ARTIFICIAL INTELLIGENCE AND BIG DATA IN INFECTIOUS DISEASES: GLOBAL LANDSCAPE, CHALLENGES, AND PERSPECTIVES IN VIETNAM
Main Article Content
Abstract
Artificial intelligence (AI) and Big Data are reshaping modern infectious disease management through advances in medical imaging, early sepsis detection, outbreak forecasting, treatment optimization, and antimicrobial stewardship. The rapid expansion of electronic health records, laboratory information systems, imaging archives, genomic datasets, and epidemiological surveillance networks has created large-scale data environments suitable for machine learning and deep learning applications. Globally, AI has demonstrated strong performance in detecting pulmonary tuberculosis and pneumonia, predicting dengue severity, supporting COVID-19 surveillance, and accelerating drug and vaccine development.
In Vietnam, AI-assisted radiology, intensive care monitoring tools, and electronic antimicrobial stewardship systems have begun to emerge in major hospitals. National digital health initiatives and infectious disease reporting systems are gradually establishing the infrastructure required for broader AI integration. However, significant challenges remain, including limited data standardization, fragmented health information systems, lack of interoperability, concerns regarding model transparency, and shortages of interdisciplinary expertise. Ethical and legal issues related to patient privacy and data security further hinder large-scale implementation.
This review summarizes key applications of AI and Big Data in infectious diseases, evaluates the current status of adoption in Vietnam, and discusses future opportunities and challenges for integrating AI into clinical practice and public health systems.
Keywords
Artificial intelligence, Big Data, Infectious diseases, Machine learning, Deep learning
Article Details
References
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