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Equipping Speech-Language Clinicians for the Critical Appraisal of an Artificial Intelligence--Driven, Evidence-Based Future.

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Title: Equipping Speech-Language Clinicians for the Critical Appraisal of an Artificial Intelligence--Driven, Evidence-Based Future.
Authors: Benway, Nina R.1 benway@umd.edu, Preston, Jonathan L.2
Source: Language, Speech & Hearing Services in Schools. Jul2025, Vol. 56 Issue 3, p442-468. 27p.
Subject Terms: *Artificial intelligence, *Professional employee training, *Machine learning, *Automation, *Speech therapy, *Algorithms, Automatic speech recognition, Medical protocols, Data security, Digital technology, Medical care use, Professional practice, Organizational ethics, Research evaluation, Conceptual structures, Evidence-based medicine, Sensitivity & specificity (Statistics)
Abstract: Purpose: Artificial intelligence (AI) is more capable and accessible than ever before. But what does this mean for clinical practice? How can speechlanguage clinicians evaluate the efficacy, validity, and reliability of AI and machine learning tools for automating assessment and treatment? How can speech-language clinicians ethically use these clinical AI technologies? We contend that clinical AI will best serve clinicians and clients when aligned with an evidence-based framework. Therefore, this tutorial presents guidelines for the critical appraisal of clinical AI through the lens of validity, reliability, ethical use, and equitable use, facilitated by the Critical Appraisal Rubric for Ethical and Equitable Clinical Artificial Intelligence. Similarly, in order for developers of clinical AI to meet the needs of the profession, these principles should guide the development and assessment of new clinical technologies. Conclusions: The questions of efficacy, validity, reliability, ethical use, and equitable use of clinical AI can be answered through the examination of a specific clinical AI for a given user, as emphasized by culturally responsive professional practice. A framework is provided to assist clinicians in the critical appraisal of clinical AI tools. [ABSTRACT FROM AUTHOR]
Copyright of Language, Speech & Hearing Services in Schools is the property of American Speech-Language-Hearing Association and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: Equipping Speech-Language Clinicians for the Critical Appraisal of an Artificial Intelligence--Driven, Evidence-Based Future.
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  Data: *<searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br />*<searchLink fieldCode="DE" term="%22Professional+employee+training%22">Professional employee training</searchLink><br />*<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Automation%22">Automation</searchLink><br />*<searchLink fieldCode="DE" term="%22Speech+therapy%22">Speech therapy</searchLink><br />*<searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Automatic+speech+recognition%22">Automatic speech recognition</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+protocols%22">Medical protocols</searchLink><br /><searchLink fieldCode="DE" term="%22Data+security%22">Data security</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+technology%22">Digital technology</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+care+use%22">Medical care use</searchLink><br /><searchLink fieldCode="DE" term="%22Professional+practice%22">Professional practice</searchLink><br /><searchLink fieldCode="DE" term="%22Organizational+ethics%22">Organizational ethics</searchLink><br /><searchLink fieldCode="DE" term="%22Research+evaluation%22">Research evaluation</searchLink><br /><searchLink fieldCode="DE" term="%22Conceptual+structures%22">Conceptual structures</searchLink><br /><searchLink fieldCode="DE" term="%22Evidence-based+medicine%22">Evidence-based medicine</searchLink><br /><searchLink fieldCode="DE" term="%22Sensitivity+%26+specificity+%28Statistics%29%22">Sensitivity & specificity (Statistics)</searchLink>
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  Data: Purpose: Artificial intelligence (AI) is more capable and accessible than ever before. But what does this mean for clinical practice? How can speechlanguage clinicians evaluate the efficacy, validity, and reliability of AI and machine learning tools for automating assessment and treatment? How can speech-language clinicians ethically use these clinical AI technologies? We contend that clinical AI will best serve clinicians and clients when aligned with an evidence-based framework. Therefore, this tutorial presents guidelines for the critical appraisal of clinical AI through the lens of validity, reliability, ethical use, and equitable use, facilitated by the Critical Appraisal Rubric for Ethical and Equitable Clinical Artificial Intelligence. Similarly, in order for developers of clinical AI to meet the needs of the profession, these principles should guide the development and assessment of new clinical technologies. Conclusions: The questions of efficacy, validity, reliability, ethical use, and equitable use of clinical AI can be answered through the examination of a specific clinical AI for a given user, as emphasized by culturally responsive professional practice. A framework is provided to assist clinicians in the critical appraisal of clinical AI tools. [ABSTRACT FROM AUTHOR]
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  Group: Ab
  Data: <i>Copyright of Language, Speech & Hearing Services in Schools is the property of American Speech-Language-Hearing Association and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ehh&AN=186696947
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        Value: 10.1044/2025_LSHSS-24-00085
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        Text: English
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        PageCount: 27
        StartPage: 442
    Subjects:
      – SubjectFull: Artificial intelligence
        Type: general
      – SubjectFull: Professional employee training
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Automation
        Type: general
      – SubjectFull: Speech therapy
        Type: general
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Automatic speech recognition
        Type: general
      – SubjectFull: Medical protocols
        Type: general
      – SubjectFull: Data security
        Type: general
      – SubjectFull: Digital technology
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      – SubjectFull: Medical care use
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      – SubjectFull: Professional practice
        Type: general
      – SubjectFull: Organizational ethics
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      – SubjectFull: Research evaluation
        Type: general
      – SubjectFull: Conceptual structures
        Type: general
      – SubjectFull: Evidence-based medicine
        Type: general
      – SubjectFull: Sensitivity & specificity (Statistics)
        Type: general
    Titles:
      – TitleFull: Equipping Speech-Language Clinicians for the Critical Appraisal of an Artificial Intelligence--Driven, Evidence-Based Future.
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            – D: 01
              M: 07
              Text: Jul2025
              Type: published
              Y: 2025
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