⚡ Quick Summary
This study provides a comprehensive bibliometric analysis of artificial intelligence (AI) applications in communication sciences and disorders (CSD), revealing a consistent annual increase in publications and highlighting key AI models used in the field. The findings emphasize the potential of AI to enhance research and clinical practices in CSD, while also identifying challenges that need to be addressed.
🔍 Key Details
- 📊 Dataset: 15,035 publications, 4,375 meeting inclusion criteria
- 🗓️ Timeframe: Publications analyzed from 1985 to December 2023
- 🔍 Focus Areas: Autism, aphasia, dysarthria, Parkinson’s disease, Alzheimer’s disease
- ⚙️ AI Models: Support vector machine, convolutional neural network, hidden Markov model
🔑 Key Takeaways
- 📈 Growth Trend: Annual publication increase of 16.51% since 2012.
- 🤖 AI Adoption: CSD has lagged slightly behind other fields in AI integration.
- 💡 Machine Learning: Predominantly classical techniques, with a shift towards deep learning.
- 🏥 Clinical Benefits: AI offers significant advantages for diagnosis and rehabilitation in CSD.
- 🔗 Collaboration Needed: Essential between technological, research, and clinical domains.
📚 Background
The integration of artificial intelligence in healthcare is rapidly evolving, with a notable impact on various disciplines, including communication sciences and disorders. As researchers and clinicians seek innovative solutions to enhance patient care, understanding the landscape of AI applications in CSD becomes crucial for effective implementation and advancement.
🗒️ Study
This bibliometric analysis aimed to provide a detailed overview of AI-based research in CSD, utilizing data from the Web of Science and Scopus databases. By examining publication trends, research activities, and hotspots, the study serves as a valuable resource for professionals in the field.
📈 Results
The analysis revealed a consistent increase in publications related to AI in CSD, with a significant surge noted from 2012 to 2023. The primary disorders investigated include autism, aphasia, dysarthria, Parkinson’s disease, and Alzheimer’s disease. Key AI models identified in the research include support vector machines, convolutional neural networks, and hidden Markov models.
🌍 Impact and Implications
The findings of this study highlight the transformative potential of AI in enhancing both research and clinical practices within CSD. By addressing existing challenges and fostering collaboration among researchers, developers, and clinicians, the field can leverage AI technologies to improve the study, diagnosis, assessment, and rehabilitation of communication disorders.
🔮 Conclusion
This bibliometric analysis underscores the growing importance of AI in communication sciences and disorders. As the field continues to evolve, embracing AI technologies can lead to improved patient outcomes and more effective treatment strategies. Continued research and collaboration will be essential to fully realize the benefits of AI in CSD.
💬 Your comments
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Artificial Intelligence and the Future of Communication Sciences and Disorders: A Bibliometric and Visualization Analysis.
Abstract
PURPOSE: 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.
METHOD: 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.
RESULTS: 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.
CONCLUSIONS: 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.
SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.27162564.
Author: [‘Zhang M’, ‘Tang E’, ‘Ding H’, ‘Zhang Y’]
Journal: J Speech Lang Hear Res
Citation: Zhang M, et al. Artificial Intelligence and the Future of Communication Sciences and Disorders: A Bibliometric and Visualization Analysis. Artificial Intelligence and the Future of Communication Sciences and Disorders: A Bibliometric and Visualization Analysis. 2024; (unknown volume):1-22. doi: 10.1044/2024_JSLHR-24-00157