# Diagnosing Shoulder Disorders Using Multimodal Large Language Models and Consumer-Grade Cameras

arXiv: https://arxiv.org/abs/2510.09230 · HTML: https://mlsanity.com/paper/2510.09230/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 37 references that are in the map.
- First posted on arXiv 10 Oct 2025.

## Builds on (most cited first, 28 of 28)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Automatic Chain of Thought Prompting in Large Language Models](https://mlsanity.com/paper/2210.03493/) (2022)
- [EgoSchema: A Diagnostic Benchmark for Very Long-form Video Language Understanding](https://mlsanity.com/paper/2308.09126/) (2023)
- [Capabilities of Gemini Models in Medicine](https://mlsanity.com/paper/2404.18416/) (2024)
- [Foundation Models in Robotics: Applications, Challenges, and the Future](https://mlsanity.com/paper/2312.07843/) (2023)
- [Progressive-Hint Prompting Improves Reasoning in Large Language Models](https://mlsanity.com/paper/2304.09797/) (2023)
- [A Survey on Data Collection for Machine Learning: a Big Data -- AI Integration Perspective](https://mlsanity.com/paper/1811.03402/) (2018)
- [Large AI Models in Health Informatics: Applications, Challenges, and the Future](https://mlsanity.com/paper/2303.11568/) (2023)
- [MEDCO: Medical Education Copilots Based on A Multi-Agent Framework](https://mlsanity.com/paper/2408.12496/) (2024)
- [Lyra: Orchestrating Dual Correction in Automated Theorem Proving](https://mlsanity.com/paper/2309.15806/) (2023)
- [AI Hiring with LLMs: A Context-Aware and Explainable Multi-Agent Framework for Resume Screening](https://mlsanity.com/paper/2504.02870/) (2025)
- [SeqPO-SiMT: Sequential Policy Optimization for Simultaneous Machine Translation](https://mlsanity.com/paper/2505.20622/) (2025)
- [Decoder-Hybrid-Decoder Architecture for Efficient Reasoning with Long Generation](https://mlsanity.com/paper/2507.06607/) (2025)
- [Dietary Assessment with Multimodal ChatGPT: A Systematic Analysis](https://mlsanity.com/paper/2312.08592/) (2023)
- [DAPE V2: Process Attention Score as Feature Map for Length Extrapolation](https://mlsanity.com/paper/2410.04798/) (2024)
- [Emerging Cyber Attack Risks of Medical AI Agents](https://mlsanity.com/paper/2504.03759/) (2025)
- [SynapseRoute: An Auto-Route Switching Framework on Dual-State Large Language Model](https://mlsanity.com/paper/2507.02822/) (2025)
- [Understanding the Mixture-of-Experts with Nadaraya-Watson Kernel](https://mlsanity.com/paper/2509.25913/) (2025)
- [SAS: Simulated Attention Score](https://mlsanity.com/paper/2507.07694/) (2025)
- [OrthoDoc: Multimodal Large Language Model for Assisting Diagnosis in Computed Tomography](https://mlsanity.com/paper/2409.09052/) (2024)

## Built on by (most cited first, 1 of 1)

- [Benchmarking the Thinking Mode of Multimodal Large Language Models in Clinical Tasks](https://mlsanity.com/paper/2511.03328/) (2025)

## Abstract (arXiv)

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.