  Understanding 3D medical image volumes is a critical task in the medical
domain. However, existing 3D convolution and transformer-based methods have
limited semantic understanding of an image volume and also need a large set of
volumes for training. Recent advances in multi-modal large language models
(MLLMs) provide a new and promising way to understand images with the help of
text descriptions. However, most current MLLMs are designed for 2D natural
images. To enhance the 3D medical image understanding with 2D MLLMs, we propose
a novel pre-training framework called Med3DInsight, which marries existing 3D
image encoders with 2D MLLMs and bridges them via a designed Plane-Slice-Aware
Transformer (PSAT) module. Extensive experiments demonstrate our SOTA
performance on two downstream segmentation and classification tasks, including
three public datasets with CT and MRI modalities and comparison to more than
ten baselines. Med3DInsight can be easily integrated into any current 3D
medical image understanding network and improves its performance by a good
margin.
