Generative AI for Healthcare Analytics: Investigating Applications in Disease Diagnosis, Patient Outcomes, and Personalized Medicine

Abstract

The integration of generative artificial intelligence (AI) into healthcare analytics represents a transformative shift in the way medical data is utilized for disease diagnosis, patient outcomes, and personalized medicine. This paper investigates the applications of generative AI, focusing on its ability to synthesize patient data, enhance diagnostic accuracy, and tailor treatment plans to individual needs. Through a comprehensive literature review, the study identifies existing methodologies and highlights the potential of generative models, such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs), in generating synthetic datasets that can augment real-world clinical data. A case study utilizing the "Heart Disease UCI" dataset demonstrates the effectiveness of generative AI in producing high-fidelity synthetic data, which can be instrumental in training machine learning models while addressing data scarcity and privacy concerns. Despite the promising findings, the research uncovers significant gaps in empirical studies assessing the real-world impact of generative AI in clinical settings. This paper emphasizes the need for further exploration into the ethical implications and practical integration of generative AI technologies in healthcare, ultimately aiming to enhance patient care and optimize healthcare delivery systems.

Keywords: Generative AI, healthcare analytics, disease diagnosis, patient outcomes, personalized medicine, machine learning, data-driven decision-making