Translating Generative AI into Clinical Healthcare: A Comprehensive Review of Methods, Performance, and Challenges

Abstract

As generative artificial intelligence (GenAI) becomes entrenched in clinical and healthcare-related fields, there have been many advancements in the use of GenAI to produce realistic-looking medical imaging, clinical text, multimodal (multi-type) patient records, and time-dependent sets of biomedical data that allow real-time analysis of how diseases develop and progress. Rather than being constrained to existing datasets like traditional approaches (discriminative models), GenAI models the complete set of possible outcomes based on underlying patterns found in the input dataset, thereby allowing for more extensive data augmentation capability as well as better methods for preserving patient privacy and improving clinical decision-making. The current review outlines the technical aspects of three major types of generative AI (GANs, diffusion models, large language models), along with their application to various healthcare-related fields, specifically: clinical imaging, clinical documentation and report generation, clinical multimodal decision support for diagnostic and therapeutic interventions, and biomedical time-series modelling of patient's disease state within the continuum of care. The review will also provide an overview of key challenges related to data quality, interpretability, and ethical deployment, as well as future research directions needed for safe and effective clinical integration.

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