Generative AI Revolution

Abstract

A scarcity of data within the healthcare sector can present considerable obstacles for a range of applications, most notably in the implementation and advancement of machine learning models. Sometimes, inadequate data sets may also lead to the wrong interpretation of data with wider patient populations, forcing the models to be biased. Overfitting, where a model acquires knowledge about the features of the training data rather than underlying patterns. In cases of overfit, the models perform well on the training data, but they face difficulty with novel and unknown data. Generative adversarial networks (GANs) play a vital role in healthcare by accelerating medical research and diagnosis. Though GAN has evidenced, genuineness of the data they provide, need to adjust to the regulations to ensure the privacy and security of patient information. This chapter provides an overview of current research and mutual efforts between the medical and artificial intelligence (AI) communities to maximize the potential of GANs to address healthcare challenges.