  With the significant advancements of Large Language Models (LLMs) in the
field of Natural Language Processing (NLP), the development of image-text
multimodal models has garnered widespread attention. Current surveys on
image-text multimodal models mainly focus on representative models or
application domains, but lack a review on how general technical models
influence the development of domain-specific models, which is crucial for
domain researchers. Based on this, this paper first reviews the technological
evolution of image-text multimodal models, from early explorations of feature
space to visual language encoding structures, and then to the latest large
model architectures. Next, from the perspective of technological evolution, we
explain how the development of general image-text multimodal technologies
promotes the progress of multimodal technologies in the biomedical field, as
well as the importance and complexity of specific datasets in the biomedical
domain. Then, centered on the tasks of image-text multimodal models, we analyze
their common components and challenges. After that, we summarize the
architecture, components, and data of general image-text multimodal models, and
introduce the applications and improvements of image-text multimodal models in
the biomedical field. Finally, we categorize the challenges faced in the
development and application of general models into external factors and
intrinsic factors, further refining them into 2 external factors and 5
intrinsic factors, and propose targeted solutions, providing guidance for
future research directions. For more details and data, please visit our GitHub
page: \url{https://github.com/i2vec/A-survey-on-image-text-multimodal-models}.
