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Ophtimus-V2-Tx:一个紧的专用领域LLM用于眼科诊断和治疗规划

Minwook Kwon¹, Kuk Jin Jang², Seung Ju Baek¹

¹Department of AI Convergence Engineering, Gyeongsang National University, Jinju, South Korea.

Scientific reports

|December 10, 2025

概括

眼科专业的大型语言模型 (LLM) Ophtimus-V2-Tx在临床决策支持方面表现出强的表现. 这种适应领域的LLM取得了与一般模型相比具有竞争力的结果,特别是在眼科病例特定推理方面.

科学领域:

人工智能在医学中的应用
眼科信息学信息学
临床决策支持系统临床决策支持系统

背景情况:

大型语言模型 (LLM) 提供了临床决策支持的潜力,但在具体案例推理方面面临挑战.
开发专门的LLM可以提高医疗领域的表现.

研究的目的:

推出Ophtimus-V2-Tx,一个为眼科微调的80亿参数LLM.
根据最先进的一般LLM和眼科特定基线评估其表现.

主要方法:

精细调整一个80亿参数的LLM在10,000多个眼科病例报告.
通过使用ROUGE-L,BLEU和METEOR指标评估文本生成.
评估使用CliBench和等级F1分数的临床代码映射 (ICD-10-CM,ATC,ICD-10-PCS).

主要成果:

与一般的LLM相比,Ophtimus-V2-Tx显示了改进的文本指标 (ROUGE-L 0.40,BLEU 0.26,METEOR 0.45) 与一般的LLM相比.
在二次诊断方面获得更高的全代码分数,在初级诊断,药物和程序方面获得更高的竞争性结果.
在文本生成方面表现优于其他眼科调整的基线.

结论:

适应领域的LLM可以与眼科等专业领域的临床基础输出相匹配或超过一般的LLM.
奥菲斯-V2-Tx展示了紧的,可以在本地部署的模型的可行性.
可审计的评估管道确保了可重复性,并支持未来的基准测试.

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