Boston Children's Hospital, Waltham, MA 02453, USA.
Massachusetts General Hospital Institute of Health Professionals, Boston, MA 02129, USA.
Int J Environ Res Public Health. 2024 Aug 29;21(9):1150. doi: 10.3390/ijerph21091150.
As artificial intelligence (AI) makes significant headway in various arenas, the field of speech-language pathology is at the precipice of experiencing a transformative shift towards automation. This study introduces , an AI-driven application designed to generate topic-specific displays from photographs in a "just-in-time" manner. Using , this study aimed to (a) determine which of two AI algorithms (NLG-AAC and GPT-3.5) results in greater specificity of vocabulary (i.e., percentage of vocabulary kept/deleted by clinician relative to vocabulary generated by ; percentage of vocabulary modified); and to (b) evaluate perceived usability of among practicing speech-language pathologists. Results revealed that the GPT-3.5 algorithm consistently resulted in greater specificity of vocabulary and that speech-language pathologists expressed high user satisfaction for the application. These results support continued study of the implementation of in clinical practice and demonstrate the possibility of utilizing topic-specific displays as just-in-time supports.
随着人工智能(AI)在各个领域取得重大进展,言语病理学领域正处于向自动化转型的风口浪尖。本研究介绍了一种 AI 驱动的应用程序,旨在以“即时”的方式从照片中生成特定主题的显示。使用该研究旨在:(a)确定两种 AI 算法(NLG-AAC 和 GPT-3.5)中哪一种可以生成更具体的词汇(即,相对于由生成的词汇,临床医生保留/删除的词汇百分比;修改的词汇百分比);以及(b)评估在实践中的言语语言病理学家对 的感知可用性。结果表明,GPT-3.5 算法始终可以生成更具体的词汇,并且言语语言病理学家对 应用程序表示高度的用户满意度。这些结果支持在临床实践中继续研究 的实施,并展示了使用特定主题显示作为即时支持的可能性。