研究人工智能在预测感知发声障碍程度中的作用。
Investigating the role of artificial intelligence in predicting perceived dysphonia level.
机构信息
Independent Researcher in Laryngology, Voice Pathology, and Speech-Language Pathology, Tehran, Iran.
Department of Speech Therapy, School of Rehabilitation, Tehran University of Medical Sciences, Enghelab Avenue, Pitch-e-Shemiran, Tehran, 11489, Iran.
出版信息
Eur Arch Otorhinolaryngol. 2024 Nov;281(11):6093-6097. doi: 10.1007/s00405-024-08868-7. Epub 2024 Aug 22.
PURPOSE
This study aims to investigate the role of one of these models in the field of voice pathology and compare its performance in distinguishing the perceived dysphonia level.
METHODS
Demographic information, voice self-assessments, and acoustic measurements related to a sample of 50 adult dysphonic outpatients were presented to ChatGPT and Perplexity AI chatbots, which were interrogated for the perceived dysphonia level.
RESULTS
The agreement between the auditory-perceptual assessment by experts and ChatGPT and Perplexity AI chatbots, as determined by Cohen's Kappa, was not statistically significant (p = 0.429). There was also a low positive correlation (r = 0.30, p = 0.03) between the diagnosis made by ChatGPT and Perplexity AI chatbots (r = 0.30, p = 0.03).
CONCLUSION
It seems that AI could not play a vital role in helping the voice care teams determine the perceptual level of dysphonia.
目的
本研究旨在探讨这些模型在语音病理学领域中的作用，并比较它们在区分感知性嗓音障碍程度方面的表现。
方法
向 ChatGPT 和 Perplexity AI 聊天机器人呈现了 50 名成年嗓音障碍门诊患者的人口统计学信息、嗓音自我评估和声学测量结果，以询问其感知性嗓音障碍程度。
结果
通过 Cohen's Kappa 检验，专家的听觉感知评估与 ChatGPT 和 Perplexity AI 聊天机器人之间的一致性没有统计学意义（p=0.429）。ChatGPT 和 Perplexity AI 聊天机器人的诊断之间也存在低度正相关（r=0.30，p=0.03）。
结论
人工智能似乎不能在帮助嗓音保健团队确定感知性嗓音障碍程度方面发挥重要作用。
Zotero 插件