Artificial Intelligence in Speech and Language Therapy: A Qualitative Comparative Analysis of Clinical Applications and Outcomes.
Large language models (LLMs) such as ChatGPT are entering clinical practice, yet how their clinical reasoning compares with speech-language therapists (SLTs) is not well understood. This comparative multi-case qualitative study used 3 hypothetical vignettes. Ten experienced SLTs (≥10 years) particip...
| Published in: | Inquiry (00469580) Vol. 63; pp. 1 - 17 |
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| Main Authors: | , , , , , , |
| Format: | pictorial research tables/charts Journal Article |
| Published: |
Sage Publications Inc.
4/24/2026
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=193250516&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 193250516 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00469580 INQ jtl: Inquiry (00469580) issn: 00469580 maglogo: Y pubinfo: dt: 4/24/2026 vid: 63 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 193250516 193250516 193250516 10.1177/00469580261445316 193250516 ppf: 1 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Artificial Intelligence in Speech and Language Therapy: A Qualitative Comparative Analysis of Clinical Applications and Outcomes. aug: au: Yaşa, İbrahim Can Dölek, Muhsin Eyilikeder Tekin, Seda Kaya, Ayşe Serra Akgün, Pınar Yılmaz, Sakine Deniz Tokalak, Selin affil: Bahçeşehir University, Beşiktaş, Istanbul, Türkiye sug: subj: Speech-Language Pathologists Psychosocial Factors Speech-Language Pathology Natural Language Processing Utilization Clinical Competence Evaluation Clinical Reasoning Evaluation Outcomes (Health Care) Evaluation Human Male Female Qualitative Studies Comparative Studies Purposive Sample Snowball Sample Turkiye Semi-Structured Interview Videorecording Artificial Intelligence, Generative Role Playing Content Analysis Thematic Analysis Clinical Assessment Tools Scales Descriptive Statistics Data Analysis Software Male Female ab: Large language models (LLMs) such as ChatGPT are entering clinical practice, yet how their clinical reasoning compares with speech-language therapists (SLTs) is not well understood. This comparative multi-case qualitative study used 3 hypothetical vignettes. Ten experienced SLTs (≥10 years) participated in semi-structured interviews, providing assessment, diagnosis and therapy plans for each vignette. ChatGPT-4o was presented with identical, standardized Turkish prompts over five consecutive days to evaluate the model's temporal consistency in clinical reasoning. All outputs were analyzed with content analysis, and day-to-day consistency of ChatGPT themes was examined. ChatGPT-4o and SLTs showed substantial overlap in core practices such as case history, spontaneous speech analysis, key diagnostic labels, and emphasis on generalization and caregiver involvement. However, SLTs utilized broader, locally normed assessment tools and offered more flexible, individualized and context-sensitive therapy approaches. ChatGPT-4o's responses were more standardized and showed stable thematic patterns across days, yet they did not reflect the clinical nuance or contextual adaptation observed in SLTs' reasoning. ChatGPT-4o can approximate expert-like reasoning in structured scenarios and may serve as a clinical decision support aid. Nonetheless, it does not replace experienced SLTs, particularly for culturally grounded, person-specific assessment and intervention planning. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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