临床演讲的负责任发展AI:弥合临床研究和技术之间的差距
1School of Electrical Computer and Energy Engineering and College of Health Solutions, Arizona State University, Tempe, AZ, USA. visar@asu.edu.
NPJ digital medicine
|August 9, 2024
概括
整合语音科学和临床研究可以改善语音生物标志物的人工智能 (AI) 模型,即使是小数据集. 这种方法提高了模型的通用性,并减少了临床应用的翻译时间表.
科学领域:
- 语音科学是一种语言科学.
- 临床研究是临床研究.
- 人工智能 (AI) 是一种人工智能.
背景情况:
- 临床语音AI模型经常使用具有有限样本大小的高维特征,导致过度拟合和糟糕的概括.
- 由于语音科学和临床见解的整合不足,现有的模型可能缺乏可解释性和稳定性.
- 小规模的临床数据集对开发可靠的语音生物标志物应用提出了重大挑战.
研究的目的:
- 探索语音作为临床环境中的生物标记物的潜力,解决小数据集带来的挑战.
- 提出通过其语音影响来组织健康状况的框架,并促进语音分析的各种临床应用.
- 为高风险的临床人工智能部署倡导可解释,个别验证的措施和严格的验证框架.
主要方法:
- 整合语音科学和临床研究的见解,以减少人工智能模型中的样本复杂性.
- 根据其对言语的影响,制定组织健康状况的框架.
- 促进使用语音分析超越横截面分类.
主要成果:
- 整合领域知识可以减少样本的复杂性,并提高临床语音AI模型的概括性.
- 一种结构化的方法可以促进语音分析在各种临床环境中的应用.
- 专注于可解释和验证的措施对于负责任的临床AI部署至关重要.
结论:
- 将人工智能研究与临床语音研究相结合,为基于语音的AI工具提供了有效翻译的机会.
- 利用语音科学和临床专业知识可以推进科学发现,特别是在有限的数据集.
- 负责任的部署需要严格的验证,道德考虑,并专注于可解释的AI措施.
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