![[2510.02001] Generating Findings for Jaw Cysts in Dental Panoramic Radiographs Using a GPT-Based VLM: A Preliminary Study on Building a Two-Stage Self-Correction Loop with Structured Output (SLSO) Framework](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2510.02001] Generating Findings for Jaw Cysts in Dental Panoramic Radiographs Using a GPT-Based VLM: A Preliminary Study on Building a Two-Stage Self-Correction Loop with Structured Output (SLSO) Framework
Summary
This study explores a new Self-correction Loop with Structured Output (SLSO) framework to enhance the accuracy of AI-generated findings for jaw cysts in dental panoramic radiographs, demonstrating improved results over traditional methods.
Why It Matters
As AI continues to integrate into medical imaging, ensuring the reliability of AI-generated findings is crucial for clinical applications. This research addresses significant challenges in dental radiology, paving the way for more accurate diagnostic tools that could improve patient outcomes.
Key Takeaways
- The SLSO framework enhances accuracy in identifying dental pathologies compared to traditional methods.
- Significant improvements were noted in tooth number identification and root resorption assessment.
- The study establishes a foundation for future research with larger datasets to validate the framework's effectiveness.
Computer Science > Computer Vision and Pattern Recognition arXiv:2510.02001 (cs) [Submitted on 2 Oct 2025 (v1), last revised 17 Feb 2026 (this version, v3)] Title:Generating Findings for Jaw Cysts in Dental Panoramic Radiographs Using a GPT-Based VLM: A Preliminary Study on Building a Two-Stage Self-Correction Loop with Structured Output (SLSO) Framework Authors:Nanaka Hosokawa, Ryou Takahashi, Tomoya Kitano, Yukihiro Iida, Chisako Muramatsu, Tatsuro Hayashi, Yuta Seino, Xiangrong Zhou, Takeshi Hara, Akitoshi Katsumata, Hiroshi Fujita View a PDF of the paper titled Generating Findings for Jaw Cysts in Dental Panoramic Radiographs Using a GPT-Based VLM: A Preliminary Study on Building a Two-Stage Self-Correction Loop with Structured Output (SLSO) Framework, by Nanaka Hosokawa and 10 other authors View PDF Abstract:Vision-language models (VLMs) such as GPT (Generative Pre-Trained Transformer) have shown potential for medical image interpretation; however, challenges remain in generating reliable radiological findings in clinical practice, as exemplified by dental pathologies. This study proposes a Self-correction Loop with Structured Output (SLSO) framework as an integrated processing methodology to enhance the accuracy and reliability of AI-generated findings for jaw cysts in dental panoramic radiographs. Dental panoramic radiographs with jaw cysts were used to implement a 10-step integrated processing framework incorporating image analysis, structured data generation, toot...
