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.