Machine Intelligence with Generative AI is currently one of the most trending topics with its application in almost all fields of life. It is not only accelerating the development of new products in healthcare industry but also automatizing the generation of new and synthetic content making it easier to train and improve machine learning models. One of the biggest achievements of Generative AI has been in the healthcare domain with drug discovery, personalized care, differentially private synthetic data generation, operational efficiency, and many more.
Generative AI models like Generative Adversarial Networks, and Variational Autoencoders are employed to generate synthetic medical images, aiding in data augmentation, facilitating disease diagnosis, and enabling advanced medical imaging research. Additionally, generative AI techniques are utilized for creating realistic electronic health records (EHRs) and simulated patient data, supporting privacy-preserving data sharing, and empowering innovative studies for personalized medicine and drug development. NLP models like ClinicalBERT use transformer-based deep learning architecture to understand and represent contextual information in large clinical text datasets, such as electronic health records (EHRs) and medical literature, and can better grasp medical terminologies, domain-specific language, and contextual nuances that are unique to the healthcare field. In this presentation, we will delve into the realm of Machine Intelligence with Generative AI and explore its profound implications in the healthcare domain.

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