Biological Security using Generative AI in Healthcare

Abstract

The integration of Generative Artificial Intelligence (AI) in healthcare represents a groundbreaking advancement in enhancing biological security, a critical aspect of public health that addresses the prevention of biological threats such as infectious diseases and bioterrorism. This research paper explores the multifaceted applications of generative AI technologies in bolstering biological security, focusing on disease prediction, drug discovery, and bioinformatics. Through a comprehensive literature review, I identify existing gaps in the current research landscape, particularly in the practical implementation of generative AI in real-world biological security scenarios. My proposed methodology employs a mixed-methods approach, utilizing case studies and quantitative analyses to evaluate the effectiveness of various generative AI models, including Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). The results indicate that generative AI can significantly enhance the accuracy and efficiency of medical research, leading to improved health outcomes and a more robust biological security framework. This paper underscores the potential of generative AI to transform medical sciences while addressing ethical considerations and data privacy concerns, ultimately paving the way for innovative solutions in the fight against biological threats.

Keywords: Generative AI, Biological Security, Medical Sciences, Disease Prediction, Drug Discovery, Bioinformatics, Machine Learning, Healthcare Technology