TY - JOUR
T1 - Artificial Intelligence in Cardiology
T2 - Present and Future
AU - Lopez-Jimenez, Francisco
AU - Attia, Zachi
AU - Arruda-Olson, Adelaide M.
AU - Carter, Rickey
AU - Chareonthaitawee, Panithaya
AU - Jouni, Hayan
AU - Kapa, Suraj
AU - Lerman, Amir
AU - Luong, Christina
AU - Medina-Inojosa, Jose R.
AU - Noseworthy, Peter A.
AU - Pellikka, Patricia A.
AU - Redfield, Margaret M.
AU - Roger, Veronique L.
AU - Sandhu, Gurpreet S.
AU - Senecal, Conor
AU - Friedman, Paul A.
N1 - Funding Information:
Potential Competing Interests: Dr Lopez-Jimenez has applied for a patent regarding applications of artificial intelligence (AI) to detect vascular age and low ejection fraction (funds paid to his institution). Dr Chareonthaitawee is a consultant for Bioclinica, GE Healthcare, and MedTrace Pharma A/S and has received travel/accommodations/meeting expenses from Ionetix Corporation. Dr Kapa has patents pending for using AI to predict ejection fraction, age, and sex from electrocardiography. Dr Pellikka is a member of the board for Bracco Diagnostics Inc (funds paid to her institution), has received grants from GE Healthcare and Lantheus Medical Imaging, Inc (funds paid to her institution), and has patents for use of AI in echocardiography. Mayo Clinic has licensed AI technology to Eko Devices Inc, a maker of digital stethoscopes with embedded electrocardiographic electrodes. Mayo Clinic may receive financial benefit from the use of this technology but at no point will Mayo Clinic benefit financially from its use for the care of patients at Mayo Clinic. Drs Lopez-Jimenez, Attia, Kapa, and Friedman may also, in the future, receive financial benefit from this agreement.Commercialized and US Food and Drug Administration–approved image viewing software programs have incorporated automated comparison to databases of normal myocardial perfusion distributions, providing computer-aided adjunctive diagnostic tools used by expert readers to identify hypoperfused myocardium. These types of automation, along with availability of digital image data for ML, have enabled the application of AI algorithms using SPECT MPI data, alone and in combination with clinical characteristics, to further enhance the prediction of angiographic coronary artery disease (CAD), prognosis, and/or revascularization and to drive structured reporting and provide clinical decision support (CDS) in recent studies, as discussed subsequently.
Publisher Copyright:
© 2020 Mayo Foundation for Medical Education and Research
PY - 2020/5
Y1 - 2020/5
N2 - 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.
AB - 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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U2 - 10.1016/j.mayocp.2020.01.038
DO - 10.1016/j.mayocp.2020.01.038
M3 - Review article
C2 - 32370835
AN - SCOPUS:85083881628
SN - 0025-6196
VL - 95
SP - 1015
EP - 1039
JO - Mayo Clinic Proceedings
JF - Mayo Clinic Proceedings
IS - 5
ER -