大型语言模型的临床应用,用于语音语言病理学的干预计划开发
Namhee Kim1, Mercy Homer1, Hyeju Jang2
1California Baptist University, Riverside.
American journal of speech-language pathology
|May 22, 2025
概括
人工智能 (AI) 工具显示出语音语言病理学干预计划的潜力,但目前的输出范围从需要改进到满足预期. 详细的提示提高了人工智能生成的干预计划质量.
科学领域:
- 语音语言病理学 语言病理学
- 医疗保健中的人工智能
- 临床决策支持系统临床决策支持系统
背景情况:
- 大型语言模型 (LLM) 越来越多地被整合到临床写作工具中.
- 对AI产生的内容在临床应用中的有效性进行评估至关重要.
研究的目的:
- 评估六种人工智能工具在生成语音和语言干预计划中的性能.
- 确定人工智能在语音语言病理学干预计划中的应用和局限性.
主要方法:
- 采用了混合方法方法,将定量和定性分析结合起来.
- 通过使用三个虚构的儿科语音和语言障碍病例,评估了6个AI工具.
- 用了两种具有不同特异性的提示类型来生成AI输出.
主要成果:
- 在临床能力方面,人工智能生成的干预计划从"改善需求"到"符合预期"等级.
- 与一般提示相比,高度具体和结构化的提示导致了更好的AI输出评级.
- 人工智能工具在支持干预计划开发方面表现出明显的优缺点.
结论:
- 这些发现为在语音语言病理学中负责任地利用人工智能提供了基础数据.
- 临床医生,教育工作者和学生可以利用这些见解将AI整合到干预计划中.
- 需要进一步的研究来优化人工智能工具的性能和临床集成.
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