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Artificial Intelligence and Communication Sciences and Disorders: A Bidirectional Frontier

Research output: Contribution to journal › Review article › peer-review

Abstract

Abstract – This paper explores the bidirectional interplay between artificial intelligence (AI) and communication sciences and disorders (CSDs). It begins by defining essential terminology and foundational concepts, followed by a discussion of key ethical considerations in the development and deployment of AI technologies. Current AI research in CSD is then reviewed, with particular attention to multimodal approaches that integrate linguistic and nonverbal behavioral cues, such as motion. The paper further illustrates how insights from CSD – and the clinical expertise of speech-language pathologists – can inform the design of AI systems, making them more inclusive, interpretable, and responsive to the diversity of communication disorders. Extending this discussion, we introduce the concept of a CSD digital twin: a computational framework capable of analyzing and simulating communication processes in real time to support precision diagnostics, personalized intervention, scientific discovery, and public health planning. Finally, we highlight the importance of cultivating AI literacy – particularly statistical thinking – as a core professional competency for both researchers and clinicians in the AI era. Ultimately, this work envisions a shared framework for advancing research and clinical practice, fostering a data-driven foundation for innovation, discovery, and evidence-based progress in the field of CSDs.

Original languageEnglish
Pages (from-to)1-12
Number of pages12
JournalActa Cytologica
DOIs
StateAccepted/In press - 2026

Bibliographical note

Publisher Copyright:
© 2026 S. Karger AG, Basel

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 4 - Quality Education
    SDG 4 Quality Education

Keywords

  • Artificial intelligence
  • Communication sciences and disorders
  • Data science
  • Digital twins
  • Machine learning
  • Multimodal data
  • Speech-language pathology
  • Statistics

ASJC Scopus subject areas

  • Pathology and Forensic Medicine
  • Histology

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