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Artificial intelligence to enhance clinical value across the spectrum of cardiovascular healthcare

Asselbergs, Folkert; Gill, Simrat K; (2023) Artificial intelligence to enhance clinical value across the spectrum of cardiovascular healthcare. European Heart Journal , 44 pp. 713-725. 10.1093/eurheartj/ehac758. Green open access

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Abstract

Artificial intelligence (AI) is increasingly being utilized in healthcare. This article provides clinicians and researchers with a step-wise foundation for high-value AI that can be applied to a variety of different data modalities. The aim is to improve the transparency and application of AI methods, with the potential to benefit patients in routine cardiovascular care. Following a clear research hypothesis, an AI-based workflow begins with data selection and pre-processing prior to analysis, with the type of data (structured, semi-structured, or unstructured) determining what type of pre-processing steps and machine-learning algorithms are required. Algorithmic and data validation should be performed to ensure the robustness of the chosen methodology, followed by an objective evaluation of performance. Seven case studies are provided to highlight the wide variety of data modalities and clinical questions that can benefit from modern AI techniques, with a focus on applying them to cardiovascular disease management.

Type: Article
Title: Artificial intelligence to enhance clinical value across the spectrum of cardiovascular healthcare
Open access status: An open access version is available from UCL Discovery
DOI: 10.1093/eurheartj/ehac758
Publisher version: https://doi.org/10.1093/eurheartj/ehac758
Language: English
Additional information: © The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Keywords: Artificial intelligence • Healthcare • Management • Treatment
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics > Clinical Epidemiology
URI: https://discovery.ucl.ac.uk/id/eprint/10162272
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