TY - JOUR
T1 - Artificial intelligence to enhance clinical value across the spectrum of cardiovascular healthcare
AU - BigData@Heart Consortium and the cardAIc group
AU - Gill, Simrat K
AU - Karwath, Andreas
AU - Uh, Hae-Won
AU - Cardoso, Victor Roth
AU - Gu, Zhujie
AU - Barsky, Andrey
AU - Slater, Luke
AU - Acharjee, Animesh
AU - Duan, Jinming
AU - Dall'Olio, Lorenzo
AU - El Bouhaddani, Said
AU - Chernbumroong, Saisakul
AU - Stanbury, Mary
AU - Haynes, Sandra
AU - Asselbergs, Folkert W
AU - Grobbee, Diederick E
AU - Eijkemans, Marinus J C
AU - Gkoutos, Georgios V
AU - Kotecha, Dipak
N1 - © The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology.
PY - 2023/3/1
Y1 - 2023/3/1
N2 - 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. Despite the growing use of AI, further education for healthcare workers, researchers, and the public are needed to aid understanding of how AI works and to close the existing gap in knowledge. In addition, issues regarding data access, sharing, and security must be addressed to ensure full engagement by patients and the public. The application of AI within healthcare provides an opportunity for clinicians to deliver a more personalized approach to medical care by accounting for confounders, interactions, and the rising prevalence of multi-morbidity.
AB - 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. Despite the growing use of AI, further education for healthcare workers, researchers, and the public are needed to aid understanding of how AI works and to close the existing gap in knowledge. In addition, issues regarding data access, sharing, and security must be addressed to ensure full engagement by patients and the public. The application of AI within healthcare provides an opportunity for clinicians to deliver a more personalized approach to medical care by accounting for confounders, interactions, and the rising prevalence of multi-morbidity.
KW - Algorithms
KW - Artificial Intelligence
KW - Cardiovascular System
KW - Delivery of Health Care
KW - Humans
KW - Machine Learning
U2 - https://doi.org/10.1093/eurheartj/ehac758
DO - https://doi.org/10.1093/eurheartj/ehac758
M3 - Article
C2 - 36629285
SN - 0195-668X
VL - 44
SP - 713
EP - 725
JO - European Heart journal
JF - European Heart journal
IS - 9
ER -