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.

翻译：肩部疾病（如冻结肩，又称粘连性囊炎）是影响全球人群健康的常见病症，在老年群体及从事重复性肩部作业的劳动者中具有高发病率。在医疗资源匮乏地区，实现早期精准诊断面临重大挑战，亟需低成本、易扩展的辅助诊断方案。本研究引入消费级设备拍摄的视频作为诊断依据，降低用户使用成本。我们聚焦于多模态大语言模型在肩部疾病初诊中的创新应用，提出混合运动视频诊断框架。该框架将动作理解与疾病诊断两项任务解耦，分别由两个多模态大语言模型完成。除传统评估指标外，本研究依据医疗决策的逻辑流程（动作识别、运动诊断与最终诊断）提出名为"可用性指数"的新型评估指标。该指标从完整医疗诊断路径的视角评估多模态大语言模型在医疗领域的有效性，为医疗从业者揭示了低成本多模态大语言模型在医疗应用中的潜在价值。实验对比表明，该框架在肩关节损伤诊断中的准确率较直接视频诊断提升79.6\%，这对未来多模态大语言模型在医疗视频理解领域的应用研究具有显著技术贡献。