聊天GPT与双子座:从放射学报告中 Lung-RADS 评分分配的比较准确性和效率
Ria Singh1, Mohamed Hamouda2, Jordan H Chamberlin2
1Osteopathic Medical School, Kansas City University, Kansas, MO, USA.
在测试的大型语言模型中,ChatGPT-4o在从肺癌查CT报告中分配肺-RADS得分方面表现出最高的准确性. 虽然有希望,但LLM需要进一步开发用于临床使用.
科学领域:
- 放射学中的人工智能
- 医学成像分析 医学成像分析
- 肺癌查 肺癌查
背景情况:
- 肺-RADS (肺成像报告和数据系统) 对于标准化肺癌查至关重要.
- 精确的肺RADS评分依赖于解释低剂量计算机断层扫描 (LDCT) 报告.
- 大型语言模型 (LLM) 显示了自动化医疗报告分析的潜力.
研究的目的:
- 评估各种LLM在生成肺-RADS得分方面的准确性.
- 为了比较ChatGPT-3.5,ChatGPT-4o,谷歌双子和双子高级的性能.
- 评估LLM在处理LDCT放射学报告中的效率.
主要方法:
- 242个LDCT放射学报告的回顾性分析.
- 在没有微调的情况下,LLM根据报告的发现分配了肺-RADS分数.
- 精度与放射科医生分配的得分相比较;测量响应时间.
主要成果:
- 在肺-RADS评分中,ChatGPT-4o获得了最高的准确度 (83.6%).
- 聊天GPT-3.5 (70.1%),双子座 (70.9%) 和双子座高级 (65.1%) 的准确性较低.
- 聊天GPT-4o与放射科医生达成最大的协议 (κ = 0.836),但低于人类间观察者水平.
结论:
- 聊天GPT-4o在肺-RADS得分准确度方面超过了其他LLM,但与人类专家的表现不匹配.
- LLM显示了肺癌查报告分析的潜力.
- 对特定领域的培训是必要的,以提高临床决策的LLM可靠性.
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