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) 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.