Recent Advances in Generative AI for Healthcare Applications

TL;DR

This review summarizes recent progress in generative AI, especially diffusion and transformer models, transforming healthcare through improved imaging, diagnosis, drug design, and clinical documentation.

Contribution

It provides a comprehensive synthesis of recent advances, current limitations, and future research directions in generative AI applications for healthcare.

Findings

Enhanced medical imaging and diagnosis capabilities

Advances in protein structure prediction and drug design

Identification of current limitations and future challenges

Abstract

The rapid advancement of 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, highlight existing limitations, and outline promising research directions to address emerging challenges. 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.