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QuickPic AAC : An AI-Based Application to Enable Just-in-Time Generation of Topic-Specific Displays for Persons Who Are Minimally Speaking

Author

Listed:
  • Christina Yu

    (Boston Children’s Hospital, Waltham, MA 02453, USA
    Massachusetts General Hospital Institute of Health Professionals, Boston, MA 02129, USA)

  • Ralf W. Schlosser

    (Boston Children’s Hospital, Waltham, MA 02453, USA
    Department of Communication Sciences and Disorders, Northeastern University, Boston, MA 02115, USA)

  • Maurício Fontana de Vargas

    (School of Information Studies, McGill University, Montreal, QC H3A 0G4, Canada)

  • Leigh Anne White

    (Boston Children’s Hospital, Waltham, MA 02453, USA)

  • Rajinder Koul

    (Department of Speech, Language, and Hearing Sciences, University of Texas at Austin, Austin, TX 78712, USA)

  • Howard C. Shane

    (Boston Children’s Hospital, Waltham, MA 02453, USA
    Massachusetts General Hospital Institute of Health Professionals, Boston, MA 02129, USA)

Abstract

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 QuickPic AAC , an AI-driven application designed to generate topic-specific displays from photographs in a “just-in-time” manner. Using QuickPic AAC , 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 QuickPic AAC ; percentage of vocabulary modified); and to (b) evaluate perceived usability of QuickPic AAC 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 QuickPic AAC application. These results support continued study of the implementation of QuickPic AAC in clinical practice and demonstrate the possibility of utilizing topic-specific displays as just-in-time supports.

Suggested Citation

  • Christina Yu & Ralf W. Schlosser & Maurício Fontana de Vargas & Leigh Anne White & Rajinder Koul & Howard C. Shane, 2024. "QuickPic AAC : An AI-Based Application to Enable Just-in-Time Generation of Topic-Specific Displays for Persons Who Are Minimally Speaking," IJERPH, MDPI, vol. 21(9), pages 1-26, August.
    • Handle: RePEc:gam:jijerp:v:21:y:2024:i:9:p:1150-:d:1467290
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