Year: 2024
Type: article
Study design: Systematic Review
Abstract: Generative Artificial Intelligence (GenAI) has emerged as a transformative technology in healthcare transforming various aspects of the healthcare industry, such as medical imaging, drug design, synthetic data generation, etc. In this systematic review, we analyze 87 research articles that explore the application of GenAI technologies such as Generative Adversarial Networks (GANs), Diffusion Models, Large Language Models (LLMs), and Variational Autoencoders (VAEs) across these domains. This review addresses several key research questions: which generative models are most frequently used in these applications, the main applications of GenAI in healthcare, the datasets that facilitate their development, and the evaluation metrics used to assess these models. Our findings indicate that GANs, Diffusion Models, LLMs, and VAEs are the predominant models applied in healthcare. Additionally, we have provided a brief summary of each research article, focusing on their contributions to the field. We have specifically selected only original research studies for inclusion, ensuring the relevance and credibility of the papers reviewed. This review serves as a foundation for understanding the current state of GenAI in healthcare.
Authors: Sandeep Chataut, Sirjana Bhatta, Bishwambhar Dahal, Grishma Ojha, Bigyan Subedi, Bijay Bastakoti
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4405944849