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Utilization and programming of an automatic MRI recognition feature for cardiac rhythm management devices Association of CYP2C19 Loss-of-Function Alleles with Major Adverse Cardiovascular Events of Clopidogrel in Stable Coronary Artery Disease Patients Undergoing Percutaneous Coronary Intervention: Meta-analysis Short-term and long-term clinical outcomes of rotational atherectomy in resistant chronic total occlusion Incidence, Predictors, and Outcomes of In-Hospital Percutaneous Coronary Intervention Following Coronary Artery Bypass Grafting Causes, Timing, and Impact of Dual Antiplatelet Therapy Interruption for Surgery (from the Patterns of Non-adherence to Anti-platelet Regimens In Stented Patients Registry) 2020 AHA/ACC Key Data Elements and Definitions for Coronary Revascularization A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Data Standards (Writing Committee to Develop Clinical Data Standards for Coronary Revascularization) Five-Year Outcomes after PCI or CABG for Left Main Coronary Disease Mortality Differences Associated With Treatment Responses in CANTOS and FOURIER: Insights and Implications Drug-Coated Balloon Versus Drug-Eluting Stent in Primary Percutaneous Coronary Intervention: A Feasibility Study Improving the Design of Future PCI Trials for Stable Coronary Artery Disease: JACC State-of-the-Art Review
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Review ArticleVolume 12, Issue 14, July 2019

JOURNAL：JACC Cardiovasc Interv. Article Link

Impact of Artificial Intelligence on Interventional Cardiology: From Decision-Making Aid to Advanced Interventional Procedure Assistance

P Sardar, JD Abbott, A Kundu et al. Keywords: artificial intelligence; interventional cardiology

ABSTRACT

Access to big data analyzed by supercomputers using advanced mathematical algorithms (i.e., deep machine learning) has allowed for enhancement of cognitive output (i.e., visual imaging interpretation) to previously unseen levels and promises to fundamentally change the practice of medicine. This field, known as “artificial intelligence” (AI), is making significant progress in areas such as automated clinical decision making, medical imaging analysis, and interventional procedures, and has the potential to dramatically influence the practice of interventional cardiology. The unique nature of interventional cardiology makes it an ideal target for the development of AI-based technologies designed to improve real-time clinical decision making, streamline workflow in the catheterization laboratory, and standardize catheter-based procedures through advanced robotics. This review provides an introduction to AI by highlighting its scope, potential applications, and limitations in interventional cardiology.

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