Medical multimodal large language models: A systematic review

Abstract

The rapid advancement of artificial intelligence (AI) has ushered in a new era of medical multimodal large language models (MLLMs), which integrate diverse data modalities such as text, imaging, physiological signals, and genomics to enhance clinical decision-making. This systematic review explores the core methodologies and applied research frontiers of medical MLLMs, focusing on their architecture, training methods, evaluation techniques, and applications. We highlight the transformative potential of MLLMs in achieving cross-modal semantic alignment, medical knowledge integration, and robust clinical reasoning. Despite their promise, challenges such as data heterogeneity, hallucination, and computational efficiency persist. By reviewing state-of-the-art solutions and future directions, this paper provides a comprehensive technical guide for developing reliable and interpretable medical MLLMs, ultimately aiming to bridge the gap between AI and clinical practice.