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生成预训练型变压器-4,一种人工智能文本预测模型,具有通过新型书面放射学考试问题的高能力
Avnish Sood1, Nina Mansoor2, Caroline Memmi3
1King's College London, Strand, London, WC2R 2LS, UK.
International journal of computer assisted radiology and surgery
|February 21, 2024
概括
像GPT-4这样的大型语言模型 (LLM) 在放射学认证考试中表现出色,明显超过GPT-3. 这表明AI在临床放射学决策支持中的潜力.
科学领域:
- 放射学中的人工智能
- 医疗教育 技术 技术 医学教育
- 自然语言处理自然语言处理.
背景情况:
- 卷积神经网络 (CNN) 在放射学AI图像解释方面表现有前途.
- 现有的人工智能模型难以整合临床环境,限制了它们在现实世界的应用.
- 大型语言模型 (LLM) 为结合临床信息提供了一种新的方法.
研究的目的:
- 评估LLM,特别是生成预训练变压器 (GPT) 版本3和4的能力,以回答放射学专业考试问题.
- 评估LLM是否能够在问答框架内有效处理和利用相关的临床信息.
主要方法:
- 从皇家放射学学院 (FRCR 2A) 和美国放射学委员会 (ABR) 考试中编制了一个全面的问题数据库.
- 问题包括单个最佳答案 (SBA) 和真假格式.
- 提示GPT-3和GPT-4回答精选的放射学考试问题.
主要成果:
- 在1072个评估项目中,GPT-4在SBA上获得了70.7%的准确率,在真假问题上获得了83.0%的准确率.
- GPT-4显著优于GPT-3,在SBA问题上准确率为31.7%,在真假问题上准确率为61.0%.
- 在FRCR 2A和ABR考试问题上,GPT-4的表现一致,在作者撰写的问题上没有退化.
结论:
- 最新一代的LLM,GPT-4,在放射学考试问题回答方面表现出实质性的熟练程度.
- 与GPT-3相比,GPT-4是一个显著的进步,表明未来精度改进的潜力.
- 需要进一步的研究来探索这些先进的人工智能模型的实际临床实用性.
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