The Transformative Impact of Artificial Intelligence and Big Data on Enhancing Health Security and Pandemic Surveillance in Modern Healthcare Systems: Review

Abstract

Background: The integration of Artificial Intelligence (AI) and Big Data has revolutionized the healthcare sector, particularly in enhancing health security and pandemic surveillance. AI applications are crucial for analyzing vast datasets to improve decision-making, diagnosis, and treatment outcomes.

Methods: This review synthesizes existing literature on AI and Big Data applications in healthcare. It explores the efficacy of machine learning (ML) algorithms, data privacy concerns, and safety implications. The analysis includes various AI technologies, such as virtual and physical AI, and their roles in diagnostic processes, patient triage, and real-time decision support.

Results: Findings indicate that AI significantly improves disease prediction and diagnosis, operational efficiency, and patient management. For instance, machine learning algorithms demonstrated high accuracy in predicting health crises, such as chronic obstructive pulmonary disease flare-ups. However, challenges persist, including data privacy issues, bias in AI algorithms, and the need for comprehensive validation to ensure patient safety.

Conclusion: While AI and Big Data hold transformative potential for healthcare, their integration requires careful consideration of ethical, legal, and safety standards. Enhancing AI systems for practical application in healthcare settings is paramount to leverage their benefits effectively. Future research should focus on developing robust frameworks for AI implementation, addressing biases, and ensuring data security.