Integration of artificial intelligence into clinical patient management: focus on cardiac imaging
Cardiac imaging is a crucial component in the management of patients with heart disease, and as such it influences multiple, inter-related parts of the clinical workflow: physician-patient contact, image acquisition, image pre- and postprocessing, study reporting, diagnostics and outcome predictions...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Universitat Pompeu Fabra |
| Repositorio: | Repositorio Digital de la UPF |
| OAI Identifier: | oai:repositori.upf.edu:10230/46183 |
| Acceso en línea: | http://hdl.handle.net/10230/46183 http://dx.doi.org/10.1016/j.rec.2020.07.003 |
| Access Level: | acceso abierto |
| Palabra clave: | Artificial intelligence Machine learning Cardiology Cardiac imaging Inteligencia artificial Aprendizaje automático Cardiología Imagen cardiaca |
| Sumario: | Cardiac imaging is a crucial component in the management of patients with heart disease, and as such it influences multiple, inter-related parts of the clinical workflow: physician-patient contact, image acquisition, image pre- and postprocessing, study reporting, diagnostics and outcome predictions, medical interventions, and, finally, knowledge-building through clinical research. With the gradual and ubiquitous infiltration of artificial intelligence into cardiology, it has become clear that, when used appropriately, it will influence and potentially improve—through automation, standardization and data integration—all components of the clinical workflow. This review aims to present a comprehensive view of full integration of artificial intelligence into the standard clinical patient management—with a focus on cardiac imaging, but applicable to all information handling—and to discuss current barriers that remain to be overcome before its widespread implementation and integration. |
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