A Review of Generative Adversarial-Based Networks of Machine Learning/Artificial Intelligence in Healthcare

Abstract

Machine learning has been proven to be a game-changing technology in every domain since the late 20th century. There have been many advancements in healthcare not only for the diagnosis of disease but advanced in the prognosis of the diseases. Artificial intelligence/machine learning (AI/ML) has progressed a lot in the medical domain in just a couple of decades and played a very important role in exploring human data to understand human body behavior better than ever before, for predicting and classifying all kinds of medical images or videos. A recent and best-used application is detecting COVID-19 by just checking the chest x-ray in a very accurate manner that can be used without human presence and stop the spread of the virus resulting in fewer doctors getting affected. It is known as generative adversarial networks. Some of the types of GANs used for differentiate domains without human supervision and many such mutations of GANs are useful in the health sector. This is simply a quick review of various technologies that will become more in-depth as time goes on.

Usage of Generative AI in Healthcare: An Implementation Science Informed Translational Path on Application, Integration and Governance

“…Generative models can be trained on vast datasets of medical records and imagery (like MRIs and CT scans) to identify patterns related to diseases. For instance, GANs have been used for image reconstruction, synthesis, segmentation, registration and classification [5,9,37,39]. Moreover, GANs can be used to generate synthetic medical images that can be used to train machine learning models for image-based diagnosis or augment medical datasets.…”

“…Generative AI is proving to be a change catalyst across various industries, and the healthcare sector is no exception [8]. With its remarkable ability to analyse extensive datasets and generate valuable insights, generative AI has emerged as a powerful tool in enhancing patient care [9], revolutionizing disease diagnosis [10] and expanding treatment options [11]. By harnessing the potential of this cutting-edge technology, healthcare professionals can now access unprecedented levels of accuracy, efficiency and innovation in their practices.…”