC
ACS
CDS
NLP
MACE
STEMI
HF
ML
ISSN:0025-6196, 1942-5546, 1942-5546
Online Access:Get full text
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Summary:Artificial intelligence (AI) is a nontechnical, popular term that refers to machine learning of various types but most often to deep neural networks. Cardiology is at the forefront of AI in medicine. For this review, we searched PubMed and MEDLINE databases with no date restriction using search terms related to AI and cardiology. Articles were selected for inclusion on the basis of relevance. We highlight the major achievements in recent years in nearly all areas of cardiology and underscore the mounting evidence suggesting how AI will take center stage in the field. Artificial intelligence requires a close collaboration among computer scientists, clinical investigators, clinicians, and other users in order to identify the most relevant problems to be solved. Best practices in the generation and implementation of AI include the selection of ideal data sources, taking into account common challenges during the interpretation, validation, and generalizability of findings, and addressing safety and ethical concerns before final implementation. The future of AI in cardiology and in medicine in general is bright as the collaboration between investigators and clinicians continues to excel.
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ISSN:0025-6196
1942-5546
1942-5546
DOI:10.1016/j.mayocp.2020.01.038