Diagnosing Shoulder Disorders Using Multimodal Large Language Models and Consumer-Grade Cameras
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866917002909581312 |
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| author | Hong, Jindong Zhang, Wencheng Qiao, Shiqin Chen, Jianhai Qiu, Jianing Zheng, Chuanyang Xu, Qian Ji, Yun Wen, Qianyue Sun, Weiwei Li, Hao Li, Huizhen Wang, Huichao Wu, Kai Li, Meng He, Yijun Luo, Lingjie Sun, Jiankai |
| author_facet | Hong, Jindong Zhang, Wencheng Qiao, Shiqin Chen, Jianhai Qiu, Jianing Zheng, Chuanyang Xu, Qian Ji, Yun Wen, Qianyue Sun, Weiwei Li, Hao Li, Huizhen Wang, Huichao Wu, Kai Li, Meng He, Yijun Luo, Lingjie Sun, Jiankai |
| contents | Shoulder disorders, such as frozen shoulder (a.k.a., adhesive capsulitis), are common conditions affecting the health of people worldwide, and have a high incidence rate among the elderly and workers engaged in repetitive shoulder tasks. In regions with scarce medical resources, achieving early and accurate diagnosis poses significant challenges, and there is an urgent need for low-cost and easily scalable auxiliary diagnostic solutions. This research introduces videos captured by consumer-grade devices as the basis for diagnosis, reducing the cost for users. We focus on the innovative application of Multimodal Large Language Models (MLLMs) in the preliminary diagnosis of shoulder disorders and propose a Hybrid Motion Video Diagnosis framework (HMVDx). This framework divides the two tasks of action understanding and disease diagnosis, which are respectively completed by two MLLMs. In addition to traditional evaluation indicators, this work proposes a novel metric called Usability Index by the logical process of medical decision-making (action recognition, movement diagnosis, and final diagnosis). This index evaluates the effectiveness of MLLMs in the medical field from the perspective of the entire medical diagnostic pathway, revealing the potential value of low-cost MLLMs in medical applications for medical practitioners. In experimental comparisons, the accuracy of HMVDx in diagnosing shoulder joint injuries has increased by 79.6\% compared with direct video diagnosis, a significant technical contribution to future research on the application of MLLMs for video understanding in the medical field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_09230 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Diagnosing Shoulder Disorders Using Multimodal Large Language Models and Consumer-Grade Cameras Hong, Jindong Zhang, Wencheng Qiao, Shiqin Chen, Jianhai Qiu, Jianing Zheng, Chuanyang Xu, Qian Ji, Yun Wen, Qianyue Sun, Weiwei Li, Hao Li, Huizhen Wang, Huichao Wu, Kai Li, Meng He, Yijun Luo, Lingjie Sun, Jiankai Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language Machine Learning Shoulder disorders, such as frozen shoulder (a.k.a., adhesive capsulitis), are common conditions affecting the health of people worldwide, and have a high incidence rate among the elderly and workers engaged in repetitive shoulder tasks. In regions with scarce medical resources, achieving early and accurate diagnosis poses significant challenges, and there is an urgent need for low-cost and easily scalable auxiliary diagnostic solutions. This research introduces videos captured by consumer-grade devices as the basis for diagnosis, reducing the cost for users. We focus on the innovative application of Multimodal Large Language Models (MLLMs) in the preliminary diagnosis of shoulder disorders and propose a Hybrid Motion Video Diagnosis framework (HMVDx). This framework divides the two tasks of action understanding and disease diagnosis, which are respectively completed by two MLLMs. In addition to traditional evaluation indicators, this work proposes a novel metric called Usability Index by the logical process of medical decision-making (action recognition, movement diagnosis, and final diagnosis). This index evaluates the effectiveness of MLLMs in the medical field from the perspective of the entire medical diagnostic pathway, revealing the potential value of low-cost MLLMs in medical applications for medical practitioners. In experimental comparisons, the accuracy of HMVDx in diagnosing shoulder joint injuries has increased by 79.6\% compared with direct video diagnosis, a significant technical contribution to future research on the application of MLLMs for video understanding in the medical field. |
| title | Diagnosing Shoulder Disorders Using Multimodal Large Language Models and Consumer-Grade Cameras |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2510.09230 |