Dataset Information

Artificial Intelligence and Machine Learning in Arrhythmias and Cardiac Electrophysiology.

ABSTRACT: Artificial intelligence (AI) and machine learning (ML) in medicine are currently areas of intense exploration, showing potential to automate human tasks and even perform tasks beyond human capabilities. Literacy and understanding of AI/ML methods are becoming increasingly important to researchers and clinicians. The first objective of this review is to provide the novice reader with literacy of AI/ML methods and provide a foundation for how one might conduct an ML study. We provide a technical overview of some of the most commonly used terms, techniques, and challenges in AI/ML studies, with reference to recent studies in cardiac electrophysiology to illustrate key points. The second objective of this review is to use examples from recent literature to discuss how AI and ML are changing clinical practice and research in cardiac electrophysiology, with emphasis on disease detection and diagnosis, prediction of patient outcomes, and novel characterization of disease. The final objective is to highlight important considerations and challenges for appropriate validation, adoption, and deployment of AI technologies into clinical practice.

SUBMITTER: Feeny AK

PROVIDER: S-EPMC7808396 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Publications

Artificial Intelligence and Machine Learning in Arrhythmias and Cardiac Electrophysiology.

Feeny Albert K AK Chung Mina K MK Madabhushi Anant A Attia Zachi I ZI Cikes Maja M Firouznia Marjan M Friedman Paul A PA Kalscheur Matthew M MM Kapa Suraj S Narayan Sanjiv M SM Noseworthy Peter A PA Passman Rod S RS Perez Marco V MV Peters Nicholas S NS Piccini Jonathan P JP Tarakji Khaldoun G KG Thomas Suma A SA Trayanova Natalia A NA Turakhia Mintu P MP Wang Paul J PJ

Circulation. Arrhythmia and electrophysiology 20200706 8

Artificial intelligence (AI) and machine learning (ML) in medicine are currently areas of intense exploration, showing potential to automate human tasks and even perform tasks beyond human capabilities. Literacy and understanding of AI/ML methods are becoming increasingly important to researchers and clinicians. The first objective of this review is to provide the novice reader with literacy of AI/ML methods and provide a foundation for how one might conduct an ML study. We provide a technical o ...[more]

PMID: 32628863

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Project description:IntroductionThe increasing availability of healthcare data and rapid development of big data analytic methods has opened new avenues for use of Artificial Intelligence (AI)- and Machine Learning (ML)-based technology in medical practice. However, applications at the point of care are still scarce.ObjectiveReview and discuss case studies to understand current capabilities for applying AI/ML in the healthcare setting, and regulatory requirements in the US, Europe and China.MethodsA targeted narrative literature review of AI/ML based digital tools was performed. Scientific publications (identified in PubMed) and grey literature (identified on the websites of regulatory agencies) were reviewed and analyzed.ResultsFrom the regulatory perspective, AI/ML-based solutions can be considered medical devices (i.e., Software as Medical Device, SaMD). A case series of SaMD is presented. First, tools for monitoring and remote management of chronic diseases are presented. Second, imaging applications for diagnostic support are discussed. Finally, clinical decision support tools to facilitate the choice of treatment and precision dosing are reviewed. While tested and validated algorithms for precision dosing exist, their implementation at the point of care is limited, and their regulatory and commercialization pathway is not clear. Regulatory requirements depend on the level of risk associated with the use of the device in medical practice, and can be classified into administrative (manufacturing and quality control), software-related (design, specification, hazard analysis, architecture, traceability, software risk analysis, cybersecurity, etc.), clinical evidence (including patient perspectives in some cases), non-clinical evidence (dosing validation and biocompatibility/toxicology) and other, such as e.g. benefit-to-risk determination, risk assessment and mitigation. There generally is an alignment between the US and Europe. China additionally requires that the clinical evidence is applicable to the Chinese population and recommends that a third-party central laboratory evaluates the clinical trial results.ConclusionsThe number of promising AI/ML-based technologies is increasing, but few have been implemented widely at the point of care. The need for external validation, implementation logistics, and data exchange and privacy remain the main obstacles.

Dataset Information

Artificial Intelligence and Machine Learning in Arrhythmias and Cardiac Electrophysiology.

Publications

Artificial Intelligence and Machine Learning in Arrhythmias and Cardiac Electrophysiology.

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