使用生成人工智能对医疗保健中的语言进行客观评估
James O'Sullivan1, Pilar Garces1, Eduardo A Aponte1
1Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland.
Scientific reports
|December 3, 2025
概括
使用GPT-4的人工智能 (AI) 为评估自闭症表达性沟通提供了一个客观,可靠和高效的方法. 这种人工智能模型显示了与既定尺度的强烈相关性,超过了传统的语言特征.
科学领域:
- 神经科学是一个神经科学.
- 人工智能的人工智能
- 临床心理学 临床心理学
背景情况:
- 在神经和精神疾病中的传统语言评估是主观的,资源密集的.
- 当前方法的局限性会影响临床试验中的可靠性,敏感性和跟踪治疗疗效.
- 对不同患者群体来说,客观语言评估至关重要.
研究的目的:
- 引入使用GPT-4的客观语言评估方法.
- 评估GPT-4能够预测Vineland适应性行为量表 (VABS-II) 自闭症患者表达性沟通得分的能力.
- 将GPT-4的性能与基准语言特征进行比较.
主要方法:
- 利用GPT-4来分析观察性临床试验中自闭症参与者的转录对话.
- 使用人工智能模型预测VABS-II表达性沟通得分.
- 将GPT-4预测与传统语言特征 (例如句子长度) 进行比较.
主要成果:
- GPT-4预测与实际的VABS-II得分有很强的相关性 (皮尔森的r>0.65).
- 人工智能模型显示了高的测试-重新测试可靠性 (ICC(2,1) = 0.97).
- 在GPT-4的分数中,比基准语言特征的分数差异要大得多.
结论:
- GPT-4提供了一个整体的,可靠的,客观的,和时间有效的表达式沟通的评估.
- 生成型人工智能模型可以提高临床试验中沟通能力的评估.
- 人工智能提供了一个可扩展的工具,用于评估临床实践中的患者和治疗疗效.
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