超越文本:临床背景对GPT-4的12导电心电图解读精度的影响
Ivan Zeljkovic1, Andrej Novak2, Ante Lisicic3
1Dubrava University Hospital, Zagreb, Croatia; Catholic University of Croatia, Zagreb, Croatia. Electronic address: https://twitter.com/i_zeljkovic.
像GPT-4这样的大型语言模型显示出解释心电图 (ECG) 的潜力,但当提供临床背景时,准确性显著提高. 单靠GPT-4可能还不能提供可靠的心电图解释.
科学领域:
- 人工智能在医学中的应用
- 心脏病学 诊断 心脏病学 诊断
- 医学数据解释 医学数据解释
背景情况:
- 人工智能 (AI) 和大型语言模型 (LLM) 显示出医疗应用的前景.
- 目前正在探索GPT-4的图像处理能力,用于医学数据的解释.
- 在解释12导电心电图 (ECG) 时,GPT-4的有效性需要评估.
研究的目的:
- 评估GPT-4在解释12导电心电图中的准确性.
- 为了确定临床背景对GPT-4ECG解释性能的影响.
- 评估GPT-4作为心脏病学诊断辅助的潜力.
主要方法:
- GPT-4解释了来自CaRD注册表的150个12心电图.
- 电心电图被分为心律不整,导电异常,急性冠状动脉综合征等.
- 在临床上下文和没有临床上下文进行解释,并由心脏病学家评估准确性.
主要成果:
- 在没有上下文的情况下,GPT-4的准确性达到19%,在有上下文的情况下提高到45% (P < 0.001).
- 临床背景显著提高了急性冠状动脉综合征的准确性 (10%对70%;P<0.01).
- 对心律失常和导电异常的准确性显示,随着环境的变化,情况有所改善.
结论:
- GPT-4的12导电图解读精度高度依赖于临床背景.
- 虽然显示出潜力,但单独的GPT-4目前可能无法提供足够准确的ECG解释.
- 需要进一步开发,以提高复杂心脏诊断中的LLM性能.
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