From Conversations to Clinical Insight: Charting the Future of Generative AI-Guided Revision Rhinoplasty Consultations.

This letter responds to the important work of Bastaninejad et al., highlighting how advanced large language models (LLMs) such as ChatGPT and Gemini perform in the emotionally and technically demanding landscape of revision rhinoplasty consultations. While the study demonstrates the remarkable communicative, empathetic, and informational strengths of general-purpose LLMs, it also exposes their probabilistic limitations and the absence of domain-specific safeguards required for high-stakes surgical decision making. The findings underscore the urgent need for specialized Surgical-LLMs and Bio-LLMs-ethically aligned, clinically validated, and fine-tuned on high-quality operative, anatomical, and perioperative datasets. Future research should expand question complexity, include multi-turn dialog, and diversify evaluators. With responsible development, multimodal, privacy-preserving surgical LLM ecosystems could meaningfully augment pre-consultation education, risk communication, and patient support.Level of Evidence V This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .