在膀癌管理中增强人工智能:对多种大型语言模型进行比较分析和优化研究
Kun-Peng Li1,2, Li Wang1,2, Shun Wan1,2
1Department of Urology, The Second Hospital of Lanzhou University, Lanzhou, China.
Journal of endourology
|March 18, 2025
概括
大型语言模型 (LLM) 在医疗保健方面表现有前途,但与专业瘤学相斗争. 战略优化显著提高了GPT-3.5-Turbo对于膀癌 (BLCA) 临床问题的准确性,达到100%.
科学领域:
- 人工智能在医学中的应用
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
背景情况:
- 大型语言模型 (LLM) 在医疗保健中越来越多地使用,但它们在瘤学等专业领域的有效性是有限的.
- 在解决复杂的临床调查时,领先的LLM之间存在性能差异.
研究的目的:
- 评估多个领先的LLM在回答与膀癌 (BLCA) 相关的临床问题的表现.
- 证明战略优化对提高专业瘤学应用的LLM准确性的影响.
主要方法:
- 根据指导方针制定了一套100个临床问题,涵盖BLCA的流行病学,诊断,治疗,预后和随访.
- 在三个试验中测试了六个LLM (克劳德-3.5-索内特,ChatGPT-4.0,Grok-beta,Gemini-1.5-Pro,Mistral-Large-2,GPT-3.5-Turbo) 在三个试验中进行了测试.
- GPT-3.5-Turbo经历了两阶段的训练优化过程.
主要成果:
- 克劳德3.5-索内特获得了最高的初始精度 (89.33%),而GPT-3.5-Turbo的初始精度最低 (74.33%).
- 经过两个优化阶段,GPT-3.5-Turbo的精度提高到86.67%,随后达到100%.
- 对比分析显示,在测试的LLM中,表现有显著差异.
结论:
- 在膀癌等专门的瘤学领域,LLM的表现各不相同.
- 有针对性的培训优化可以大大提高临床决策支持的LLM准确性.
- 将GPT-3.5-Turbo成功改进到100%的准确度突出了战略模型改进的潜力.
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