# Leveraging Large Language Models to Effectively Generate Visual Data for Canine Musculoskeletal Diagnoses

arXiv: https://arxiv.org/abs/2509.12866 · HTML: https://mlsanity.com/paper/2509.12866/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 35 references that are in the map.
- First posted on arXiv 16 Sep 2025.

## Builds on (most cited first, 22 of 22)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
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- [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)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [GPQA: A Graduate-Level Google-Proof Q&A Benchmark](https://mlsanity.com/paper/2311.12022/) (2023)
- [Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters](https://mlsanity.com/paper/2408.03314/) (2024)
- [What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams](https://mlsanity.com/paper/2009.13081/) (2020)
- [AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration](https://mlsanity.com/paper/2306.00978/) (2023)
- [Phi-4 Technical Report](https://mlsanity.com/paper/2412.08905/) (2024)
- [Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models](https://mlsanity.com/paper/2312.06585/) (2023)
- [Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine](https://mlsanity.com/paper/2311.16452/) (2023)
- [On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey](https://mlsanity.com/paper/2406.15126/) (2024)
- [Capabilities of Gemini Models in Medicine](https://mlsanity.com/paper/2404.18416/) (2024)
- [Efficient Guided Generation for Large Language Models](https://mlsanity.com/paper/2307.09702/) (2023)
- [Let Me Speak Freely? A Study on the Impact of Format Restrictions on Performance of Large Language Models](https://mlsanity.com/paper/2408.02442/) (2024)
- [XGrammar: Flexible and Efficient Structured Generation Engine for Large Language Models](https://mlsanity.com/paper/2411.15100/) (2024)
- [Self-Guided Noise-Free Data Generation for Efficient Zero-Shot Learning](https://mlsanity.com/paper/2205.12679/) (2022)
- [Enhancing Canine Musculoskeletal Diagnoses: Leveraging Synthetic Image Data for Pre-Training AI-Models on Visual Documentations](https://mlsanity.com/paper/2409.08181/) (2024)

## Abstract (arXiv)

It is well-established that more data generally improves AI model performance. However, data collection can be challenging for certain tasks due to the rarity of occurrences or high costs. These challenges are evident in our use case, where we apply AI models to a novel approach for visually documenting the musculoskeletal condition of dogs. Here, abnormalities are marked as colored strokes on a body map of a dog. Since these strokes correspond to distinct muscles or joints, they can be mapped to the textual domain in which large language models (LLMs) operate. LLMs have demonstrated impressive capabilities across a wide range of tasks, including medical applications, offering promising potential for generating synthetic training data. In this work, we investigate whether LLMs can effectively generate synthetic visual training data for canine musculoskeletal diagnoses. For this, we developed a mapping that segments visual documentations into over 200 labeled regions representing muscles or joints. Using techniques like guided decoding, chain-of-thought reasoning, and few-shot prompting, we generated 1,000 synthetic visual documentations for patellar luxation (kneecap dislocation) diagnosis, the diagnosis for which we have the most real-world data. Our analysis shows that the generated documentations are sensitive to location and severity of the diagnosis while remaining independent of the dog's sex. We further generated 1,000 visual documentations for various other diagnoses to create a binary classification dataset. A model trained solely on this synthetic data achieved an F1 score of 88% on 70 real-world documentations. These results demonstrate the potential of LLM-generated synthetic data, which is particularly valuable for addressing data scarcity in rare diseases. While our methodology is tailored to the medical domain, the insights and techniques can be adapted to other fields.