# RetSTA: An LLM-Based Approach for Standardizing Clinical Fundus Image Reports

arXiv: https://arxiv.org/abs/2503.09358 · HTML: https://mlsanity.com/paper/2503.09358/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 30 references that are in the map.
- First posted on arXiv 12 Mar 2025.

## Builds on (most cited first, 14 of 14)

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- [Baichuan 2: Open Large-scale Language Models](https://mlsanity.com/paper/2309.10305/) (2023)
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- [VisionUnite: A Vision-Language Foundation Model for Ophthalmology Enhanced with Clinical Knowledge](https://mlsanity.com/paper/2408.02865/) (2024)
- [EyeGPT: Ophthalmic Assistant with Large Language Models](https://mlsanity.com/paper/2403.00840/) (2024)

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

- [CFCH: Coarse-Fine Collaborative Hierarchical Learning for Anterior Segment Disease Analysis](https://mlsanity.com/paper/2609.32559/) (2026)

## Abstract (arXiv)

Standardization of clinical reports is crucial for improving the quality of healthcare and facilitating data integration. The lack of unified standards, including format, terminology, and style, is a great challenge in clinical fundus diagnostic reports, which increases the difficulty for large language models (LLMs) to understand the data. To address this, we construct a bilingual standard terminology, containing fundus clinical terms and commonly used descriptions in clinical diagnosis. Then, we establish two models, RetSTA-7B-Zero and RetSTA-7B. RetSTA-7B-Zero, fine-tuned on an augmented dataset simulating clinical scenarios, demonstrates powerful standardization behaviors. However, it encounters a challenge of limitation to cover a wider range of diseases. To further enhance standardization performance, we build RetSTA-7B, which integrates a substantial amount of standardized data generated by RetSTA-7B-Zero along with corresponding English data, covering diverse complex clinical scenarios and achieving report-level standardization for the first time. Experimental results demonstrate that RetSTA-7B outperforms other compared LLMs in bilingual standardization task, which validates its superior performance and generalizability. The checkpoints are available at https://github.com/AB-Story/RetSTA-7B.