The Influence of Patient Persona and Affective Framing on Management Recommendations of ChatGPT, Gemini and Claude for Unruptured Intracranial Aneurysms: A Comparative Benchmarking Study.

Patients increasingly consult large language models (LLMs) before specialist review and may do so in widely differing emotional registers. We examined whether re-casting an unruptured intracranial aneurysm (UIA) case from a third-person vignette into a first-person patient account, with anxiety about treatment or non-treatment, alters the recommendations LLMs return. Sixty-seven UIA cases previously discussed at our multidisciplinary team (MDT) were presented to Claude Opus 4.6, ChatGPT-5.4 and Gemini 3 Pro Thinking under four conditions: third-person vignette (3P), neutral first-person (1PN), and first-person accounts anxious about treatment (1PAT) or non-treatment (1PANT). Each case was submitted five times (4,020 prompts). Majority recommendations were benchmarked against MDT consensus (Cohen's κ, McNemar), tested for directional asymmetry (Bowker), classified as progressive or regressive, and linguistically analysed. Concordance with MDT was fair-to-moderate (71.6-80.0%; κ 0.34-0.51). ChatGPT over-treated under all conditions (p = 0.001-0.013) and Gemini over-treated only at 3P baseline (p = 0.001), abolished under first-person framing. Claude showed no directional preference. First-person framing drove significant clipping-to-coiling migration in Gemini and ChatGPT (p = 0.001 and 0.015). Claude alone hedged under treatment-directed anxiety (p = 0.002). Anxiety-induced shifts were predominantly regressive. Gemini under rupture-anxiety combined the highest empathy-opener rate (85%), highest confidence-marker density and lowest hedging density of any cell, a sycophantic phenotype invisible to categorical analysis. Patient persona and affective framing measurably and model-specifically alter LLM recommendations for UIAs, with anxiety-induced shifts moving models away from rather than toward expert consensus. Clinicians should anticipate AI-shaped expectations varying with a patient's emotional state and counsel against framing-induced advice. This study was retrospectively registered on the Open Science Framework (https://doi.org/10.17605/OSF.IO/HCU6D).