Healthcare: A Growing Role for Large Language Models and Generative AI

Abstract

Abstract: Large language models and generative artificial intelligence (GAI) have recently demonstrated significant promise for revolutionizing a range of industries, including healthcare. The paper investigates how these cutting-edge AI developments are transforming healthcare applications. We focus on how big language models, like GPT-3 (generative pretrained transformer), Visual ChatGPT and generative AI, such Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), can be applied to solve important problems in the healthcare sector. Medical text analysis is one of the main uses of massive language models in the visual ChatGPT healthcare industry. These models have impressive natural language processing abilities that make it possible to effectively extract important information from electronic health records (EHRs), biomedical text data from large biobanks, scholarly articles and patient notes. The Biomedical Transformer Model represents a ground-breaking development in natural language processing for the biomedical field, exhibiting outstanding performance in comprehending and producing textual data. It opens up new avenues for biomedical research, diagnosis, and personalized therapy when combined with Multimodal Biomedical AI, which makes use of numerous data sources, including pictures, genomes, and clinical records. On the other side, generative AI has made great progress in medical picture analysis such as MRI scans and X-rays. The outstanding performance of GANs in medical picture synthesis and augmentation has helped to increase the precision and accuracy of diagnosis. Due to the issues with small and uneven medical datasets, VAEs have proven crucial in producing realistic medical images for training and research reasons. In addition to describing the various generative AI tools used in healthcare, this paper also provides an overview of multimodal medical LLMs and the biomedical transformer LLMs in the healthcare industry. Large language models and generative AI have great potential, but ethical issues and data privacy are still major problems in healthcare applications. Further, we investigate the potential role of multimodal medical LLMs as the foundation for novel assistive technologies in the fields of professional medicine, medical research, and consumer applications in the healthcare industry