Applications of Large Models in Medicine
Fuente:
arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
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| _version_ | 1866911194260963328 |
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| author | Su, YunHe Lu, Zhengyang Liu, Junhui Pang, Ke Dai, Haoran Liu, Sa Jia, Yuxin Ge, Lujia Yang, Jing-min |
| author_facet | Su, YunHe Lu, Zhengyang Liu, Junhui Pang, Ke Dai, Haoran Liu, Sa Jia, Yuxin Ge, Lujia Yang, Jing-min |
| contents | This paper explores the advancements and applications of large-scale models in the medical field, with a particular focus on Medical Large Models (MedLMs). These models, encompassing Large Language Models (LLMs), Vision Models, 3D Large Models, and Multimodal Models, are revolutionizing healthcare by enhancing disease prediction, diagnostic assistance, personalized treatment planning, and drug discovery. The integration of graph neural networks in medical knowledge graphs and drug discovery highlights the potential of Large Graph Models (LGMs) in understanding complex biomedical relationships. The study also emphasizes the transformative role of Vision-Language Models (VLMs) and 3D Large Models in medical image analysis, anatomical modeling, and prosthetic design. Despite the challenges, these technologies are setting new benchmarks in medical innovation, improving diagnostic accuracy, and paving the way for personalized healthcare solutions. This paper aims to provide a comprehensive overview of the current state and future directions of large models in medicine, underscoring their significance in advancing global health. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_17132 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Applications of Large Models in Medicine Su, YunHe Lu, Zhengyang Liu, Junhui Pang, Ke Dai, Haoran Liu, Sa Jia, Yuxin Ge, Lujia Yang, Jing-min Artificial Intelligence This paper explores the advancements and applications of large-scale models in the medical field, with a particular focus on Medical Large Models (MedLMs). These models, encompassing Large Language Models (LLMs), Vision Models, 3D Large Models, and Multimodal Models, are revolutionizing healthcare by enhancing disease prediction, diagnostic assistance, personalized treatment planning, and drug discovery. The integration of graph neural networks in medical knowledge graphs and drug discovery highlights the potential of Large Graph Models (LGMs) in understanding complex biomedical relationships. The study also emphasizes the transformative role of Vision-Language Models (VLMs) and 3D Large Models in medical image analysis, anatomical modeling, and prosthetic design. Despite the challenges, these technologies are setting new benchmarks in medical innovation, improving diagnostic accuracy, and paving the way for personalized healthcare solutions. This paper aims to provide a comprehensive overview of the current state and future directions of large models in medicine, underscoring their significance in advancing global health. |
| title | Applications of Large Models in Medicine |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2502.17132 |