Large Language Model‐Assisted Shared Decision‐Making ( SDM ) in Rhinology: A Randomized Controlled Pilot Study

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, p = 0.004) and −2.7 in the Google arm (95% CI: −4.3 to −1.1, p = 0.002), with no significant difference between groups. Participants uncertain about treatment at baseline had higher decisional conflict both pre‐and post‐intervention, and demonstrated less improvement than those who preferred surgery. Treatment preferences and MCQ performance remained unchanged post‐intervention in both groups. SUS scores were comparable between platforms, and ChatGPT's responses were rated as moderately accurate (7.2/10, SD = 2.0). 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.