在GPT-5中对超参数和输入优化的前性定量分析:对腹部放射学放射科医生的表现的比较贡献
Eren Çamur1, Turay Cesur2, Yasin Celal Güneş3
1Ankara 29 Mayıs State Hospital, Clinic of Radiology, Ankara, Türkiye.
Diagnostic and interventional radiology (Ankara, Turkey)
|February 16, 2026
概括
优化GPT-5的输入格式和超参数显著提高了其在腹部成像中的诊断准确性. 大型语言模型 (LLM) 的帮助也改善了放射科医生的诊断性能.
科学领域:
- 医疗成像中的人工智能
- 在放射学中使用自然语言处理.
背景情况:
- 像GPT-5这样的大型语言模型 (LLM) 显示出临床应用的潜力.
- 优化LLM性能需要仔细考虑输入数据和模型参数.
研究的目的:
- 评估输入格式和超参数设置对GPT-5在腹部病例分析中的性能的影响.
- 评估GPT-5辅助对放射科医生的诊断准确性和差异诊断能力的贡献.
主要方法:
- 在基于浏览器和API访问的环境中使用GPT-5对86例腹部病例的前性评估.
- 用"仅视觉"和"视觉与成像发现和临床呈现"输入格式评估GPT-5的性能.
- 使用可变温度和top-p设置优化GPT-5,并评估其对初级放射科医生的帮助.
主要成果:
- 随着额外的临床信息 (P < 0.001),GPT-5的诊断准确度从12% (仅视觉) 增加到58% (P < 0.001).
- 超参数优化 (温度: 1.5,顶部-p: 1) 进一步提高了GPT-5的诊断准确度,达到73%.
- 放射科医生的诊断准确度从没有辅助的71-73%提高到有GPT-5辅助的86-94%.
结论:
- 结构化的文本输入和基于API的超参数优化提高了GPT-5在腹部成像中的性能.
- GPT-5辅助显著改善了初级放射科医生的诊断和差异诊断性能.
- 标准化超参数设置对于未来基于LLM的临床决策支持工具至关重要.
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