ArticleLaryngoscope investigative otolaryngology2024
TrachGPT: Appraisal of tracheostomy care recommendations from an artificial intelligent Chatbot.
Article in Laryngoscope investigative otolaryngology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Applications of Natural Language Processing in Otolaryngology: A Scoping Review.The Laryngoscope · 2025Article
- Tracheostomy in the Digital Age: How Artificial Intelligence and Immersive Technologies Are Redefining Airway Care.Tracheostomy (Warrenville, Ill.) · 2025Article
- TrachGPT: Appraisal of tracheostomy care recommendations from an artificial intelligent Chatbot.Laryngoscope investigative otolaryngology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Safe home tracheostomy care requires engagement and troubleshooting by patients, who may turn to online, AI-generated information sources. This study assessed the quality of ChatGPT responses to such queries. Methods: In this cross-sectional study, ChatGPT was prompted with 10 hypothetical tracheostomy care questions in three domains (complication management, self-care advice, and lifestyle adjustment). Responses were graded by four otolaryngologists for appropriateness, accuracy, and overall score. The readability of responses was evaluated using the Flesch Reading Ease (FRE) and Flesch-Kincaid Reading Grade Level (FKRGL). Descriptive statistics and ANOVA testing were performed with statistical significance set to Results: On a scale of 1-5, with 5 representing the greatest appropriateness or overall score and a 4-point scale with 4 representing the highest accuracy, the responses exhibited moderately high appropriateness (mean = 4.10, SD = 0.90), high accuracy (mean = 3.55, SD = 0.50), and moderately high overall scores (mean = 4.02, SD = 0.86). Scoring between response categories (self-care recommendations, complication recommendations, lifestyle adjustments, and special device considerations) revealed no significant scoring differences. Suboptimal responses lacked nuance and contained incorrect information and recommendations. Readability indicated college and advanced levels for FRE (Mean = 39.5, SD = 7.17) and FKRGL (Mean = 13.1, SD = 1.47), higher than the sixth-grade level recommended for patient-targeted resources by the NIH. Conclusion: While ChatGPT-generated tracheostomy care responses may exhibit acceptable appropriateness, incomplete or misleading information may have dire clinical consequences. Further, inappropriately high reading levels may limit patient comprehension and accessibility. At this point in its technological infancy, AI-generated information should not be solely relied upon as a direct patient care resource.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.