AI in communication sciences and disorders (Zhang et al., 2024)

<b>Purpose: </b>As artificial intelligence (AI) takes an increasingly prominent role in health care, a growing body of research is being dedicated to its application in the investigation of communication sciences and disorders (CSD). This study aims to provide a comprehensive overview, serving as a valuable resource for researchers, developers, and professionals seeking to comprehend the evolving landscape of AI in CSD research.<b>Method: </b>We conducted a bibliometric analysis of AI-based research in the discipline of CSD published up to December 2023. Utilizing the Web of Science and Scopus databases, we identified 15,035 publications, with 4,375 meeting our inclusion criteria. Based on the bibliometric data, we examined publication trends and patterns, characteristics of research activities, and research hotspot tendencies.<b>Results: </b>From 1985 onwards, there has been a consistent annual increase in publications, averaging 16.51%, notably surging from 2012 to 2023. The primary communication disorders studied include autism, aphasia, dysarthria, Parkinson’s disease, and Alzheimer’s disease. Noteworthy AI models instantiated in CSD research encompass support vector machine, convolutional neural network, and hidden Markov model, among others.<b>Conclusions: </b>Compared to AI applications in other fields, the adoption of AI in CSD has lagged slightly behind. While CSD studies primarily use classical machine learning techniques, there is a growing trend toward the integration of deep learning methods. AI technology offers significant benefits for both research and clinical practice in CSD, but it also presents certain challenges. Moving forward, collaboration among technological, research, and clinical domains is essential to empower researchers and speech-language pathologists to effectively leverage AI technology for the study, diagnosis, assessment, and rehabilitation of CSD.<b>Supplemental Material S1. </b>Search query text.<b>Supplemental Material S2. </b>Key word search in WoS.<b>Supplemental Material S3. </b>Key word search in Scopus.Zhang, M., Tang, E., Ding, H., &amp; Zhang, Y. (2024). Artificial intelligence and the future of communication sciences and disorders: A bibliometric and visualization analysis. <i>Journal of Speech, Language, and Hearing Research</i><i>, 67</i>(11), 4369–4390. https://doi.org/10.1044/2024_JSLHR-24-00157

<b>研究目的：</b>随着人工智能（Artificial Intelligence, AI）在医疗领域的地位日益凸显，越来越多的研究致力于探索其在沟通科学与障碍（Communication Sciences and Disorders, CSD）领域的应用。本研究旨在开展全面综述，为旨在了解AI在CSD研究领域发展动态的研究人员、开发者及相关专业人士提供一份极具价值的参考资料。
<b>研究方法：</b>本研究针对截至2023年12月发表的CSD领域AI相关研究开展文献计量分析。我们依托Web of Science与Scopus两大数据库，共检索到15035篇文献，其中4375篇符合纳入标准。基于文献计量数据，我们对文献发表趋势与规律、研究活动特征以及研究热点走向展开了分析。
<b>研究结果：</b>1985年以来，相关文献发表量持续逐年增长，年均增长率达16.51%，其中2012年至2023年的增长尤为显著。本研究涉及的主要沟通障碍包括自闭症、失语症、构音障碍、帕金森病以及阿尔茨海默病。CSD研究中应用的代表性AI模型包括支持向量机（support vector machine）、卷积神经网络（convolutional neural network）与隐马尔可夫模型（hidden Markov model）等。
<b>研究结论：</b>相较于其他领域的AI应用，CSD领域的AI应用普及稍显滞后。尽管CSD研究目前仍以经典机器学习技术为主，但深度学习方法的整合应用已呈现日益增长的趋势。AI技术可为CSD领域的研究与临床实践带来显著益处，但同时也面临诸多挑战。未来，技术、研究与临床三大领域需加强协作，助力研究人员与言语语言病理学家有效利用AI技术，开展CSD的研究、诊断、评估与康复工作。
<b>补充材料S1：</b>检索查询文本。
<b>补充材料S2：</b>WoS关键词检索。
<b>补充材料S3：</b>Scopus关键词检索。
张M，唐E，丁H，张Y. (2024). 人工智能与沟通科学与障碍的未来：一项文献计量与可视化分析. 《言语、语言与听力研究杂志》, 67(11), 4369–4390. https://doi.org/10.1044/2024_JSLHR-24-00157