International Rhinologic Society Rhinology 0300-0729 2026 10 17 Accuracy, completeness and hallucinations in large language model generated statements and references for common rhinologic disorders EN I. Margulis Division of Otolaryngology-Head and Neck Surgery, Department of Surgery, McMaster University, Hamilton, ON, Canada M.A.M. SlimDivision of Otolaryngology-Head and Neck Surgery, Department of Surgery, McMaster University, Hamilton, ON, Canada ENT Department, Forth Valley Royal Hospital, NHS Forth Valley, Scotland, United Kingdom School of Medicine, Dentistry and Nursing, Wolfson Medical School Building, Glasgow, United Kingdom E.C. Sommer Bachelor of Health Sciences Program, Wilfrid Laurier University, Waterloo, ON, Canada Y. AAlammarDivision of Otolaryngology-Head and Neck Surgery, Department of Surgery, McMaster University, Hamilton, ON, Canada Department of Otolaryngology Head and Neck Surgery, King Saud University, Riyadh, Saudi Arabia A.B. Kassam Neurological Surgery, Intent Medical Group, Endeavor Health Advanced Neurosciences Institute, Northwest Community Hospital, Arlington Heights, IL, USA S. Khalife Division of Otolaryngology-Head and Neck Surgery, Department of Surgery, McMaster University, Hamilton, ON, Canada L. Macias-Valle Department of Otolaryngology Head and Neck Surgery, La Salle University, Hospital Español de México, México City, México D.D. Sommer Division of Otolaryngology-Head and Neck Surgery, Department of Surgery, McMaster University, Hamilton, ON, Canada Letter 3641 10.4193/Rhin26.460 Large language models (LLMs) are increasingly adopted for medical documentation, clinical decision-making, and scientific writing. Although these applications demonstrated impressive capabilities, concerns persist regarding their accuracy, fabricated references, and ethical implications. The Massive Multitask Language Understanding (MMLU) test was introduced as a comprehensive benchmark to evaluate how well LLMs perform across a diverse range of tasks. This prospective comparative two part study evaluated the accuracy and completeness of ChatGPT (CGPT)-3.5-generated 100 word summaries of four common rhinologic disorders (epistaxis, allergic rhinitis, odontogenic sinusitis, and chronic rhinosinusitis) including background, investigations, and magement as assessed by international rhinology society members. Furthermore, we evaluated the accuracy of references generated by contemporary LLM versions for the same prompts (CGPT-5 thinking, non-thinking, Claude Sonnet 4.5 non-thinking and Gemini 2.5). Otolaryngologists evaluated each statement using standardized scales for accuracy, completeness, and overall reference quality.