Designing medical artificial intelligence systems for global use: focus on interoperability, scalability, and accessibility.
Hellenic J Cardiol
; 2024 Jul 16.
Article
en En
| MEDLINE
| ID: mdl-39025234
ABSTRACT
Advances in artificial intelligence (AI) and machine learning systems promise faster, more efficient and more personalized care. While many of these models are built on the premise of improving access to the timely screening, diagnosis, and treatment of cardiovascular disease, their validity and accessibility across diverse and international cohorts remains unknown. In this mini-review article, we summarize key obstacles in the effort to design AI systems that will be scalable, accessible, and accurate across distinct geographical and temporal settings. We discuss representativeness, interoperability, quality assurance and the importance of vendor-agnostic data types that will be immediately available to end-users across the globe. These topics illustrate how the timely integration of these principles into AI development is crucial to maximizing the global benefits of AI in cardiology.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Idioma:
En
Revista:
Hellenic J Cardiol
Asunto de la revista:
CARDIOLOGIA
Año:
2024
Tipo del documento:
Article