A Review of AI-Driven Digital Twin Frameworks for Cardiovascular Disease Diagnosis and Management
2024 IEEE 65th International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS 2024): Proceedings
2024
Marta Narigina,
Andrejs Romānovs,
Jurijs Merkurjevs
The combination of Artificial Intelligence (AI) and Digital Twin (DT) technologies in healthcare could revolutionize the administration and treatment of intricate conditions, including myocardial infarction and stroke. This study offers an extensive analysis of contemporary methodologies and examines the prospects of a conceptual AIdriven digital twin framework for healthcare applications. The proposed system integrates real-time data, machine learning algorithms, and sophisticated computational methods to improve diagnostic accuracy and refine treatment approaches. Although current literature illustrates the efficacy of AI and digital technologies in customized medicine, substantial obstacles persist in data integration, processing capacity, and ethical issues. This study clarifies the present condition of AIdriven digital twin technologies and delineates critical domains for prospective research and development. The objective is to create a basis for enhancing the incorporation of these technologies in healthcare to optimize patient outcomes and clinical decision-making.
Keywords
Ethics , Technological innovation , Reviews , Data integration , Medical services , Machine learning , Myocardium , Real-time systems , Digital twins , Medical diagnostic imaging
DOI
10.1109/ITMS64072.2024.10741948
Hyperlink
https://ieeexplore.ieee.org/document/10741948
Narigina, M., Romānovs, A., Merkurjevs, J. A Review of AI-Driven Digital Twin Frameworks for Cardiovascular Disease Diagnosis and Management. In: 2024 IEEE 65th International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS 2024): Proceedings, Latvia, Riga, 3-4 October, 2024. Piscataway: IEEE, 2024, pp.86-91. ISBN 979-8-3315-3384-7. e-ISBN 979-8-3315-3383-0. ISSN 2771-6953. e-ISSN 2771-6937. Available from: doi:10.1109/ITMS64072.2024.10741948
Publication language
English (en)