The paper "M3D: Advancing 3D Medical Image Analysis with Multi-Modal Large Language Models" by Fan Bai, Yuxin Du, Tiejun Huang, Max Q.-H. Meng, and Bo Zhao introduces a comprehensive approach to advancing 3D medical image analysis using multi-modal large language models (MLLMs). The authors address the under-explored area of 3D medical images, which contain rich spatial information, and propose a large-scale 3D multi-modal medical dataset, M3D-Data, comprising 120K image-text pairs and 662K instruction-response pairs. They also introduce M3D-LaMed, a versatile multi-modal large language model designed for 3D medical image analysis, and M3D-Bench, a benchmark for evaluating various 3D medical tasks. Key contributions include: 1. **M3D-Data**: A large-scale 3D multi-modal medical dataset with 120K image-text pairs and 662K instruction-response pairs, supporting tasks such as image-text retrieval, report generation, visual question answering, positioning, and segmentation. 2. **M3D-LaMed**: A versatile multi-modal large language model that integrates 3D medical images and text data, capable of handling various 3D medical tasks. 3. **M3D-Bench**: A comprehensive benchmark for evaluating the performance of 3D medical image analysis models across eight tasks. The paper details the dataset construction, model architecture, and evaluation methods, demonstrating that M3D-LaMed outperforms existing solutions in various tasks. The authors also provide qualitative and quantitative results to validate the effectiveness of their approach, including comparisons with previous models and ablation studies. The code, data, and models are publicly available to facilitate further research and applications in 3D medical image analysis.The paper "M3D: Advancing 3D Medical Image Analysis with Multi-Modal Large Language Models" by Fan Bai, Yuxin Du, Tiejun Huang, Max Q.-H. Meng, and Bo Zhao introduces a comprehensive approach to advancing 3D medical image analysis using multi-modal large language models (MLLMs). The authors address the under-explored area of 3D medical images, which contain rich spatial information, and propose a large-scale 3D multi-modal medical dataset, M3D-Data, comprising 120K image-text pairs and 662K instruction-response pairs. They also introduce M3D-LaMed, a versatile multi-modal large language model designed for 3D medical image analysis, and M3D-Bench, a benchmark for evaluating various 3D medical tasks. Key contributions include: 1. **M3D-Data**: A large-scale 3D multi-modal medical dataset with 120K image-text pairs and 662K instruction-response pairs, supporting tasks such as image-text retrieval, report generation, visual question answering, positioning, and segmentation. 2. **M3D-LaMed**: A versatile multi-modal large language model that integrates 3D medical images and text data, capable of handling various 3D medical tasks. 3. **M3D-Bench**: A comprehensive benchmark for evaluating the performance of 3D medical image analysis models across eight tasks. The paper details the dataset construction, model architecture, and evaluation methods, demonstrating that M3D-LaMed outperforms existing solutions in various tasks. The authors also provide qualitative and quantitative results to validate the effectiveness of their approach, including comparisons with previous models and ablation studies. The code, data, and models are publicly available to facilitate further research and applications in 3D medical image analysis.