内心电图解读的大型语言模型:对于心脏起器患者的心脏诊断的新前沿
Serdar Bozyel1, Ahmet Berk Duman1, Şadiye Nur Dalgıç1
1Department of Cardiology, Health Sciences University, Kocaeli City Hospital, Kocaeli, Türkiye.
Anatolian journal of cardiology
|July 10, 2025
概括
像ChatGPT-4o这样的人工智能在解释心内电图 (EGM) 方面表现出前景,其准确性随着临床背景的改善而提高. 然而,复杂的病例仍然是一个挑战,需要进一步验证临床使用.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
背景情况:
- 心内电图 (EGM) 解释需要专门的专业知识,而心脏病学家往往缺乏这些专业知识.
- 先进的AI模型,如ChatGPT-4o,为提高EGM分析的诊断准确性提供了一个潜在的途径.
- 这项研究系统地评估了ChatGPT-4o在不同级别的上下文信息下解释EGM的能力.
研究的目的:
- 评估ChatGPT-4o在解释心内电图 (EGM) 的诊断性能.
- 评估越来越多的临床背景对ChatGPT-4o的EGM解释准确性和可靠性的影响.
- 为了比较ChatGPT-4o在不同复杂度的不同场景中的性能.
主要方法:
- 使用ChatGPT-4o分析了20个心内电图 (EGM) 病例.
- 在四个场景 (A-D) 中评估了绩效,并增加了上下文数据.
- 统计分析包括McNemar的测试,科恩的卡帕,和流行和偏差调整的卡帕 (PABAK) 超过两个月.
主要成果:
- 通过增加临床背景,ChatGPT-4o的准确性从57% (情景A) 提高到66% (情景B).
- 在情景A中,对于心房活动和同步,一致性很高,但对于腔室识别却很差.
- 复杂情景 (C和D) 的表现较低,随着时间的推移相当一致,表明复杂决策的局限性.
结论:
- 聊天GPT-4o在结构化的EGM解释任务中表现出有希望的准确性和可靠性.
- 人工智能模型有效地整合了上下文信息,但对复杂的临床病例的适应能力有限.
- 在广泛采用EGM解释AI临床应用之前,需要进一步的研究和验证.
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