评估ChatGPT-4在FRCR第1部分检查问题中识别放射解剖学的性能
Pradosh Kumar Sarangi1, Suvrankar Datta2, Braja Behari Panda3
1Department of Radiodiagnosis, All India Institute of Medical Sciences, Deoghar, Jharkhand, India.
The Indian journal of radiology & imaging
|April 29, 2025
概括
聊天GPT-4在回答放射解剖问题的准确性有限,特别是在结构识别和侧面性方面,尽管正确识别了成像模式. 对于临床应用,需要进一步的AI培训.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 医学教育 医学教育
背景情况:
- 精确的放射图像解释对于患者护理至关重要.
- 人工智能 (AI) 和自然语言处理 (NLP) 显示出支持放射科医生的潜力.
- 对放射学人工智能性能的研究仍在发展.
研究的目的:
- 评估ChatGPT-4在回答放射解剖问题的有效性.
- 评估类似于皇家放射科医师学院 (FRCR) 奖学金第1部分解剖学考试的问题上的表现.
主要方法:
- 使用了100个模拟FRCR第1部分解剖学问题.
- 聊天GPT-4被测试了和没有检查的背景.
- 对答案的正确性,侧面性,模式识别和近似性进行了评分.
主要成果:
- 聊天GPT-4的正确度得分很低 (4-7.5%),但在识别成像模式方面达到100%的准确性.
- 该模型面临着边缘性 (40-42%) 的困难,并显示出有限的响应一致性 (32%).
- 估计得分超过了50%,表明识别了没有直接查询的结构.
结论:
- 聊天GPT-4在解释正常放射解剖学方面表现出显著的局限性.
- 增强的人工智能培训对于准确的解剖学解释是必要的.
- 识别解剖结构的正确侧面仍然是当前AI模型面临的挑战.
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