ArticleLaryngoscope investigative otolaryngology2026
Large Language Model-Assisted Shared Decision-Making (SDM) in Rhinology: A Randomized Controlled Pilot Study.
Article in Laryngoscope investigative otolaryngology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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Who cites it
1 citing paper in PubMed.
- Large Language Model-Assisted Shared Decision-Making (SDM) in Rhinology: A Randomized Controlled Pilot Study.Laryngoscope investigative otolaryngology · 2026Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Shared decision-making (SDM) integrates patient preferences into evidence-based care. Generative AI tools may offer a novel approach to facilitate SDM in rhinology, but their clinical utility has not been assessed. Methods: In this prospective randomized pilot study conducted at a single tertiary academic rhinology clinic, English-speaking adult patients with medically refractory chronic rhinosinusitis (CRS) and/or nasal obstruction were randomized to use either ChatGPT-4 or Google to ask treatment-related questions. The primary outcome was change in Decisional Conflict Scale (DCS) score. Secondary outcomes were changes in a treatment-related knowledge questionnaire (MCQ), system usability scale (SUS) scores, and accuracy ratings of ChatGPT responses. Results: A total of 57 participants completed the study (ChatGPT arm = 29; Google arm = 28). Both groups showed significant reductions in DCS scores with a mean difference of -4.3 in the ChatGPT arm (95% CI: -7.1 to -1.5, Conclusion: SDM using ChatGPT and Google may reduce decisional conflict in patients considering sinus surgery, though patients with baseline uncertainty may require additional support. Larger, multi-center trials are needed to validate these tools and explore their integration across diverse settings. Level of Evidence: Level II.
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