Recent Advances in Generative AI for Healthcare Applications.
Authors
Affiliations (6)
Affiliations (6)
- Department of Biomedical Engineering and Science, Florida Institute of Technology, Melbourne, FL, 32901, USA.
- Department of Mathematics and Systems Engineering, Florida Institute of Technology, Melbourne, FL, 32901, USA.
- Department of Data Science and Analytics, Kennesaw State University, Kennesaw, GA, 30144, USA.
- Department of Ira A. Fulton Schools of Engineering, Arizona State University, Tempe, AZ, 85281, USA.
- Department of Mathematics and Systems Engineering, Florida Institute of Technology, Melbourne, FL, 32901, USA. [email protected].
- Department of Biomedical Engineering and Science, Florida Institute of Technology, Melbourne, FL, 32901, USA. [email protected].
Abstract
Artificial intelligence (AI) has catalyzed revolutionary changes across various sectors, notably in healthcare. In particular, generative AI-led by diffusion models and transformer architectures-has enabled significant breakthroughs in medical imaging (including image reconstruction, image-to-image translation, generation, and classification), protein structure prediction, clinical documentation, diagnostic assistance, radiology interpretation, clinical decision support, medical coding, and billing, as well as drug design and molecular representation. These innovations have enhanced clinical diagnosis, data reconstruction, and drug synthesis. This review paper aims to offer a comprehensive synthesis of recent advances in healthcare applications of generative AI, with an emphasis on diffusion and transformer models. Moreover, we discuss current capabilities, limitations, and outline promising research directions. Serving as both a reference for researchers and a guide for practitioners, this work offers an integrated view of the state of the art, its impact on healthcare, and its future potential.