用大模型统一眼底图像报告术语和格式，提升医疗数据可用性
RetSTA: An LLM-Based Approach for Standardizing Clinical Fundus Image Reports
- 基于双语术语库构建大模型，模拟临床场景进行微调
- 新模型首次实现跨语言报告级标准化，准确率显著优于现有模型
- 适合医疗AI研究者、眼科医生及医学文本处理开发者使用
临床报告标准化对提升医疗质量与数据整合至关重要。当前眼底诊断报告在格式、术语和风格上缺乏统一标准，给大语言模型理解带来挑战。为此，我们构建了包含眼底临床术语与常用描述的双语标准术语库，并建立了两个模型：RetSTA-7B-Zero 和 RetSTA-7B。前者在模拟临床场景的增强数据集上微调后展现出强大的标准化能力，但覆盖疾病范围有限。为进一步提升性能，我们构建了 RetSTA-7B，融合 RetSTA-7B-Zero 生成的大量标准化数据及对应英文数据，涵盖多样复杂临床场景，首次实现报告级别的标准化。实验表明，RetSTA-7B 在双语标准化任务中优于其他对比模型，验证了其卓越性能与泛化能力。模型检查点已公开于 https://github.com/AB-Story/RetSTA-7B。
原文摘要 · Abstract (English)
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.
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