Opportunities and challenges in the application of large artificial intelligence models in radiology
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909149158178816 |
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| author | Pan, Liangrui Zhao, Zhenyu Lu, Ying Tang, Kewei Fu, Liyong Liang, Qingchun Peng, Shaoliang |
| author_facet | Pan, Liangrui Zhao, Zhenyu Lu, Ying Tang, Kewei Fu, Liyong Liang, Qingchun Peng, Shaoliang |
| contents | Influenced by ChatGPT, artificial intelligence (AI) large models have witnessed a global upsurge in large model research and development. As people enjoy the convenience by this AI large model, more and more large models in subdivided fields are gradually being proposed, especially large models in radiology imaging field. This article first introduces the development history of large models, technical details, workflow, working principles of multimodal large models and working principles of video generation large models. Secondly, we summarize the latest research progress of AI large models in radiology education, radiology report generation, applications of unimodal and multimodal radiology. Finally, this paper also summarizes some of the challenges of large AI models in radiology, with the aim of better promoting the rapid revolution in the field of radiography. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_16112 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Opportunities and challenges in the application of large artificial intelligence models in radiology Pan, Liangrui Zhao, Zhenyu Lu, Ying Tang, Kewei Fu, Liyong Liang, Qingchun Peng, Shaoliang Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Influenced by ChatGPT, artificial intelligence (AI) large models have witnessed a global upsurge in large model research and development. As people enjoy the convenience by this AI large model, more and more large models in subdivided fields are gradually being proposed, especially large models in radiology imaging field. This article first introduces the development history of large models, technical details, workflow, working principles of multimodal large models and working principles of video generation large models. Secondly, we summarize the latest research progress of AI large models in radiology education, radiology report generation, applications of unimodal and multimodal radiology. Finally, this paper also summarizes some of the challenges of large AI models in radiology, with the aim of better promoting the rapid revolution in the field of radiography. |
| title | Opportunities and challenges in the application of large artificial intelligence models in radiology |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2403.16112 |