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

Ray PP.

PubMed 41530561 Publisher (DOI)Free full text (PMC)

What this paper says

A letter responding to the Bastaninejad et al. chatbot study, arguing that general-purpose AI performed well but lacks the safeguards needed for surgical decisions and that specialised surgical language models should be built.

Overview

This is a commentary with no patients or data. Ray responds to the study in which ChatGPT and Gemini outscored expert surgeons in simulated revision-rhinoplasty consultations. The letter accepts the communicative strengths shown but stresses the probabilistic nature of the models and the absence of domain-specific validation.

Sections of note

General-purpose LLMs showed "communicative, empathetic, and informational strengths" in the original study.
The letter identifies probabilistic error and missing domain safeguards as key risks in high-stakes surgical settings.
It calls for surgical and biomedical LLMs that are ethically aligned, clinically validated and fine-tuned on operative, anatomical and perioperative data.
Suggested future work: more complex questions, multi-turn dialogue, and more diverse evaluators.
Level of Evidence V.

What it means for a patient

AI chat tools can explain rhinoplasty concepts well but are not validated to advise on an individual's surgery.
The letter's position is that purpose-built, privacy-preserving medical AI is the direction of travel, not consumer chatbots.

Why this paper matters

It is a short expert commentary framing how AI consultation research should proceed. Limits: opinion only, no new evidence.

Terms

Letter to the editor: a short published response to an earlier article.
Multi-turn dialogue: a conversation with several back-and-forth exchanges rather than a single question and answer.
Fine-tuning: further training of an AI model on specialised data.
Multimodal: an AI system that handles images and text together.

Summary written by rhinoplasty.cc from the abstract, 2026-09-08; not medical advice. The authors' own abstract follows.

Abstract

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 .

Abstract as indexed by PubMed; the article is open access (PubMed Central).